Abstract
BackgroundHigh-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes.ResultsBy mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus.ConclusionsThis research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network.
Highlights
High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research
Our research here probes the effectiveness of graph theoretic modeling/application by identifying key structural nodes in a dual-state temporal correlation network made from high-throughput data
There was not a significant loss of gateway nodes when switching from 65% to 75% filter (30 gateways to 26 gateways, respectively), but changing the cluster threshold from 75% to 85% resulted in a major loss (26 gateways to 4)
Summary
High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. Well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Our research here probes the effectiveness of graph theoretic modeling/application by identifying key structural nodes in a dual-state temporal correlation network made from high-throughput data. It has been observed that in a modular correlation network, certain genes connect clusters between different states (i.e. stage I vs stage II) [7] but the clusters connected by those genes rarely share more than a handful of genes Those genes, serving almost as a pivot between two states become interesting targets of study in this research as they tend to link large, dense clusters of genes, and would serve as a readily available and reproducible candidate in many publicly available gene expression datasets. A proof-of-concept is presented using expression data from the hippocampus of C57/Bl6 mice at 2 months and 16 months; the results of which suggest that these gateway genes provide insight into what drives the aging process in the murine brain
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