title: Feedback-based, system-level properties of vertebrate-microbial interactions.
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.
authors: Center for Global Health, University of New Mexico, Albuquerque, New Mexico, USA. alrivas@unm.edu
authors: Ariel L
authors: AL
authors: Rivas
authors: Mark D
authors: MD
authors: Jankowski
authors: Renata
authors: R
authors: Piccinini
authors: Gabriel
authors: G
authors: Leitner
authors: Daniel
authors: D
authors: Schwarz
authors: Kevin L
authors: KL
authors: Anderson
authors: Jeanne M
authors: JM
authors: Fair
authors: Almira L
authors: AL
authors: Hoogesteijn
authors: Wilfried
authors: W
authors: Wolter
authors: Marcelo
authors: M
authors: Chaffer
authors: Shlomo
authors: S
authors: Blum
authors: Tom
authors: T
authors: Were
authors: Stephen N
authors: SN
authors: Konah
authors: Prakash
authors: P
authors: Kempaiah
authors: John M
authors: JM
authors: Ong'echa
authors: Ulrike S
authors: US
authors: Diesterbeck
authors: Rachel
authors: R
authors: Pilla
authors: Claus-Peter
authors: CP
authors: Czerny
authors: James B
authors: JB
authors: Hittner
authors: James M
authors: JM
authors: Hyman
authors: Douglas J
authors: DJ
authors: Perkins
mesh_terms: D000818:Animals; D001717:Birds / Q000821:virology; D002417:Cattle; D005188:False Negative Reactions; D025461:Feedback, Physiological; D054884:Host-Pathogen Interactions / Q000502:physiology*; D006801:Humans; D008288:Malaria / Q000175:diagnosis / Q000469:parasitology; D055624:Methicillin-Resistant Staphylococcus aureus / Q000502:physiology; D011379:Prognosis; D015203:Reproducibility of Results; D013045:Species Specificity; D049490:Systems Biology; D014714:Vertebrates / Q000382:microbiology* / Q000469:parasitology / Q000821:virology*; D014780:Viruses / Q000378:metabolism