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Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research

Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of ch...

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Autores principales: Huie, J. Russell, Nielson, Jessica L., Wolfsbane, Jorden, Andersen, Clark R., Spratt, Heidi M., DeWitt, Douglas S., Ferguson, Adam R., Hawkins, Bridget E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871446/
https://www.ncbi.nlm.nih.gov/pubmed/36704298
http://dx.doi.org/10.3389/fbioe.2022.887898
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author Huie, J. Russell
Nielson, Jessica L.
Wolfsbane, Jorden
Andersen, Clark R.
Spratt, Heidi M.
DeWitt, Douglas S.
Ferguson, Adam R.
Hawkins, Bridget E.
author_facet Huie, J. Russell
Nielson, Jessica L.
Wolfsbane, Jorden
Andersen, Clark R.
Spratt, Heidi M.
DeWitt, Douglas S.
Ferguson, Adam R.
Hawkins, Bridget E.
author_sort Huie, J. Russell
collection PubMed
description Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of chronic TBI (cTBI). Data collected by the Moody Project for Translational TBI Research included rats with no injury (naïve animals with similar amounts of anesthetic exposure to TBI and sham-injured animals), sham injury, or lateral fluid percussion TBI, followed by recovery periods up to 12 months. Behavioral measures included locomotor coordination (beam balance neuroscore) and memory and cognition assessments (Morris water maze: MWM) at multiple timepoints. Gene arrays were performed using hippocampal and cortical samples to probe 45,610 genes. To reduce the high dimensionality of molecular and behavioral domains and uncover gene–behavior associations, we performed non-linear principal components analyses (NL-PCA), which de-noised the data. Genomic NL-PCA unveiled three interpretable eigengene components (PC2, PC3, and PC4). Ingenuity pathway analysis (IPA) identified the PCs as an integrated stress response (PC2; EIF2-mTOR, corticotropin signaling, etc.), inflammatory factor translation (PC3; PI3K-p70S6K signaling), and neurite growth inhibition (PC4; Rho pathways). Behavioral PCA revealed three principal components reflecting the contribution of MWM overall speed and distance, neuroscore/beam walk, and MWM platform measures. Integrating the genomic and behavioral domains, we then performed a ‘meta-PCA’ on individual PC scores for each rat from genomic and behavioral PCAs. This meta-PCA uncovered three unique multimodal PCs, characterized by robust associations between inflammatory/stress response and neuroscore/beam walk performance (meta-PC1), stress response and MWM performance (meta-PC2), and stress response and neuroscore/beam walk performance (meta-PC3). Multivariate analysis of variance (MANOVA) on genomic–behavioral meta-PC scores tested separately on cortex and hippocampal samples revealed the main effects of TBI and recovery time. These findings are a proof of concept for the integration of disparate data domains for translational knowledge discovery, harnessing the full syndromic space of TBI.
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spelling pubmed-98714462023-01-25 Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research Huie, J. Russell Nielson, Jessica L. Wolfsbane, Jorden Andersen, Clark R. Spratt, Heidi M. DeWitt, Douglas S. Ferguson, Adam R. Hawkins, Bridget E. Front Bioeng Biotechnol Bioengineering and Biotechnology Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of chronic TBI (cTBI). Data collected by the Moody Project for Translational TBI Research included rats with no injury (naïve animals with similar amounts of anesthetic exposure to TBI and sham-injured animals), sham injury, or lateral fluid percussion TBI, followed by recovery periods up to 12 months. Behavioral measures included locomotor coordination (beam balance neuroscore) and memory and cognition assessments (Morris water maze: MWM) at multiple timepoints. Gene arrays were performed using hippocampal and cortical samples to probe 45,610 genes. To reduce the high dimensionality of molecular and behavioral domains and uncover gene–behavior associations, we performed non-linear principal components analyses (NL-PCA), which de-noised the data. Genomic NL-PCA unveiled three interpretable eigengene components (PC2, PC3, and PC4). Ingenuity pathway analysis (IPA) identified the PCs as an integrated stress response (PC2; EIF2-mTOR, corticotropin signaling, etc.), inflammatory factor translation (PC3; PI3K-p70S6K signaling), and neurite growth inhibition (PC4; Rho pathways). Behavioral PCA revealed three principal components reflecting the contribution of MWM overall speed and distance, neuroscore/beam walk, and MWM platform measures. Integrating the genomic and behavioral domains, we then performed a ‘meta-PCA’ on individual PC scores for each rat from genomic and behavioral PCAs. This meta-PCA uncovered three unique multimodal PCs, characterized by robust associations between inflammatory/stress response and neuroscore/beam walk performance (meta-PC1), stress response and MWM performance (meta-PC2), and stress response and neuroscore/beam walk performance (meta-PC3). Multivariate analysis of variance (MANOVA) on genomic–behavioral meta-PC scores tested separately on cortex and hippocampal samples revealed the main effects of TBI and recovery time. These findings are a proof of concept for the integration of disparate data domains for translational knowledge discovery, harnessing the full syndromic space of TBI. Frontiers Media S.A. 2023-01-10 /pmc/articles/PMC9871446/ /pubmed/36704298 http://dx.doi.org/10.3389/fbioe.2022.887898 Text en Copyright © 2023 Huie, Nielson, Wolfsbane, Andersen, Spratt, DeWitt, Ferguson and Hawkins. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Huie, J. Russell
Nielson, Jessica L.
Wolfsbane, Jorden
Andersen, Clark R.
Spratt, Heidi M.
DeWitt, Douglas S.
Ferguson, Adam R.
Hawkins, Bridget E.
Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research
title Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research
title_full Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research
title_fullStr Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research
title_full_unstemmed Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research
title_short Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research
title_sort data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871446/
https://www.ncbi.nlm.nih.gov/pubmed/36704298
http://dx.doi.org/10.3389/fbioe.2022.887898
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