Mapping Signaling Mechanisms in Neurotoxic Injury from Sparsely Sampled Data Using a Constraint Satisfaction Framework

Details

• Personal Author:
  Boyd J ; Broderick G ; Kelly KA ; Michalovicz LT ; O'Callahghan JP ; Page J ; Qu J ; Shen S ; Zhu X
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Animals Nervous System Nervous System Diseases Occupational Health Safety
• Keywords:
  Animal Studies Author Keywords: Animal Model Brain Function Cognitive Function Military Personnel Network Regulatory Dynamics Neurological Disorders Neurotoxic Effects Neurotoxic Insult Physiological Stress
• ISBN:
  9783031615689
• ISSN:
  0302-9743
• Publisher:
  Springer
• Document Type:
  Text
• Genre:
  Proceedings
• Place as Subject:
  New York ; OSHA Region 2 ; OSHA Region 3 ; Virginia ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  95-110
• Volume:
  14694
• NIOSHTIC Number:
  nn:20069865
• Citation:
  Augmented Cognition: 18th International Conference, AC 2024, held as part of the 26th HCI International Conference, HCII 2024, June 29-July 4, 2024, Washington, D.C. Lecture notes in computer science, volume 14694. Schmorrow DD, Fidopiastis CM, eds. Cham, Switzerland: Springer, 2024 Jun; 14694(Pt 1):95-110
• Email:
  gordonbroderick55@gmail.com
• Editor(s):
  Schmorrow DD; Fidopiastis CM
• Federal Fiscal Year:
  2024
• Peer Reviewed:
  False
• Part Number:
  1
• Source Full Name:
  Augmented Cognition: 18th International Conference, AC 2024, held as part of the 26th HCI International Conference, HCII 2024, June 29-July 4, 2024, Washington, D.C. Lecture notes in computer science, volume 14694
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:bd1c137fc1e92c44a6db4ac47be35be0ad823bbc1741e8b127e69d57c4fcb8303c298307d393fbd33ed2524f90217ffcf8dcfe9bd9f7a8b9fa40471de85c4747
• Download URL:
  https://stacks.cdc.gov/view/cdc/208205/cdc_208205_DS1.pdf
• File Type:
  [PDF - 4.05 MB ]