Two-color Dye-swap DNA Microarray approach toward confident gene expression profiling in PMCAO mouse model for ischemia-related and PACAP38-influenced genes

Abstract

Toward twin goals of identifying molecular factors in brain injured by ischemic stroke, and the effects of neuropeptide pituitary adenylate-cyclase activating polypeptide (PACAP) on the ischemic brain, we have established the permanent middle cerebral artery occlusion (PMCAO) mouse model and utilized the Agilent mouse whole genome 4 × 44 K DNA chip. PACAP38 (1 pmol) injection was given intracerebroventrically in comparison to a control saline (0.9% NaCl) injection, to screen genes responsive to PACAP38. Two sets of tissues were prepared, whole hemispheres (ischemic and non-ischemic) and infract core and penumbra regions at 6 and 24 h. In this study, we have detailed the experimental design and protocol used therein and explained the quality controls for the use of total RNA in the downstream DNA microarray experiment utilizing a two-color dye-swap approach for stringent and confident gene identification published in a series of papers by Hori and coworkers (Hori et al., 2012–2015).

Unraveling the Progression of Ischemic Core Genome-Wide by Bioinformatics Analysis of Permanent Middle Cerebral Artery Occlusion (PMCAO) Mouse Model Brain Regions Genes

“…Samples were then quickly immersed in liquid nitrogen and stored in -80ºC prior to further analysis. Animal care and experimental procedures were used as approved by the Institutional Animal Care and Use Committee of Showa University (School of Medicine), Tokyo, Japan, and the PMCAO model mice, (PACAP38 treatment), dissection of brain, sampling and storage, and total RNA extraction followed by DNA microarray analysis was performed using our established dye-swap approach as described previously [11][12][13][14][15][16]. bioinformatics tool that provided us with the latest annotations of genes.…”

“…The neuropeptide, pituitary adenylate-cyclase activating polypeptide (PACAP) [1][2][3], is the major focus of research as a neuroprotective factor in the group of Prof. S. Shioda [4][5][6][7][8][9][10][11][12][13][14][15][16]. A major emphasis of the research is on the neuroprotective effects of PACAP38 on the brain, particularly in brain ischemia [17].…”

“…Therein, a permanent middle cerebral artery occlusion (hereafter referred to as PMCAO) mouse model has been established and used for unraveling of the genome-wide gene expression profiles by high-throughput omics approach, namely DNA microarray technology. DNA microarray analysis of the whole brain/ischemic hemisphere and specific brain regions of the ischemic core hereafter, the IC and penumbra hereafter, the P with or without PACAP38 treatment have been performed [11][12][13][14][15][16]. It is to be noted that the reason for utilizing intraluminal filament technique-based PMCAO model over the transient MCAO is to avoid reperfusion injury, in our research model (PMCAO).…”

“…However, it should be emphasized that different research groups are using different stroke models such as the MCAO, resulting in greater insight into how PACAP treatment influences the brain ischemia. Overall, these genomic data on differential gene expression in the brain of mouse PMCAO model resulted in us i) obtaining the transcriptome profiles of ischemic brain hemispheres along with the diverse categories of gene families being modulated under the ischemic condition [11][12][13], ii) unraveling specific gene expressions and localization of molecular factors in the IC and P effected specifically by the PACAP38 treatment [14,15], and iii) providing the explanation and validation of the dyeswap, two-color DNA microarray approach [16]. The vast inventories of differential gene expressions generated from these numerous analyses have not only revealed the importance of both whole hemisphere and region-specific analyses in genome-wide identification of target molecular factors that might play a role in the neuroprotective function of PACAP38, but also provided a valuable resource for further study by the scientific community.…”

The inference of predictor set in gene regulatory networks using gravitational search algorithm

“…With the advent of technologies to extract the gene expression data like DNA Microarray [6], SAGE [7] and RNA sequencing [8], the measurement of ten thousands of gene expression provided simultaneously. By the increasing in availability of the gene expression data, the scholars pay more attention to the relations among genes and their functions.…”