IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0012116.html
   My bibliography  Save this article

Collective Dynamics of Specific Gene Ensembles Crucial for Neutrophil Differentiation: The Existence of Genome Vehicles Revealed

Author

Listed:
  • Masa Tsuchiya
  • Vincent Piras
  • Alessandro Giuliani
  • Masaru Tomita
  • Kumar Selvarajoo

Abstract

Cell fate decision remarkably generates specific cell differentiation path among the multiple possibilities that can arise through the complex interplay of high-dimensional genome activities. The coordinated action of thousands of genes to switch cell fate decision has indicated the existence of stable attractors guiding the process. However, origins of the intracellular mechanisms that create “cellular attractor” still remain unknown. Here, we examined the collective behavior of genome-wide expressions for neutrophil differentiation through two different stimuli, dimethyl sulfoxide (DMSO) and all-trans-retinoic acid (atRA). To overcome the difficulties of dealing with single gene expression noises, we grouped genes into ensembles and analyzed their expression dynamics in correlation space defined by Pearson correlation and mutual information. The standard deviation of correlation distributions of gene ensembles reduces when the ensemble size is increased following the inverse square root law, for both ensembles chosen randomly from whole genome and ranked according to expression variances across time. Choosing the ensemble size of 200 genes, we show the two probability distributions of correlations of randomly selected genes for atRA and DMSO responses overlapped after 48 hours, defining the neutrophil attractor. Next, tracking the ranked ensembles' trajectories, we noticed that only certain, not all, fall into the attractor in a fractal-like manner. The removal of these genome elements from the whole genomes, for both atRA and DMSO responses, destroys the attractor providing evidence for the existence of specific genome elements (named “genome vehicle”) responsible for the neutrophil attractor. Notably, within the genome vehicles, genes with low or moderate expression changes, which are often considered noisy and insignificant, are essential components for the creation of the neutrophil attractor. Further investigations along with our findings might provide a comprehensive mechanistic view of cell fate decision.

Suggested Citation

  • Masa Tsuchiya & Vincent Piras & Alessandro Giuliani & Masaru Tomita & Kumar Selvarajoo, 2010. "Collective Dynamics of Specific Gene Ensembles Crucial for Neutrophil Differentiation: The Existence of Genome Vehicles Revealed," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-10, August.
    • Handle: RePEc:plo:pone00:0012116
    DOI: 10.1371/journal.pone.0012116
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012116
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0012116&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0012116?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. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Tal Danino & Octavio Mondragón-Palomino & Lev Tsimring & Jeff Hasty, 2010. "A synchronized quorum of genetic clocks," Nature, Nature, vol. 463(7279), pages 326-330, January.
    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. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    2. Xiao‐Bing Hu & Hang Li & XiaoMei Guo & Pieter H. A. J. M. van Gelder & Peijun Shi, 2019. "Spatial Vulnerability of Network Systems under Spatially Local Hazards," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 162-179, January.
    3. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    4. Pirvu Daniela & Barbuceanu Mircea, 2016. "Recent Contributions Of The Statistical Physics In The Research Of Banking, Stock Exchange And Foreign Exchange Markets," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 2, pages 85-92, April.
    5. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    6. Yu, Shuo & Alqahtani, Fayez & Tolba, Amr & Lee, Ivan & Jia, Tao & Xia, Feng, 2022. "Collaborative Team Recognition: A Core Plus Extension Structure," Journal of Informetrics, Elsevier, vol. 16(4).
    7. Shang, Jiaxing & Liu, Lianchen & Li, Xin & Xie, Feng & Wu, Cheng, 2016. "Targeted revision: A learning-based approach for incremental community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 70-85.
    8. Roth, Camille, 2007. "Empiricism for descriptive social network models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 53-58.
    9. Ying Song & Zhiwen Zheng & Yunmei Shi & Bo Wang, 2023. "GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks," Mathematics, MDPI, vol. 11(15), pages 1-16, July.
    10. Shiau, Wen-Lung & Dwivedi, Yogesh K. & Yang, Han Suan, 2017. "Co-citation and cluster analyses of extant literature on social networks," International Journal of Information Management, Elsevier, vol. 37(5), pages 390-399.
    11. Zhang, Zhiwei & Wang, Zhenyu, 2015. "Mining overlapping and hierarchical communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 25-33.
    12. J. Sylvan Katz & Guillermo Armando Ronda-Pupo, 2019. "Cooperation, scale-invariance and complex innovation systems: a generalization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1045-1065, November.
    13. Wu, Zhihao & Lin, Youfang & Wan, Huaiyu & Tian, Shengfeng & Hu, Keyun, 2012. "Efficient overlapping community detection in huge real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2475-2490.
    14. Atieh Mirshahvalad & Johan Lindholm & Mattias Derlén & Martin Rosvall, 2012. "Significant Communities in Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-7, March.
    15. Nie, Yanyi & Li, Wenyao & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Markovian approach to tackle competing pathogens in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    16. Cerqueti, Roy & Ciciretti, Rocco & Dalò, Ambrogio & Nicolosi, Marco, 2022. "A new measure of the resilience for networks of funds with applications to socially responsible investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    17. Rizman Žalik, Krista & Žalik, Borut, 2014. "A local multiresolution algorithm for detecting communities of unbalanced structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 380-393.
    18. Shi, Xiaolin & Adamic, Lada A. & Strauss, Martin J., 2007. "Networks of strong ties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 33-47.
    19. Zhang, Shihua & Wang, Rui-Sheng & Zhang, Xiang-Sun, 2007. "Identification of overlapping community structure in complex networks using fuzzy c-means clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 483-490.
    20. Chen, Lei & Kou, Yingxin & Li, Zhanwu & Xu, An & Wu, Cheng, 2018. "Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 754-773.

    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:pone00:0012116. 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.