Troppo - A Python framework for the reconstruction of context-specific metabolic models

The surge in high-throughput technology availability for molecular biology has enabled the development of powerful predictive tools for use in many applications, including (but not limited to) the diagnosis and treatment of human diseases such as cancer. Genome-scale metabolic models have shown some promise in clearing a path towards precise and personalized medicine, although some challenges still persist. The integration of omics data and subsequent creation of context-specific models for specific cells/tissues still poses a significant hurdle, and most current tools for this purpose have been implemented using proprietary software. Here, we present a new software tool developed in Python, troppo - Tissue-specific RecOnstruction and Phenotype Prediction using Omics data, implementing a large variety of context-specific reconstruction algorithms. Our framework and workflow are modular, which facilitates the development of newer algorithms or omics data sources.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also thank the PhD scholarships funded by national funds through Fundacao para a Ciencia e Tecnologia, with references: SFRH/BD/133248/2017 (J.F.), SFRH/BD/118657/2016 (V.V.).info:eu-repo/semantics/publishedVersio

Plasma Dynamics

Contains table of contents for Section 2 and reports on four research projects.Lawrence Livermore National Laboratory Subcontract 6264005National Science Foundation Grant ECS 84-13173National Science Foundation Grant ECS 85-14517U.S. Air Force - Office of Scientific Research Contract AFOSR 89-0082-AU.S. Army - Harry Diamond Laboratories Contract DAAL02-86-C-0050U.S. Navy - Office of Naval Research Contract N00014-87-K-2001Lawrence Livermore National Laboratory Subcontract B108472National Science Foundation Grant ECS 88-22475U.S. Department of Energy Contract DE-FG02-91-ER-54109National Aeronautics and Space Administration Grant NAGW-2048U.S. Department of Energy Contract DE-AC02-ET-51013U.S. Department of Energy Contract DE-AC02-78-ET-5101

Plasma Dynamics

Contains table of contents for Section 2 and reports on four research projects.National Science Foundation Grant ECS-89-02990U.S. Air Force - Office of Scientific Research Grant AFOSR 89-0082-CU.S. Army - Harry Diamond Laboratories Contract DAAL02-89-K-0084U.S. Army - Harry Diamond Laboratories Contract DAAL02-92-K-0037U.S. Department of Energy Contract DE-AC02-90ER-40591U.S. Navy - Office of Naval Research Grant N00014-90-J-4130Lawrence Livermore National Laboratories Subcontract B-160456National Aeronautics and Space Administration Grant NAGW-2048National Science Foundation Grant ECS-88-22475U.S. Department of Energy Grant DE-FG02-91-ER-5410

Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment

Plasma Dynamics

Contains table of contents for Section 2 and reports on four research projects.Lawrence Livermore National Laboratory Subcontract 6264005National Science Foundation Grant ECS 84-13173National Science Foundation Grant ECS 85-14517U.S. Air Force - Office of Scientific Research Contract AFOSR 84-0026U.S. Army - Harry Diamond Laboratories Contract DAAL02-86-C-0050U.S. Navy - Office of Naval Research Contract N00014-87-K-2001National Science Foundation Grant ECS 85-15032National Science Foundation Grant ECS 88-22475U.S. Department of Energy Contract DE-AC02-ET-5101

Plasma Dynamics

Contains table of contents for Section 2 and reports on four research projects.National Science Foundation Grant ECS 89-02990U.S. Air Force - Office of Scientific Research Grant AFOSR 89-0082-BU.S. Army - Harry Diamond Laboratories Contract DAAL02-89-K-0084U.S. Department of Energy Contract DE-AC02-90ER40591U.S. Navy - Office of Naval Research Grant N00014-90-J-4130Lawrence Livermore National Laboratory Subcontract B-160456National Science Foundation Grant ECS 88-22475U.S. Department of Energy Contract DE-FG02-91-ER-54109National Aeronautics and Space Administration Grant NAGW-2048U.S.-Israel Binational Science Foundation Grant 87-0057U.S Department of Energy Contract DE-AC02-78-ET-5101

Zea mays iRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species

Tankyrase inhibition sensitizes melanoma to PD-1 immune checkpoint blockade in syngeneic mouse models

publishedVersio

Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells

Genome-Scale Metabolic Models (GSMMs), mathematical representations of the cell metabolism in different organisms including humans, are resourceful tools to simulate metabolic phenotypes and understand associated diseases, such as obesity, diabetes and cancer. In the last years, different algorithms have been developed to generate tissue-specific metabolic models that simulate different phenotypes for distinct cell types. Hepatocyte cells are one of the main sites of metabolic conversions, mainly due to their diverse physiological functions. Most of the liver's tissue is formed by hepatocytes, being one of the largest and most important organs regarding its biological functions. Hepatocellular carcinoma is, also, one of the most important human cancers with high mortality rates. In this study, we will analyze four different algorithms (MBA, mCADRE, tINIT and FASTCORE) for tissue-specific model reconstruction, based on a template model and two types of data sources: transcriptomics and proteomics. These methods will be applied to the reconstruction of metabolic models for hepatocyte cells and HepG2 cancer cell line. The models will be analyzed and compared under different perspectives, emphasizing their functional analysis considering a set of metabolic liver tasks. The results show that there is no ``ideal'' algorithm. However, with the current analysis, we were able to retrieve knowledge about the metabolism of the liver.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684), BioTecNorte operation (NORTE01-0145-FEDER-000004) and Search-ON2: Revitalization of HPC infrastructure of UMinho, (NORTE-07-0162-FEDER-000086), all funded by European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio