Dysregulated mechanisms underlying Duchenne muscular dystrophy from co-expression network preservation analysis.
BACKGROUND
Duchenne Muscular Dystrophy (DMD) is an X-linked recessive disorder with its primary insult on the skeletal muscle. Severe muscle wasting, chronic inflammation and fibrosis characterize dystrophic muscle. Here we identify dysregulated pathways in DMD utilizing a co-expression network approach as described in Weighted Gene Co-expression Network Analysis (WGCNA). Specifically, we utilize WGCNA's "preservation" statistics to identify gene modules that exhibit a weak conservation of network topology within healthy and dystrophic networks. Preservation statistics rank modules based on their topological metrics such as node density, connectivity and separability between networks.


METHODS
Raw data for DMD was downloaded from Gene Expression Omnibus (GSE6011) and suitably preprocessed. Co-expression networks for each condition (healthy and dystrophic) were generated using the WGCNA library in R. Preservation of healthy network edges was evaluated with respect to dystrophic muscle and vice versa using WGCNA. Highly exclusive gene pairs for each of the low preserved modules within both networks were also determined using a specificity measure.


RESULTS
A total of 11 and 10 co-expressed modules were identified in the networks generated from 13 healthy and 23 dystrophic samples respectively. 5 out of the 11, and 4 out of the 10 modules were identified as exhibiting none-to-weak preservation. Functional enrichment analysis identified that these weakly preserved modules were highly relevant to the condition under study. For instance, weakly preserved dystrophic module D2 exhibited the highest fraction of genes exclusive to DMD. The highly specific gene pairs identified within these modules were enriched for genes activated in response to wounding and affect the extracellular matrix including several markers such as SPP1, MMP9 and ITGB2.


CONCLUSIONS
The proposed approach allowed us to identify clusters of genes that are non-randomly associated with the disease. Furthermore, highly specific gene pairs pointed to interactions between known markers of disease and identification of putative markers likely associated with disease. The analysis also helped identify putative novel interactions associated with the progression of DMD.
Bioinformatics and System Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, MC0412, La Jolla, CA, 92093, USA. k1mukund@ucsd.edu.
Kavitha
K
Mukund
Bioinformatics and System Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, MC0412, La Jolla, CA, 92093, USA. shankar@ucsd.edu.|Departments of Bioengineering, Computer Science & Engineering, Cellular & Molecular Medicine and Chemistry & Biochemistry University of California, San Diego, 9500 Gilman Drive, MC0412, La Jolla, CA, 92093, USA. shankar@ucsd.edu.
Shankar
S
Subramaniam
D016022:Case-Control Studies; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D020388:Muscular Dystrophy, Duchenne / Q000235:genetics*