Abstract
BackgroundImproved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered.MethodsTo 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.ResultsIn 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.ConclusionsMore 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.
Highlights
The rate of undetected infections remains markedly elevated and may be increasing [1,2,3].Pathogens that develop resistance to antimicrobials pose new challenges, such as methicillin- or multidrug-resistant Staphyloccocus aureus (MRSA) infections which, in the USA, cause more deaths than tuberculosis, AIDS, and viral hepatitis combined [4]
evolutionary biology (EB) focuses on biological features well conserved in evolution [6,7,8,9,10,11,12]
Unlike reductionist approaches, which only consider a few and static variables, systems biology (SB) focuses on systems and their dynamics –a feature that may extract more information from the same data [13,14,15,16,17,18]
Summary
The rate of undetected infections remains markedly elevated and may be increasing [1,2,3].Pathogens that develop resistance to antimicrobials pose new challenges, such as methicillin- or multidrug-resistant Staphyloccocus aureus (MRSA) infections which, in the USA, cause more deaths than tuberculosis, AIDS, and viral hepatitis combined [4]. To enhance the detection of infectious diseaserelated data patterns, new approaches are required. Systems biology (SB) and evolutionary biology (EB) may be considered. EB focuses on biological features well conserved in evolution [6,7,8,9,10,11,12]. In infectious diseases, EB has not yet provided usable methods [6]. Unlike reductionist approaches, which only consider a few and static variables, SB focuses on systems and their dynamics –a feature that may extract more information from the same data [13,14,15,16,17,18]. Improved characterization of infectious disease dynamics is required. Three-dimensional (3D) data analysis of feedback-like processes may be considered
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