Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function

Liu, Zhengtao; Zhang, Cheng Cheng; Lee, Sunjae; Kim, Woonghee; Klevstig, Martina; Harzandi, Azadeh; Sikanic, Natasha; Arif, Muhammad; Ståhlman, Marcus; Nielsen, Jens; Uhlén, Mathias; Borén, Jan; Mardinoğlu, Adil

doi:10.1016/j.ymben.2019.01.001

Cited by 42 publications

(38 citation statements)

References 54 publications

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“…The integrated signaling network analysis demonstrated that signaling hubs are between lipid‐sensing regulators (PPARs) and lipid metabolism enzymes. [ 102 ] Moreover, the sterol regulatory element‐binding protein 1 and fork‐head box protein A2 were found as the top‐scoring anabolic and catabolic hub genes, respectively. Other genes involved in lipid metabolism were identified p63, a homolog of the metabolic regulator p53 and the glucose transporter type 4.…”

Section: Metabolic Network‐based Identification For Potential Drug Tamentioning

confidence: 99%

Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach

et al. 2020

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Hepatocellular carcinoma (HCC) is one of the leading causes of cancer‐related death primarily due to the lack of effective targeted therapies. Despite the distinct morphological and phenotypic patterns of HCC, treatment strategies are restricted to relatively homogeneous therapies, including multitargeted tyrosine kinase inhibitors and immune checkpoint inhibitors. Therefore, more effective therapy options are needed to target dysregulated metabolic and molecular pathways in HCC. Integrative genomic profiling of HCC patients provides insight into the most frequently mutated genes and molecular targets, including telomerase reverse transcriptase, the TP53 gene, and the Wnt/β‐catenin signaling pathway oncogene (CTNNB1). Moreover, emerging techniques, such as genome‐scale metabolic models may elucidate the underlying cancer‐specific metabolism, which allows for the discovery of potential drug targets and identification of biomarkers. De novo lipogenesis has been revealed as consistently upregulated since it is required for cell proliferation in all HCC patients. The metabolic network‐driven stratification of HCC patients in terms of redox responses, utilization of metabolites, and subtype‐specific pathways may have clinical implications to drive the development of personalized medicine. In this review, the current and emerging therapeutic targets in light of molecular approaches and metabolic network‐based strategies are summarized, prompting effective treatment of HCC patients.

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Section: Metabolic Network‐based Identification For Potential Drug Tamentioning

confidence: 99%

Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach

et al. 2020

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“…The differentially expressed (DE) genes between the intestine of germfree and conventionally raised mice were obtained from the same study [4]. The GEM for human cancer cell line HepG2 is generated in another recent study [21] based on transcriptomic data and exchange constraints calculated from the formulation of the cultivating medium. The irreversible form of the model which has 5403 reactions, 2784 metabolites and 1957 genes was used by RMetD2 in this study.…”

Section: Gem and Simulationmentioning

confidence: 99%

“…We also applied RMetD2 to investigate the metabolic difference between wild-type and PKLR inhibited liver cancer cell line, HepG2, using constraints and DE genes from our recent study [21]. The reference GEM of HepG2 was reconstructed in the same study based on transcriptomic data as well as composition of culture medium using tINIT [26].…”

Section: Case 2: Liver Cancer Cell Line Before and After Gene Inhibitionmentioning

confidence: 99%

“…show case is provided. RMetD2 was used to reveal the metabolic differences between germfree and conventional raised mice based on the dietary inputs and DE genes obtained from the previous study [21]. Since most of the frequently used methods could not be used for integrating additional constraints together with transcriptomic changes, we only compared the RMetD2 result with experimental observations.…”

Section: Case 3: Intestine Of Conventional Raised Mice Vs Germ-free mentioning

confidence: 99%

“…RMetD2 integrates relative gene expressions together with the stoichiometric structure of GEMs to identify key up-/down-regulated metabolic reactions that are most relevant to the changes in the transcriptome. The concept of RMetD has been successfully used in number of recent studies [4,20,21]. Unlike previous methods, RMetD2 stretches the potential biological effects into many steps by setting gradient constraints to the differentially expressed reactions.…”

Section: Introductionmentioning

confidence: 99%

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RMetD2: a tool for integration of relative transcriptomics data into Genome-scale metabolic models

Zhang

Bidkhori

et al. 2019

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Relative Metabolic Differences version 2 (RMetD2) is a tool for integration of differentially expressed (DE) genes into genome-scale metabolic models (GEMs) for revealing the altered metabolism between two biological conditions. This method provides a robust evaluation of the metabolism by using flux ranges instead of a single set of flux distributions. RMetD2 classifies reactions into three different groups, namely up-regulated, down-regulated and unchanged, which enables systematic interpretation of the metabolic differences between two different conditions. We employed this method in three different case studies using mice and human datasets, and compared it with state-of-the-art methods used for studying condition-specific metabolic differences using GEMs. We observed that RMetD2 is capable of capturing experimentally-observed features that are missed by other methods, highlighting its potential use in biotechnology and systems medicine applications. RMetD2 is implemented in Matlab and it is available without any limitation at https://sourceforge.net/projects/rmetd.

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Scaling with body mass and age in glycolytic enzymes of domestic dogs

et al. 2022

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Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function

Cited by 42 publications

References 54 publications

Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach

Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach

RMetD2: a tool for integration of relative transcriptomics data into Genome-scale metabolic models

Scaling with body mass and age in glycolytic enzymes of domestic dogs

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