Analyzing Sex-Biased Gene Expression in Autoimmune Diseases

Abstract

In this project, I plan to analyze sex-biased gene expression in autoimmune diseases by using a dataset containing information about people’s cell counts. Autoimmune diseases happen when your immune system starts to attack its own healthy cells. No exact cause has been pinpointed, but some suspected causes are sex, race, genetics, and environmental factors. In terms of sex, autoimmune diseases are more prevalent in women than men. In all autoimmune disease cases, women make up 75% while men only make up 25%. Scientists have thought that this disparity could be due to hormonal factors. As we know, women have constantly fluctuating hormone levels, and this has been connected to autoimmune diseases. One study was performed to evaluate the effects of changing prolactin levels, a hormone that contributes to the production of milk in mammals. The study found mice with a prolactin-inhibitor had longer longevity and produced more antibodies that detect systemic lupus erythematosus (SLE), which is an autoimmune disease. On the other hand, mice with glands that produce more prolactin had accelerated mortality and proteins in their urine, which is a key symptom of SLE. For this project, I used R and RStudio, which is a programming language that allows me to analyze vast amounts of data. The database I used is called DICE which contains information about the donor’s sex, race, ethnicity, and the count of various immune cells per 1 million transcripts. The data collection was done using RNA-Seq, which is a sequencing technique used to quantify RNA in a sample. In RStudio, the code I implemented followed a series of steps to build to a conclusion. To begin with, I eliminated data columns that aren’t needed, after which I filtered the dataset into one with females and one with males. Next, I calculated the mean of each cell type for each divided dataset. Finally, I noted the differences in sexes by subtracting the male average from the female average for each cell type and calculating the absolute value of that difference. After this analysis, I found NK cells and Naive CD 4 +T cells have the largest differences, each of which have been found to be abnormal in count or quality in people with autoimmune diseases. For future direction, I plan to narrow down on specific genes that contribute to the sex-disparity in autoimmune diseases.