Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia

Eduardo Tejera,Irene Rebelo,João Bernardes

doi:10.1186/1755-8794-6-51

Abstract

BackgroundIn this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively.MethodsAfter co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods.ResultsSignificant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia.ConclusionsWeighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.

Highlights

In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches
The comparative enrichment module analysis reveals that all modules have a certain overlap between N and PRE group (Figure 4 Left) to some degree, suggesting that the genes are grouped in a similar fashion between the two conditions
In the 14 genes with maximum KDist values- NDRG1, FLT1, TPBG, FSTL3, FLNB, INHBA, SPAG4, INHA, HK2, HEXB, TPI1, BCL6, LEP, QSOX1- we found FLT1, FLNB, INHA, LEP and INHBA, which are some of the nodes with greater intramodular connectivity

Summary

Introduction

We explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. Preeclampsia remains a leading cause of maternal/fetal mortality and morbidity associated with gestational hypertension and proteinuria. Due to possible multifactorial causes involved [1,2,4], an increase in “omics” experimental approaches is noted, generating a large amount of information, not always integrated or analysed by recent methodologies. Some bioinformatics analysis were performed on specific microarray assays [5,6,7], and our group has recently carried out an extensive review of related data, processing multiple microarrays combined with text mining tools that led to the identification of several specific genes [8]. We present a different strategy focused on gene prioritization by co-expression network analysis and genetic algorithms optimization.

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Discussion

Conclusion