Dataset Information

Inverse Data-Driven Modeling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation.

ABSTRACT: Mechanistic or mammalian target of rapamycin complex 1 (mTORC1) is an important regulator of effector functions, proliferation, and cellular metabolism in macrophages. The biochemical processes that are controlled by mTORC1 are still being defined. Here, we demonstrate that integrative multiomics in conjunction with a data-driven inverse modeling approach, termed COVRECON, identifies a biochemical node that influences overall metabolic profiles and reactions of mTORC1-dependent macrophage metabolism. Using a combined approach of metabolomics, proteomics, mRNA expression analysis, and enzymatic activity measurements, we demonstrate that Tsc2, a negative regulator of mTORC1 signaling, critically influences the cellular activity of macrophages by regulating the enzyme phosphoglycerate dehydrogenase (Phgdh) in an mTORC1-dependent manner. More generally, while lipopolysaccharide (LPS)-stimulated macrophages repress Phgdh activity, IL-4-stimulated macrophages increase the activity of the enzyme required for the expression of key anti-inflammatory molecules and macrophage proliferation. Thus, we identify Phgdh as a metabolic checkpoint of M2 macrophages.

SUBMITTER: Wilson JL

PROVIDER: S-EPMC7003064 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Inverse Data-Driven Modeling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation.

Wilson Jayne Louise JL Nägele Thomas T Linke Monika M Demel Florian F Fritsch Stephanie D SD Mayr Hannah Katharina HK Cai Zhengnan Z Katholnig Karl K Sun Xiaoliang X Fragner Lena L Miller Anne A Haschemi Arvand A Popa Alexandra A Bergthaler Andreas A Hengstschläger Markus M Weichhart Thomas T Weckwerth Wolfram W

Cell reports 20200201 5

Mechanistic or mammalian target of rapamycin complex 1 (mTORC1) is an important regulator of effector functions, proliferation, and cellular metabolism in macrophages. The biochemical processes that are controlled by mTORC1 are still being defined. Here, we demonstrate that integrative multiomics in conjunction with a data-driven inverse modeling approach, termed COVRECON, identifies a biochemical node that influences overall metabolic profiles and reactions of mTORC1-dependent macrophage metabo ...[more]

PMID: 32023468

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Project description:MotivationOne central goal of systems biology is to infer biochemical regulations from large-scale OMICS data. Many aspects of cellular physiology and organismal phenotypes can be understood as results of metabolic interaction network dynamics. Previously, we have proposed a convenient mathematical method, which addresses this problem using metabolomics data for the inverse calculation of biochemical Jacobian matrices revealing regulatory checkpoints of biochemical regulations. The proposed algorithms for this inference are limited by two issues: they rely on structural network information that needs to be assembled manually, and they are numerically unstable due to ill-conditioned regression problems for large-scale metabolic networks.ResultsTo address these problems, we developed a novel regression loss-based inverse Jacobian algorithm, combining metabolomics COVariance and genome-scale metabolic RECONstruction, which allows for a fully automated, algorithmic implementation of the COVRECON workflow. It consists of two parts: (i) Sim-Network and (ii) inverse differential Jacobian evaluation. Sim-Network automatically generates an organism-specific enzyme and reaction dataset from Bigg and KEGG databases, which is then used to reconstruct the Jacobian's structure for a specific metabolomics dataset. Instead of directly solving a regression problem as in the previous workflow, the new inverse differential Jacobian is based on a substantially more robust approach and rates the biochemical interactions according to their relevance from large-scale metabolomics data. The approach is illustrated by in silico stochastic analysis with differently sized metabolic networks from the BioModels database and applied to a real-world example. The characteristics of the COVRECON implementation are that (i) it automatically reconstructs a data-driven superpathway model; (ii) more general network structures can be investigated, and (iii) the new inverse algorithm improves stability, decreases computation time, and extends to large-scale models.Availability and implementationThe code is available in the website https://bitbucket.org/mosys-univie/covrecon.

Dataset Information

Inverse Data-Driven Modeling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation.

Publications

Inverse Data-Driven Modeling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation.

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