Engineering topics

Stenzel-Poore, Mary

Publications and source records attributed to Stenzel-Poore, Mary.

4 records

DOE OSTI2015

CpG preconditioning regulates miRNA expression that modulates genomic reprogramming associated with neuroprotection against ischemic injury

CpG preconditioning reprograms the genomic response to stroke to protect the brain against ischemic injury. The mechanisms that underlie genomic reprogramming are incompletely understood. MicroRNAs (miRNA) are novel regulators of gene expression; however, their potential role in modulating gene responses produced by CpG preconditioning is unknown. We evaluated brain miRNA expression in response to CpG preconditioning prior to and following stroke using microarray. Importantly, we have gene expression data from previous gene microarrays under the same conditions and timepoints, which allowed integration of miRNA and gene expression data to specifically identify regulated miRNA gene targets. Statistical analysis revealed that the miRNA targets were enriched in our regulated gene population, implicating that miRNAs likely orchestrate this gene expression. CpG and saline treatment shared expression of many miRNAs and their gene targets correlate with damaging upregulation of inflammation and protective suppression of metabolism and excitotoxicity. Excitingly, miRNAs that were differentially regulated between CpG and saline treated animals associated with the upregulation of several neuroprotective genes, indicating that these miRNAs are involved in genomic reprogramming to increase protection. This data suggests that miRNAs regulate endogenous responses to stroke and that manipulation of these miRNAs may have the potential to acutely activate novel protective responses to stroke.

systems biology, microRNA, bioinformatics, stroke,↗

DOE OSTI2014

Matrix Effects in Biological Mass Spectrometry Imaging: Identification and Compensation

Matrix effects in mass spectrometry imaging (MSI) may affect the observed molecular distribution in chemical and biological systems. In this study, we introduce an experimental approach that efficiently compensates for matrix effects in nanospray desorption electrospray ionization (nano-DESI) MSI without introducing any complexity into the experimental protocol. We demonstrate compensation for matrix effects in nano-DESI MSI of phosphatidylcholine (PC) in normal and ischemic mouse brain tissue by doping the nano-DESI solvent with PC standards. Specifically, we use mouse brain tissue of a middle cerebral artery occlusion (MCAO) stroke model with an ischemic region localized to one hemisphere of the brain. Due to similar suppression in ionization of endogenous PC molecules extracted from the tissue and PC standards added to the solvent, matrix effects are eliminated by normalizing the intensity of the sodium and potassium adducts of endogenous PC to the intensity of the corresponding adduct of the PC standard. This approach efficiently compensates for signal variations resulting from differences in the local concentrations of sodium and potassium in tissue sections and from the complexity of the extracted analyte mixture derived from local variations in molecular composition.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2013

Modeling Emergence in Neuroprotective Regulatory Networks

The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.

agent-based modeling↗

DOE OSTI2012

Modeling Dynamic Regulatory Processes in Stroke.

The ability to examine in silico the behavior of biological systems can greatly accelerate the pace of discovery in disease pathologies, such as stroke, where in vivo experimentation is lengthy and costly. In this paper we describe an approach to in silico examination of blood genomic responses to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) relating regulators and functional clusters from the data. These ODEs were used to develop dynamic models that simulate the expression of regulated functional clusters using system dynamics as the modeling paradigm. The dynamic model has the considerable advantage of only requiring an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. The manipulation of input model parameters, such as changing the magnitude of gene expression, made it possible to assess the behavior of the networks through time under varying conditions. We report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different preconditioning paradigms.

dynamic modeling↗