Submission
| Title: | Gene Co-Expression Network Analyses in Mild Cognitive Impairment |
| Presenter: | Valerie Dorsant-Ardon |
| Institution: | Indiana University Purdue University Indianapolis (IUPUI) |
| Authors: | Valerie Dorsant-Ardon MD, MS. Indiana University School of Medicine Indianapolis, IN, USA Apoorva Bharthur Sanjay MS, Ph.D. Rion Brattig Correia Ph.D. Luis M Rocha Ph.D. Liana G Apostolova MD, MS, FAAN. |
Abstract
| Background/Significance/Rationale: | Multiomic data analysis has been extensively developed in recent years. Particularly, microarray data can be used as biomarkers of disease of progression specially in pathologies as cancer, autoimmunity and others. This modality of data analysis can also be employed in other multifactorial diseases as Alzheimer’s disease specially the progression from normal cognition to mild cognitive impairment and finally to dementia. These genes work in intricate relationship with each other. Thus, the establishment of computational gene interaction networks shows the complexity of the biological systems but increases the complexity of the analysis, for this reason, the removal of redundant edges leaves a network backbone revealing important driver nodes that are essential for the observed interactions, reducing the number of genes of interest and narrowing potential diagnostic or therapeutic targets. |
| Methods: | We used data from the ImaGENE study and the Weighted gene co-expression network analysis (WGCNA) pipeline to analyze complex gene-gene interactions of mRNA transcripts obtaining hierarchical clustering of genes and eigengenes in a sample of 160 individuals. The backbones were obtained using the shortest path computation and Gene ontology was used to classify processes associated with these genes. |
| Results/Findings: | We identified 42 hubs of differentially expressed genes and we analyzed the group with the strongest association with amnestic MCI phenotype (r=0.45, pfdr<0.0001). This cluster consisting of 46 genes was further classified according to function and interactions with other nodes in the backbone identifying processes like Calcium signaling, Nucleotide excision repair, Fatty acid metabolism among others. |
| Conclusions/Discussion: | There is an identifiable difference in gene expression in individuals with MCI compared to normal controls, the analysis of the backbone of the gene hubs allowed to reduce the number of genes of interest. |
| Translational/Human Health Impact: | This approach can help to elucidate important biological interactions, potential therapeutic targets and could eventually be used as risk factors markers for the development of MCI and AD. |
