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      AHR is a master regulator of diverse pathways in endogenous metabolism

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          Abstract

          The aryl hydrocarbon receptor (AHR) is a transcription factor with roles in detoxification, development, immune response, chronic kidney disease and other syndromes. It regulates the expression of drug transporters and drug metabolizing enzymes in a proposed Remote Sensing and Signaling Network involved in inter-organ communication via metabolites and signaling molecules. Here, we use integrated omics approaches to analyze its contributions to metabolism across multiple scales from the organ to the organelle. Global metabolomics analysis of Ahr −/− mice revealed the role of AHR in the regulation of 290 metabolites involved in many biochemical pathways affecting fatty acids, bile acids, gut microbiome products, antioxidants, choline derivatives, and uremic toxins. Chemoinformatics analysis suggest that AHR plays a role in determining the hydrophobicity of metabolites and perhaps their transporter-mediated movement into and out of tissues. Of known AHR ligands, indolepropionate was the only significantly altered molecule, and it activated AHR in both human and murine cells. To gain a deeper biological understanding of AHR, we employed genome scale metabolic reconstruction to integrate knockout transcriptomics and metabolomics data, which indicated a role for AHR in regulation of organic acids and redox state. Together, the results indicate a central role of AHR in metabolism and signaling between multiple organs and across multiple scales.

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          Complex heatmaps reveal patterns and correlations in multidimensional genomic data.

          Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.
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            Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

            Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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              COBRApy: COnstraints-Based Reconstruction and Analysis for Python

              Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Results Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. Conclusion COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. Availability http://opencobra.sourceforge.net/
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                Author and article information

                Contributors
                snigam@health.ucsd.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 October 2022
                5 October 2022
                2022
                : 12
                : 16625
                Affiliations
                [1 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Bioengineering, , University of California San Diego, ; La Jolla, CA 92093 USA
                [2 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Departments of Biology, , University of California San Diego, ; La Jolla, CA 92093 USA
                [3 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Veterinary and Biomedical Sciences, Center for Molecular Toxicology and Carcinogenesis, , The Pennsylvania State University, ; University Park, PA 16802 USA
                [4 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Biochemistry and Molecular Biology, , The Pennsylvania State University, ; State College, PA 16801 USA
                [5 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Radiological Sciences, , University of California Los Angeles, ; Los Angeles, CA 90095 USA
                [6 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Pediatrics, , University of California San Diego, ; La Jolla, CA 92093 USA
                [7 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Medicine (Nephrology), , University of California San Diego, ; La Jolla, CA 92093 USA
                Article
                20572
                10.1038/s41598-022-20572-2
                9534852
                36198709
                e51ab770-23a3-4ec5-9f38-48759f5f18a5
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 May 2022
                : 15 September 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R01 GM132938
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: R35 ES028244
                Award ID: R01 ES028288
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                biochemical networks,transcription,metabolomics,biochemical reaction networks
                Uncategorized
                biochemical networks, transcription, metabolomics, biochemical reaction networks

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