Dataset Information

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.

ABSTRACT:

Background

Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation.

Methods

We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer.

Results

We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing.

Conclusions

Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

SUBMITTER: Berndt N

PROVIDER: S-EPMC7052204 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.

Berndt Nikolaus N Egners Antje A Mastrobuoni Guido G Vvedenskaya Olga O Fragoulis Athanassios A Dugourd Aurélien A Bulik Sascha S Pietzke Matthias M Bielow Chris C van Gassel Rob R Damink Steven W Olde SWO Erdem Merve M Saez-Rodriguez Julio J Holzhütter Hermann-Georg HG Kempa Stefan S Cramer Thorsten T

British journal of cancer 20191210 2

<h4>Background</h4>Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation.<h4>Methods</h4>We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer.<h4>Results</h4>We show that relative differences of protein abu ...[more]

PMID: 31819186

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Project description:BackgroundHistidine metabolism is crucial in role in tumor biology, contributing to tumor progression, immune regulation, and metabolic reprogramming. In hepatocellular carcinoma (HCC), dysregulated histidine metabolism may promote tumor growth and immune evasion, although the specific mechanisms remain poorly understood.MethodsUsing single-cell RNA sequencing, the expression patterns of histidine metabolism-related genes were evaluated across different cell types in HCC samples. In vivo and in vitro experiments were conducted to validate how histidine treatment affects macrophage and T-cell function. Furthermore, the TCGA database was utilized to construct a prognostic model to identify the key gene BUD23 and to examine its correlation with metabolism and immune infiltration.ResultsThe proportion of parenchymal cells exhibiting high histidine metabolism was significantly increased, accompanied by a general reduction in immune and stromal cell infiltration. Notably, macrophages and T cells demonstrated impaired antitumor functions. In the high histidine metabolism group, multiple critical cell communication pathways (e.g., MIF, CLEC, MHC II) were downregulated, macrophages shifted toward immunosuppressive subpopulations, T cells exhibited an exhaustion phenotype, and CD8 + T-cell activation was diminished. Further in vivo and in vitro co-culture experiments confirmed that elevated histidine concentrations promoted M2 polarization in macrophages and weakened T-cell cytotoxicity, accelerating tumor proliferation. According to TCGA analyses, BUD23 was upregulated in the high histidine metabolism group and significantly negatively correlated with patient survival and immune cell infiltration. Silencing BUD23 boosted immune cell activation and cytotoxic effects, effectively reversing the immunosuppressive microenvironment. A multivariable Cox regression-based prognostic model indicated unfavorable outcomes in patients with high histidine metabolism.ConclusionHistidine metabolism drives tumor cell metabolic reprogramming and reshapes the tumor immune microenvironment through intercellular communication, thereby promoting tumor progression. BUD23 shows promise as a biomarker for prognosis and immune response prediction in liver cancer. This study provides new therapeutic targets and theoretical support for liver cancer treatment by targeting histidine metabolism.

Project description:Pharmacokinetic modelling with arterial sampling is the gold standard for analysing dynamic PET data of the brain. However, the invasive character of arterial sampling prevents its widespread clinical application. Several methods have been developed to avoid arterial sampling, in particular reference region methods. Unfortunately, for some tracers or diseases, no suitable reference region can be defined. For these cases, other potentially non-invasive approaches have been proposed: (1) a population based input function (PBIF), (2) an image derived input function (IDIF), or (3) simultaneous estimation of the input function (SIME). This systematic review aims to assess the correspondence of these non-invasive methods with the gold standard. Studies comparing non-invasive pharmacokinetic modelling methods with the current gold standard methods using an input function derived from arterial blood samples were retrieved from PubMed/MEDLINE (until December 2021). Correlation measurements were extracted from the studies. The search yielded 30 studies that correlated outcome parameters (VT, DVR, or BPND for reversible tracers; Ki or CMRglu for irreversible tracers) from a potentially non-invasive method with those obtained from modelling using an arterial input function. Some studies provided similar results for PBIF, IDIF, and SIME-based methods as for modelling with an arterial input function (R2 = 0.59-1.00, R2 = 0.71-1.00, R2 = 0.56-0.96, respectively), if the non-invasive input curve was calibrated with arterial blood samples. Even when the non-invasive input curve was calibrated with venous blood samples or when no calibration was applied, moderate to good correlations were reported, especially for the IDIF and SIME (R2 = 0.71-1.00 and R2 = 0.36-0.96, respectively). Overall, this systematic review illustrates that non-invasive methods to generate an input function are still in their infancy. Yet, IDIF and SIME performed well, not only with arterial blood calibration, but also with venous or no blood calibration, especially for some tracers without plasma metabolites, which would potentially make these methods better suited for clinical application. However, these methods should still be properly validated for each individual tracer and application before implementation.

Dataset Information

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.

Background

Methods

Results

Conclusions

Publications

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.

Similar Datasets

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