Characterizing Regulatory Factors of the Sumoylation System
The effects that influenza’s seasonal epidemics have on human health and the global economy have been clearly noted; while they are indeed very impressive, the impact of influenza pandemics arguably surpass other known infectious agents. Influenza A virus attaches to, enters, and infect cells by releasing its segmented genome which localize to the nucleus then use the host’s cellular machinery to replicate and create viral progeny. The virus is able to hijack transcriptional components as well as to interact with other known and unknown host proteins which ultimately allows for a balance between cell viability and viral propagation. One known system that is required for the viral life cycle is the SUMOylation system. Several influenza A viral proteins need to be SUMOylated to function properly. Our lab was the first to show that proteins from influenza A are SUMOylated in a tissue culture model. This was followed by verification, by ours and other groups, that non-SUMOylatable forms of the same viral proteins resulted in loss of function or diminished viral propagation. The Non Structural protein 1 (NS1) is known for its role as an immune response antagonist. NS1 is also a protein that requires SUMOylation to function properly. Rather than target viral components which easily overcome current therapeutics with single point mutations, understanding and then modulating the SUMOylation system to impede viral replication may be a viable alternative to combat influenza. The SUMOylation system has been tied to both the immune and the cellular stress response systems. Regulation of the SUMOylation system, however, is not yet fully understood. The aim of this study is to characterize potential regulatory factors of the SUMOylation system which could in turn have a downstream effect on the cellular stress and immune response. This will be accomplished by using several models which are known to induce an increase in the SUMO profile; both expression levels of SUMO as well as its conjugation to target proteins. The first aim of this project is to optimize all protocols needed to induce increases in SUMOylation using three models; heat shock, hypoxia, and influenza infection. The second part of Aim 1 will lead to the standardization of protocols to determine if the changes in SUMO expression and conjugation is regulated at the transcriptional level. Quantitative Reverse Transcriptase - Polymerase Chain Reaction (qRT-PCR) will be used to measure SUMO1 transcript levels after treating cells with several factors known to induce SUMOylation increases, e.g. Infection, heat shock, and hypoxia. The second aim of this project, to further understand how SUMO is regulated, will be to optimize the steps required to conduct an efficient and fully reproducible proteomic analysis of SUMO modified proteins. Collection of the samples requires standardized procedures that minimize loss and contamination of the sample. Quality control measures are included in order to avoid degradation and inaccuracy. The differentially SUMOylated proteins, after infection, may be key players involved in the immune response regardless if SUMOylation activates or inhibits them. Antibodies against SUMO1 will allow for the isolation and enrichment of the SUMO modified proteins in the most non-artificial biologically accurate setting. Proteins which interact with SUMO before and after treatment will be compared after analysis by Tandem Liquid Chromatography Mass Spectrometry (LC-MS/MS). To perform this analysis a large amount of antibodies will be required. Liquid tumors, known as ascites, are able to form in mice when inoculated by fusion cells. These hybridoma cells are comprised of b-cells fused with myeloma cells. These cells can be grown in animal cavities until large volumes of ascitic fluid, with a high concentration of antibodies, are later extracted and sold commercially. Optimization of this technique is a major focus of my second aim.
Quintanar, David, "Characterizing Regulatory Factors of the Sumoylation System" (2017). ETD Collection for University of Texas, El Paso. AAI10279911.