Abstract
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.
Highlights
One of the most significant effects of aging is the decrease in normal brain functions, cognition and memory
In order to overcome some limitations in traditional metaand pathway analysis, we explored the option of using a mathematical modeling approach that could better utilize the information captured in microarray data
We chose to use WGCNA and applied it on a set of R7 exploratory datasets containing young rats that were learning unimpaired and aged rats that were predominantly learning impaired. This analyses identified a set of gene network modules
Summary
One of the most significant effects of aging is the decrease in normal brain functions, cognition and memory. Hippocampus in the brain is integral to memory function including spatial memory both in humans and in rodents (Morris et al, 1982; Burgess, 2002), microarray gene-expression data were generated using the hippocampus tissue These datasets allowed us to assess a combined gene expression changes related to aging, as well as ASLI in rats across multiple studies (Uddin and Singh, 2013). IPA pathway or similar knowledge base analysis can only model gene networks based on information that is available in the literature Such analyses are unable to fully utilize the gene transcript expression information captured by the microarray data. To overcome the above limitations, mathematical modeling of gene networks from large scale gene-expression data is becoming a popular alternative choice in the network discovery process, and has proven highly useful in recent years
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