Genetics of PCOS: A systematic bioinformatics approach to unveil the proteins responsible for PCOS

Abstract

Polycystic ovary syndrome (PCOS) is a hormonal imbalance in women, which causes problems during menstrual cycle and in pregnancy that sometimes results in fatality. Though the genetics of PCOS is not fully understood, early diagnosis and treatment can prevent long-term effects. In this study, we have studied the proteins involved in PCOS and the structural aspects of the proteins that are taken into consideration using computational tools. The proteins involved are modeled using Modeller 9v14 and Ab-initio programs. All the 43 proteins responsible for PCOS were subjected to phylogenetic analysis to identify the relatedness of the proteins. Further, microarray data analysis of PCOS datasets was analyzed that was downloaded from GEO datasets to find the significant protein-coding genes responsible for PCOS, which is an addition to the reported protein-coding genes. Various statistical analyses were done using R programming to get an insight into the structural aspects of PCOS that can be used as drug targets to treat PCOS and other related reproductive diseases.

Keywords: MeV; Microarray; Modeller; PCOS; Phylogenetic analysis; R; RaptorX; String.

Fig. 1.1

Unrooted phylogenetic tree representing 43 genes.

Fig. 1.2

Rooted phylogenetic tree of 43 genes involved in PCOS.

Fig. 2

String analysis of closely identified proteins. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

GDS4399 polycystic ovary syndrome: granulosa cell samples.

Fig. 3.1

Box plot of dataset GSE34526 before normalization.

Fig. 3.2

Box plot of dataset GSE34526 after normalization.

Fig. 3.3

Histogram plot of GSE34526.

Fig. 3.4

Volcano plot for finding significant and non-significant genes using t-test. Red color represents the significant genes and black color represents non-significant genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3.5

Sam test from MeV using normalized PCOS dataset.

Fig. 3.6

Plotting of significant and non-significant genes using MeV using normalized PCOS dataset.