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Feedback-Based, System-Level Properties of Vertebrate-Microbial Interactions

Author

Listed:
  • Ariel L Rivas
  • Mark D Jankowski
  • Renata Piccinini
  • Gabriel Leitner
  • Daniel Schwarz
  • Kevin L Anderson
  • Jeanne M Fair
  • Almira L Hoogesteijn
  • Wilfried Wolter
  • Marcelo Chaffer
  • Shlomo Blum
  • Tom Were
  • Stephen N Konah
  • Prakash Kempaiah
  • John M Ong’echa
  • Ulrike S Diesterbeck
  • Rachel Pilla
  • Claus-Peter Czerny
  • James B Hittner
  • James M Hyman
  • Douglas J Perkins
Abstract
Background: Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered. Methods: To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions. Results: In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections. Conclusions: More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

Suggested Citation

  • Ariel L Rivas & Mark D Jankowski & Renata Piccinini & Gabriel Leitner & Daniel Schwarz & Kevin L Anderson & Jeanne M Fair & Almira L Hoogesteijn & Wilfried Wolter & Marcelo Chaffer & Shlomo Blum & Tom, 2013. "Feedback-Based, System-Level Properties of Vertebrate-Microbial Interactions," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-16, February.
    • Handle: RePEc:plo:pone00:0053984
    DOI: 10.1371/journal.pone.0053984
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    References listed on IDEAS

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    1. Hiroaki Kitano, 2002. "Computational systems biology," Nature, Nature, vol. 420(6912), pages 206-210, November.
    2. George Laking & Joanne Lord & Alastair Fischer, 2006. "The economics of diagnosis," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1109-1120, October.
    3. Katz, Jonathan N. & King, Gary, 1999. "A Statistical Model for Multiparty Electoral Data," American Political Science Review, Cambridge University Press, vol. 93(1), pages 15-32, March.
    4. Matthew Freeman, 2000. "Feedback control of intercellular signalling in development," Nature, Nature, vol. 408(6810), pages 313-319, November.
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    Cited by:

    1. Gabriel Leitner & Shlomo E Blum & Ariel L Rivas, 2015. "Visualizing the Indefinable: Three-Dimensional Complexity of ‘Infectious Diseases’," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-21, April.
    2. Michelle J Iandiorio & Jeanne M Fair & Stylianos Chatzipanagiotou & Anastasios Ioannidis & Eleftheria Trikka-Graphakos & Nikoletta Charalampaki & Christina Sereti & George P Tegos & Almira L Hoogestei, 2016. "Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-19, July.

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