A number of gene expression microarray studies have been carried out in the past, which studied aging and age-associated spatial learning impairment (ASLI) in the hippocampus in animal models, with varying results. Data from such studies were never integrated to identify the most significant ASLI genes and to understand their effect. In this study we integrated these data involving rats using meta-analysis. Our results show that proper removal of batch effects from microarray data generated from different laboratories is necessary before integrating them for meta-analysis. Our meta-analysis has identified a number of significant differentially expressed genes across age or across ASLI. These genes affect many key functions in the aged compared to the young rats, which include viability of neurons, cell-to-cell signalling and interaction, migration of cells, neuronal growth, and synaptic plasticity. These functional changes due to the altered gene expression may manifest into various neurodegenerative diseases and disorders, some of which leading into syndromic memory impairments. While other aging related molecular changes can result into altered synaptic plasticity simply causing normal aging related non-syndromic learning or spatial learning impairments such as ASLI.
Background: Chromosome 22q11.2 region is highly susceptible to rearrangement, specifically deletions that give rise to a variety of genomic disorders including velocardiofacial or DiGeorge syndrome. Individuals with this 22q11 microdeletion syndrome are at a greatly increased risk to develop schizophrenia.
Most familial behavioral phenotypes result from the complex interaction of multiple genes. Studies of such phenotypes involving human subjects are often inconclusive owing to complexity of causation and experimental limitations. Studies of animal models argue for the use of established genetic strains as a powerful tool for genetic dissection of behavioral disorders and have led to the identification of rare genes and genetic mechanisms implicated in such phenotypes. We have used microarrays to study global gene expression in adult brains of four genetic strains of mice (C57BL/6J, DBA/2J, A/J, and BALB/c). Our results demonstrate that different strains show expression differences for a number of genes in the brain, and that closely related strains have similar patterns of gene expression as compared with distantly related strains. In addition, among the 24 000 genes and ESTs on the microarray, 77 showed at least a 1.5-fold increase in the brains of C57BL/6J mice as compared with those of DBA/2J mice. These genes fall into such functional categories as gene regulation, metabolism, cell signaling, neurotransmitter transport, and DNA/RNA binding. The importance of these findings as a novel genetic resource and their use and application in the genetic analysis of complex behavioral phenotypes, susceptibilities, and responses to drugs and chemicals are discussed.
Transcription factors (TFs) and their combinatorial control on cis-regulatory elements play critical role in the co-expression of genes. This affects the interaction of genes in the transcriptome and thus may affect signals that cascade through cellular pathways. Using a combination of bioinformatic approaches, we sought to identify such common combinations of TFs in a set of ethanol-responsive (ER) genes and assess the role of ethanol in affecting multiple pathways through their co-regulation. Our results show that the metallothionein genes are regulated by TF motifs cAMP responsive element binding protein (CREB) and metal-activated transcription factor 1 and primarily involved in zinc ion homeostasis. We have also identified new target genes, Synaptojanin 1 and tryptophan hydroxylase 1, potentially regulated by this module. Altered arrangement of TF-binding sites in the module may direct the action of these and other target genes in intracellular signaling cascades, cell growth and/or maintenance. In addition to CREB, other key TFs identified are ecotropic viral integration site-1 and SP1. These modulate the contribution of the target ER genes in cell cycle regulation and apoptosis or programmed cell death. Multiple lines of evidence confirm the above findings and indicate that different groups of ER genes are involved in different biological processes and their co-regulation most likely results from different sets of regulatory modules. These findings associate the role of the ER genes studied and their potential TF modules with alcohol response pathways and phenotypes.
As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.
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