[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1009522.html
   My bibliography  Save this article

Comparison of metabolic states using genome-scale metabolic models

Author

Listed:
  • Chaitra Sarathy
  • Marian Breuer
  • Martina Kutmon
  • Michiel E Adriaens
  • Chris T Evelo
  • Ilja C W Arts
Abstract
Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.Author summary: Cellular metabolism is a highly complex and interconnected system. As many lifestyle diseases in humans have a strong metabolic component, it is important to understand metabolic differences between healthy and diseased states. In systems biology, metabolic behaviours are investigated using genome-scale metabolic models. In addition to the sheer size and complexity of the genome-scale metabolic models of human systems, using existing analysis methods is challenging and the parameter selection is not straightforward. Therefore, novel methodological frameworks are necessary for analysing metabolic conditions despite the challenges posed by human models. Particularly, an ongoing challenge has been that of comparing several phenotypes for identifying condition- or disease-specific metabolic signatures. We address this significant challenge by developing a scalable and model-driven approach, ComMet (Comparison of Metabolic states). ComMet enables an in-depth investigation and comparison of metabolic phenotypes in large models while also identifying the underlying functional differences. Novel hypotheses can be generated using ComMet for not only understanding known metabolic phenotypes better but also for guiding the design of new experiments to validate the processes predicted by ComMet.

Suggested Citation

  • Chaitra Sarathy & Marian Breuer & Martina Kutmon & Michiel E Adriaens & Chris T Evelo & Ilja C W Arts, 2021. "Comparison of metabolic states using genome-scale metabolic models," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-25, November.
  • Handle: RePEc:plo:pcbi00:1009522
    DOI: 10.1371/journal.pcbi.1009522
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009522
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009522&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1009522?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alfredo Braunstein & Anna Paola Muntoni & Andrea Pagnani, 2017. "An analytic approximation of the feasible space of metabolic networks," Nature Communications, Nature, vol. 8(1), pages 1-9, April.
    2. Adil Mardinoglu & Rasmus Agren & Caroline Kampf & Anna Asplund & Mathias Uhlen & Jens Nielsen, 2014. "Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease," Nature Communications, Nature, vol. 5(1), pages 1-11, May.
    3. Gökhan S. Hotamisligil, 2006. "Inflammation and metabolic disorders," Nature, Nature, vol. 444(7121), pages 860-867, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Farman, Muhammad & Ahmad, Aqeel & Zehra, Anum & Nisar, Kottakkaran Sooppy & Hincal, Evren & Akgul, Ali, 2024. "Analysis and controllability of diabetes model for experimental data by using fractional operator," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 133-148.
    2. Hai-Hua Chuang & Rong-Ho Lin & Wen-Cheng Li & Wei-Chung Yeh & Yen-An Lin & Jau-Yuan Chen, 2020. "High-Sensitivity C-Reactive Protein Elevation Is Independently Associated with Subclinical Renal Impairment in the Middle-Aged and Elderly Population—A Community-Based Study in Northern Taiwan," IJERPH, MDPI, vol. 17(16), pages 1-10, August.
    3. Piontak, Joy Rayanne & Russell, Michael A. & Danese, Andrea & Copeland, William E. & Hoyle, Rick H. & Odgers, Candice L., 2017. "Violence exposure and adolescents' same-day obesogenic behaviors: New findings and a replication," Social Science & Medicine, Elsevier, vol. 189(C), pages 145-151.
    4. Jaime Gomez-Ramirez, 2012. "Inverse Thinking in Economic Theory: A Radical Approach to Economic Thinking," Papers 1208.3460, arXiv.org.
    5. Surapon Tangvarasittichai, 2018. "Iron Homeostasis and Diabetes Risk," Current Research in Diabetes & Obesity Journal, Juniper Publishers Inc., vol. 7(4), pages 1-11, July.
    6. André Schultz & Amina A Qutub, 2016. "Reconstruction of Tissue-Specific Metabolic Networks Using CORDA," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-33, March.
    7. Reiko Kuroda & Kazuhiro Nogawa & Yuuka Watanabe & Hideki Morimoto & Kouichi Sakata & Yasushi Suwazono, 2021. "Association between High-Sensitive C-Reactive Protein and the Development of Liver Damage in Japanese Male Workers," IJERPH, MDPI, vol. 18(6), pages 1-11, March.
    8. José Manuel Leiva-Valderrama & Adrián Montes-de-Oca-Garcia & Edgardo Opazo-Diaz & Jesus G. Ponce-Gonzalez & Guadalupe Molina-Torres & Daniel Velázquez-Díaz & Alejandro Galán-Mercant, 2021. "Effects of High-Intensity Interval Training on Inflammatory Biomarkers in Patients with Type 2 Diabetes. A Systematic Review," IJERPH, MDPI, vol. 18(23), pages 1-19, November.
    9. Bin Li, 2019. "Sirt1 Inhibits Adipose Tissue Inflammation by Foxos/ mTOR/S6K1 Signal Pathway in Mice," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 19(5), pages 14646-14655, July.
    10. Hua Jiang & Wen-Hua Yan & Chan-Juan Li & An-Ping Wang & Jing-Tao Dou & Yi-Ming Mu, 2014. "Elevated White Blood Cell Count Is Associated with Higher Risk of Glucose Metabolism Disorders in Middle-Aged and Elderly Chinese People," IJERPH, MDPI, vol. 11(5), pages 1-13, May.
    11. Maria E. Bleil & Cathryn Booth-LaForce & Aprile D. Benner, 2017. "Race Disparities in Pubertal Timing: Implications for Cardiovascular Disease Risk Among African American Women," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(5), pages 717-738, October.
    12. Seok Hui Kang & Kyu Hyang Cho & Jun Young Do, 2019. "Association between periodontitis and cardiometabolic risk: Results from the Korean National Health and Nutrition Examination Survey 2008-2014," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-12, April.
    13. Ema Schönberger & Vjera Mihaljević & Kristina Steiner & Sandra Šarić & Tomislav Kurevija & Ljiljana Trtica Majnarić & Ines Bilić Ćurčić & Silvija Canecki-Varžić, 2023. "Immunomodulatory Effects of SGLT2 Inhibitors—Targeting Inflammation and Oxidative Stress in Aging," IJERPH, MDPI, vol. 20(17), pages 1-19, August.
    14. Goosby, Bridget J. & Cheadle, Jacob E. & McDade, Thomas, 2016. "Birth weight, early life course BMI, and body size change: Chains of risk to adult inflammation?," Social Science & Medicine, Elsevier, vol. 148(C), pages 102-109.
    15. Jiajing Jiang & Kelei Li & Fenglei Wang & Bo Yang & Yuanqing Fu & Jusheng Zheng & Duo Li, 2016. "Effect of Marine-Derived n-3 Polyunsaturated Fatty Acids on Major Eicosanoids: A Systematic Review and Meta-Analysis from 18 Randomized Controlled Trials," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-18, January.
    16. Seung Jin Han & Kyoung Hwa Ha & Ja Young Jeon & Hae Jin Kim & Kwan Woo Lee & Dae Jung Kim, 2015. "Impact of Cadmium Exposure on the Association between Lipopolysaccharide and Metabolic Syndrome," IJERPH, MDPI, vol. 12(9), pages 1-14, September.
    17. Chin Yi Chan & Shaanthana Subramaniam & Norazlina Mohamed & Soelaiman Ima-Nirwana & Norliza Muhammad & Ahmad Fairus & Pei Yuen Ng & Nor Aini Jamil & Noorazah Abd Aziz & Kok-Yong Chin, 2020. "Determinants of Bone Health Status in a Multi-Ethnic Population in Klang Valley, Malaysia," IJERPH, MDPI, vol. 17(2), pages 1-16, January.
    18. Marina Sanchez & Shirin Panahi & Angelo Tremblay, 2014. "Childhood Obesity: A Role for Gut Microbiota?," IJERPH, MDPI, vol. 12(1), pages 1-14, December.
    19. Hazan, Aurélien, 2019. "A maximum entropy network reconstruction of macroeconomic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 1-17.
    20. Xingzhou Tian & Qi Lu, 2022. "Anthocyanins in Dairy Cow Nutrition: A Review," Agriculture, MDPI, vol. 12(11), pages 1-13, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1009522. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.