IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0094692.html
   My bibliography  Save this article

Automated Protein Turnover Calculations from 15N Partial Metabolic Labeling LC/MS Shotgun Proteomics Data

Author

Listed:
  • David Lyon
  • Maria Angeles Castillejo
  • Christiana Staudinger
  • Wolfram Weckwerth
  • Stefanie Wienkoop
  • Volker Egelhofer

Abstract

Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS 15N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (15N) to the intensity sum of all light (14N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.

Suggested Citation

  • David Lyon & Maria Angeles Castillejo & Christiana Staudinger & Wolfram Weckwerth & Stefanie Wienkoop & Volker Egelhofer, 2014. "Automated Protein Turnover Calculations from 15N Partial Metabolic Labeling LC/MS Shotgun Proteomics Data," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-10, April.
    • Handle: RePEc:plo:pone00:0094692
    DOI: 10.1371/journal.pone.0094692
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0094692
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0094692&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0094692?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. Matthias Selbach & Björn Schwanhäusser & Nadine Thierfelder & Zhuo Fang & Raya Khanin & Nikolaus Rajewsky, 2008. "Widespread changes in protein synthesis induced by microRNAs," Nature, Nature, vol. 455(7209), pages 58-63, September.
    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. Alicia Hurtado & Irene Mota-Gómez & Miguel Lao & Francisca M. Real & Johanna Jedamzick & Miguel Burgos & Darío G. Lupiáñez & Rafael Jiménez & Francisco J. Barrionuevo, 2024. "Complete male-to-female sex reversal in XY mice lacking the miR-17~92 cluster," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. W.L. Bai & Y.L. Dang & R.H. Yin & R.L. Yin & W.Q. Jiang & Z.Y. Wang & Y.B. Zhu & J.J. Wang & Z.H. Zhao & G.B. Luo, 2016. "Combination of let-7d-5p, miR-26a-5p, and miR-15a-5p is suitable normalizer for studying microRNA expression in skin tissue of Liaoning cashmere goat during hair follicle cycle," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 61(3), pages 99-107.
    3. Shuangmei Tian & Ziyu Zhao & Beibei Ren & Degeng Wang, 2024. "Non-Linear Relationship between MiRNA Regulatory Activity and Binding Site Counts on Target mRNAs," Data, MDPI, vol. 9(10), pages 1-13, September.
    4. Adam A Margolin & Shao-En Ong & Monica Schenone & Robert Gould & Stuart L Schreiber & Steven A Carr & Todd R Golub, 2009. "Empirical Bayes Analysis of Quantitative Proteomics Experiments," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-15, October.
    5. Chikako Ragan & Michael Zuker & Mark A Ragan, 2011. "Quantitative Prediction of miRNA-mRNA Interaction Based on Equilibrium Concentrations," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-11, February.
    6. Peng Wang & Shangwei Ning & Qianghu Wang & Ronghong Li & Jingrun Ye & Zuxianglan Zhao & Yan Li & Teng Huang & Xia Li, 2013. "mirTarPri: Improved Prioritization of MicroRNA Targets through Incorporation of Functional Genomics Data," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    7. Panagiotis Alexiou & Manolis Maragkakis & Giorgio L Papadopoulos & Victor A Simmosis & Lin Zhang & Artemis G Hatzigeorgiou, 2010. "The DIANA-mirExTra Web Server: From Gene Expression Data to MicroRNA Function," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-7, February.
    8. Ramkrishna Mitra & Sanghamitra Bandyopadhyay, 2011. "MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-13, September.
    9. Wenliang Zhu & Xuan Kan, 2014. "Neural Network Cascade Optimizes MicroRNA Biomarker Selection for Nasopharyngeal Cancer Prognosis," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-7, October.
    10. Yuheng Lu & Christina S Leslie, 2016. "Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-18, July.
    11. Parawee Lekprasert & Michael Mayhew & Uwe Ohler, 2011. "Assessing the Utility of Thermodynamic Features for microRNA Target Prediction under Relaxed Seed and No Conservation Requirements," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-13, June.
    12. Jana Zecha & Wassim Gabriel & Ria Spallek & Yun-Chien Chang & Julia Mergner & Mathias Wilhelm & Florian Bassermann & Bernhard Kuster, 2022. "Linking post-translational modifications and protein turnover by site-resolved protein turnover profiling," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    13. Urmo Võsa & Tõnu Esko & Silva Kasela & Tarmo Annilo, 2015. "Altered Gene Expression Associated with microRNA Binding Site Polymorphisms," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-24, October.
    14. Zhdanov, Vladimir P., 2010. "ncRNA-mediated bistability in the synthesis of hundreds of distinct mRNAs and proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 887-890.
    15. Ray M Marín & Jiří Vaníček, 2012. "Optimal Use of Conservation and Accessibility Filters in MicroRNA Target Prediction," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-11, February.
    16. Youjia Hua & Shiwei Duan & Andrea E Murmann & Niels Larsen & Jørgen Kjems & Anders H Lund & Marcus E Peter, 2011. "miRConnect: Identifying Effector Genes of miRNAs and miRNA Families in Cancer Cells," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-16, October.
    17. Yasemin Oztemur & Tufan Bekmez & Alp Aydos & Isik G Yulug & Betul Bozkurt & Bala Gur Dedeoglu, 2015. "A Ranking-Based Meta-Analysis Reveals Let-7 Family as a Meta-Signature for Grade Classification in Breast Cancer," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
    18. Evelyn Zacharewicz & Paul Della Gatta & John Reynolds & Andrew Garnham & Tamsyn Crowley & Aaron P Russell & Séverine Lamon, 2014. "Identification of MicroRNAs Linked to Regulators of Muscle Protein Synthesis and Regeneration in Young and Old Skeletal Muscle," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-25, December.

    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:pone00:0094692. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.