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Comparative Study
. 2013;8(2):e57655.
doi: 10.1371/journal.pone.0057655. Epub 2013 Feb 28.

Identification of shared genes and pathways: a comparative study of multiple sclerosis susceptibility, severity and response to interferon beta treatment

Sunil Mahurkar  1 , Max Moldovan, Vijayaprakash Suppiah, Catherine O'Doherty
Affiliations
Comparative Study

Identification of shared genes and pathways: a comparative study of multiple sclerosis susceptibility, severity and response to interferon beta treatment

Sunil Mahurkar et al. PLoS One. 2013.

Abstract

Recent genome-wide association studies (GWAS) have successfully identified several gene loci associated with multiple sclerosis (MS) susceptibility, severity or interferon-beta (IFN-ß) response. However, due to the nature of these studies, the functional relevance of these loci is not yet fully understood. We have utilized a systems biology based approach to explore the genetic interactomes of these MS related traits. We hypothesised that genes and pathways associated with the 3 MS related phenotypes might interact collectively to influence the heterogeneity and unpredictable clinical outcomes observed. Individual genetic interactomes for each trait were constructed and compared, followed by prioritization of common interactors based on their frequencies. Pathway enrichment analyses were performed to highlight shared functional pathways. Biologically relevant genes ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A were identified as common to all 3 MS phenotypes. We observed that the highest number of first degree interactors were shared between MS susceptibility and MS severity (p = 1.34×10(-79)) with UBC as the most prominent first degree interactor for this phenotype pair from the prioritisation analysis. As expected, pairwise comparisons showed that MS susceptibility and severity interactomes shared the highest number of pathways. Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes. Finally, FYN was the most common first degree interactor in the MS drugs-gene network. By applying the systems biology based approach, additional significant information can be extracted from GWAS. Results of our interactome analyses are complementary to what is already known in the literature and also highlight some novel interactions which await further experimental validation. Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Number of shared first degree interactors between each of the three GWAS phenotype categories.
Figure 2
Figure 2. Statistical significance (p-value and interactor score) of shared first degree interactors between paired phenotype categories.
Figure 3
Figure 3. Number of shared pathways relating to the interactomes of the three GWAS phenotype categories.
Figure 4
Figure 4. Primary “drug modulated/modulating” genes (large blue circles) and their extended common interactors (small red circles).
‘+’ on each gene (node) indicates that the gene’s linkages are suppressed and ‘−’ indicates all linkages of the gene have been shown. The different coloured interaction between genes represents various biological processes that identified the interactions. Every line (edge) connecting each gene pair represents an interaction. The colour of the line specifies the experimental method used to identify that interaction. For example, “black” coloured line connecting genes ITGAV and FN represents the transcriptional upregulation method. If an interaction is identified by several different biological methods, then the line will be coloured in segments with corresponding colours for each methods. For example 5 different colours indicated in line connecting genes ITGAV and ITGB1 represent 5 biological methods used for identification of this interaction i.e. in vivo, inferred by curator, affinity chromatography, co- immunoprecipitation and pull down method.
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Grants and funding

SM is supported by a postgraduate scholarship provided by the School of Pharmacy, University of South Australia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.