Systematic Diet Composition Swap in a Mouse Genome-scale Metabolic Model Reveals Determinants of Obesogenic Diet Metabolism in Liver Cancer

Overview

Journal iScience

Publisher Cell Press

Date 2023 Feb 27

PMID 36844450

Authors

Frederick Clasen

Patricia M Nunes

Gholamreza Bidkhori

Nourdine Bah

Stefan Boeing

Saeed Shoaie

Dimitrios Anastasiou

Affiliations

Soon will be listed here.

Abstract

Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.

Citing Articles

Untargeted Metabolomic Profiling Reveals Differentially Expressed Serum Metabolites and Pathways in Type 2 Diabetes Patients with and without Cognitive Decline: A Cross-Sectional Study.

Al-Akl N, Khalifa O, Ponirakis G, Parray A, Ramadan M, Khan S Int J Mol Sci. 2024; 25(4).

PMID: 38396924 PMC: 10889568. DOI: 10.3390/ijms25042247.

Predicting microbial interactions with approaches based on flux balance analysis: an evaluation.

Joseph C, Zafeiropoulos H, Bernaerts K, Faust K BMC Bioinformatics. 2024; 25(1):36.

PMID: 38262921 PMC: 10804772. DOI: 10.1186/s12859-024-05651-7.

Dietary fat and lipid metabolism in the tumor microenvironment.

Goswami S, Zhang Q, Celik C, Reich E, Yilmaz O Biochim Biophys Acta Rev Cancer. 2023; 1878(6):188984.

PMID: 37722512 PMC: 10937091. DOI: 10.1016/j.bbcan.2023.188984.

References

Zielinski D, Jamshidi N, Corbett A, Bordbar A, Thomas A, Palsson B . Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism. Sci Rep. 2017; 7:41241. PMC: 5264163. DOI: 10.1038/srep41241. View

Brown S . Advances in mouse genetics for the study of human disease. Hum Mol Genet. 2021; 30(R2):R274-R284. PMC: 8490014. DOI: 10.1093/hmg/ddab153. View

Marinos G, Kaleta C, Waschina S . Defining the nutritional input for genome-scale metabolic models: A roadmap. PLoS One. 2020; 15(8):e0236890. PMC: 7428157. DOI: 10.1371/journal.pone.0236890. View

Mardinoglu A, Agren R, Kampf C, Asplund A, Nookaew I, Jacobson P . Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol Syst Biol. 2013; 9:649. PMC: 3619940. DOI: 10.1038/msb.2013.5. View

Tajan M, Vousden K . Dietary Approaches to Cancer Therapy. Cancer Cell. 2020; 37(6):767-785. DOI: 10.1016/j.ccell.2020.04.005. View

Bray G, Nielsen S, Popkin B . Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. Am J Clin Nutr. 2004; 79(4):537-43. DOI: 10.1093/ajcn/79.4.537. View

Wang Y, Kwon H, Su X, Wondisford F . Glycerol not lactate is the major net carbon source for gluconeogenesis in mice during both short and prolonged fasting. Mol Metab. 2020; 31:36-44. PMC: 6881678. DOI: 10.1016/j.molmet.2019.11.005. View

Jang C, Hui S, Lu W, Cowan A, Morscher R, Lee G . The Small Intestine Converts Dietary Fructose into Glucose and Organic Acids. Cell Metab. 2018; 27(2):351-361.e3. PMC: 6032988. DOI: 10.1016/j.cmet.2017.12.016. View

Tomas T, Urlep Z, Moskon M, Mraz M, Rozman D . Computational Model: Sexual Aspects in Hepatic Metabolism and Abnormalities. Front Physiol. 2018; 9:360. PMC: 5907313. DOI: 10.3389/fphys.2018.00360. View

10.

Mills E, Pierce K, Jedrychowski M, Garrity R, Winther S, Vidoni S . Accumulation of succinate controls activation of adipose tissue thermogenesis. Nature. 2018; 560(7716):102-106. PMC: 7045287. DOI: 10.1038/s41586-018-0353-2. View

11.

Broadfield L, Duarte J, Schmieder R, Broekaert D, Veys K, Planque M . Fat Induces Glucose Metabolism in Nontransformed Liver Cells and Promotes Liver Tumorigenesis. Cancer Res. 2021; 81(8):1988-2001. PMC: 7611295. DOI: 10.1158/0008-5472.CAN-20-1954. View

12.

Wang L, Wang S, Li W . RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012; 28(16):2184-5. DOI: 10.1093/bioinformatics/bts356. View

13.

Thiele I, Palsson B . A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010; 5(1):93-121. PMC: 3125167. DOI: 10.1038/nprot.2009.203. View

14.

Nilsson A, Nielsen J . Genome scale metabolic modeling of cancer. Metab Eng. 2016; 43(Pt B):103-112. DOI: 10.1016/j.ymben.2016.10.022. View

15.

DeLuca D, Levin J, Sivachenko A, Fennell T, Nazaire M, Williams C . RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012; 28(11):1530-2. PMC: 3356847. DOI: 10.1093/bioinformatics/bts196. View

16.

Nunes P, Wright A, Veltien A, van Asten J, Tack C, Jones J . Dietary lipids do not contribute to the higher hepatic triglyceride levels of fructose- compared to glucose-fed mice. FASEB J. 2014; 28(5):1988-97. DOI: 10.1096/fj.13-241208. View

17.

Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J . Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol. 2014; 10:721. PMC: 4017677. DOI: 10.1002/msb.145122. View

18.

Gupta A, Das A, Majumder K, Arora N, Mayo H, Singh P . Obesity is Independently Associated With Increased Risk of Hepatocellular Cancer-related Mortality: A Systematic Review and Meta-Analysis. Am J Clin Oncol. 2017; 41(9):874-881. PMC: 5700876. DOI: 10.1097/COC.0000000000000388. View

19.

Duarte N, Becker S, Jamshidi N, Thiele I, Mo M, Vo T . Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A. 2007; 104(6):1777-82. PMC: 1794290. DOI: 10.1073/pnas.0610772104. View

20.

Yizhak K, Chaneton B, Gottlieb E, Ruppin E . Modeling cancer metabolism on a genome scale. Mol Syst Biol. 2015; 11(6):817. PMC: 4501850. DOI: 10.15252/msb.20145307. View