Abstract
Gulf War Illness (GWI) is a poorly understood exposure-induced neuroinflammatory disorder where complexity and the high cost of animal exposure studies has led to fragmented and sparse data sets incompatible with conventional data mining. We propose a numerical approach for generating hypotheses from sparse data to describe dysregulation of phosphoproteomic signaling in GWI brain. In an established animal model, hippocampus, and prefrontal cortex (PFC) samples were collected in mice exposed to corticosterone (CORT) to mimic high physiological stress, sarin surrogate diisopropyl fluorophosphate (DFP), CORT and DFP (CORT + DFP), as well as controls. IonStar liquid chromatography/ mass spectrometry (LC/MS) profiling produced a network of 93 undirected interactions (Pearson correlation Bonferroni < 1%) linking 12 hippocampal and 5 PFC phosphoproteins. With only one pre-treatment resting state and one post-treatment transient observation, conventional rate models were infeasible. Instead, a simple discrete state transition logic was applied to each network node requiring baseline be a steady state from which the network could evolve through the transient 6-h post-treatment state. Solving this as a Constraint Satisfaction (SAT) problem produced 3 competing network models where DFP directly targeted phosphorylated subspecies of sodium channel protein type 1 subunit alpha (Scn1a), protein kinase C gamma (Prkcg), sacsin molecular chaperone (Sacs), in PFC and R3H domain containing 2 (R3hdm2) in hippocampus potentiated by corticosteroids. In simulation-based searches for intervention targets inhibition of Prkcg was disproportionately represented in rescuing the model-predicted persistent illness state, though companion targets were also necessary. Results such as these suggest that a dynamically constrained model-informed design can be highly useful in the initial phases of investigation into complex poorly understood illness where detailed data is largely unavailable.
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Acknowledgments
This work was supported by Rochester Regional Health in conjunction with Elsevier BV (Amsterdam) under a collaborative research sponsorship (Broderick, PI), the US Department of Defense through Congressionally Directed Medical Research Programs (CDMRP) award GW170081 (Boyd, O’Callaghan, Kelly, Broderick, Qu) award, and Intramural funding from the Centers for Disease Control and Prevention - National Institute for Occupational Safety and Health (O’Callaghan, Kelly, Michalovicz). Pathway Studio (© 2020), Elsevier Text Mining, Elsevier Knowledge Graph and EmBio are trademarks of Elsevier Limited. Copyright Elsevier Limited except certain content provided by third parties. We wish to acknowledge the excellent technical support of Brenda K. Billig, Christopher M. Felton, and Ali A. Yilmaz.
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The opinions and assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the US Department of Veterans Affairs, the US Department of Defense, Rochester Regional Health, or Elsevier BV. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention. This work was supported by the Assistant Secretary of Defense for Health Affairs through the Gulf War Illness Research Program. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the Department of Defense.
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Page, J. et al. (2024). Mapping Signaling Mechanisms in Neurotoxic Injury from Sparsely Sampled Data Using a Constraint Satisfaction Framework. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2024. Lecture Notes in Computer Science(), vol 14694. Springer, Cham. https://doi.org/10.1007/978-3-031-61569-6_7
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