Abstract
The Human Genome Project was the first step in a scientific revolution that unleashed the full potential of the field of genomics (Durmaz et al., 2015). The project launched in the 1990s and the first draft of the human genome was published in 2003. Since this development researchers have been able to harness genetic data from patients using methods such as next-generation sequencing (NGS) and RNA-seq to draw meaningful conclusions. Two ways this data has been used are in the analysis of disease mechanisms and the development of genetic tests.
The technical report will focus on using genetic expressions to study disease mechanisms. Specifically, this technical report observes the pathogenic signaling mechanisms in Systemic Lupus Erythematosus (SLE). SLE is a chronic, heterogeneous autoimmune disease that causes damage to numerous organs (COJOCARU et al., 2011). The current understanding of disease pathogenesis is incomplete and subsequently, treatments for SLE are inadequate (Mok & Lau, 2003). The approach to this project is based on current knowledge of autoimmune diseases. Previous research has identified that dysregulated immune signaling in autoimmune disease often arises because of an improper balance in the activation and inhibition of inflammatory cells or pathways (Arakelyan et al., 2017). One potential regulatory mechanism that controls immune activation, and thus can become dysregulated, is the imbalance of kinases and phosphatases. Functionally, phosphatases and kinases have an inverse relationship: kinases are considered to activate signaling, and phosphatases are thought to inhibit signaling (Protein Phosphatases and Kinases | NEB, n.d.). Therefore, to study the pathogenic signaling mechanisms in SLE this technical report investigates phosphatases and kinases. Specifically, the report aims to identify groups of genes whose expression allows for meaningful separation between patients with SLE and control. To accomplish this goal, the technical project set out three aims: design gene sets of phosphatases and kinases, analyze patterns in the expression of gene sets, and evaluate the ability of gene sets to discriminate between SLE and control. The project used whole blood microarray data from three large data sets. The methods used to complete these aims include literature analysis, gene set variation analysis (GSVA), co-expression, binary classification machine learning (ML), and classification and regression trees (CART). Through this careful approach, this project was able to identify gene sets that differentiate between SLE and control and thus provide insight to the pathogenesis of SLE.
The second piece of this portfolio observes another developing field of genomics: genetic testing. The research paper specifically investigates the role of preimplantation genetic testing (PGT) which is a technology sometimes used in in vitro fertilization (IVF) (Klimczak et al., 2021). IVF is an alternative to sex as a method used to impregnate a woman. In IVF the embryo is fertilized outside of the body. In the IVF process, multiple embryos are fertilized but only one is eventually implanted into the womb. PGT is a method for conducting genetic tests on the fertilized embryo and is used to determine which embryo should be implanted for development. PGT is a controversial technology as it provides doctors, parents, and legislators the opportunity to decide which embryo is chosen. Internationally PGT is regulated to varying degrees (Ginoza & Isasi, 2020). This research paper investigates the regulation of two countries with opposite approaches to regulation: Switzerland and the United States. In order to understand the sociotechnical system related to PGT, this paper takes a holistic approach by investigating policy, history, culture, and ethics related to PGT.
Progress in genomic research has led to the development of many new technologies. These technologies have allowed for great discoveries but also led to widespread concern about possible gross misuse. This portfolio investigates two of these new technologies using one to attempt to understand a disease, and another to understand the sociotechnical system surrounding the technology itself. Together this work allows one to thoroughly understand the current applications of genomic technology.
