Date of Award

8-8-2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Applied Science

First Advisor

Qingfang He

Abstract

In the world of microorganisms, cyanobacteria are a uniquely skilled powerhouse. They participate in global nutrient and carbon cycling. They have the ability to photosynthesize, which allows them to use sunlight and carbon dioxide to produce cellular energy. However, photosynthesis has a lot of points were environmental stresses can interfere, increasing the chances of oxidative stress and nutrient imbalances that can amplify deleterious effects. One regulator in particular will be the target of this study. DspA, also known as Hik33, is a histidine kinase that perceives and regulates multiple overlapping stress responses. In this project, abiotic stress effects will be analyzed in wildtype cells and in cells where DspA has been deleted (DspA-). Some such stresses are iron limitation and high intensity light. The relationship between cell vitality and stress response will be investigated as they relate to regulation by DspA. Scanning and transmission electron microscopy will be used to assess the morphological effects of both the deletion of DspA and the effects of the aforementioned stresses. A program will be developed to help automatically process images of Synechocystis cells. A machine learning algorithm will be used to detect patterns within the image dataset. So far, this project has found that iron limitation stress, when induced by different methods, exhibits differing severity and expression. DspA- cells have been observed to have differing pigment levels and growth rates under normal growth conditions, indicating that DspA has a role in standard cell maintenance as well as stress response regulation. TEM images have shown that DspA- cells under normal conditions also have less organized, abnormal thylakoid membranes. Lastly, the program has been able to automate cell dimension measurement in cells that are not dividing. The program also helps preprocess the images for neural network analysis. With the completion of this project, we will know more about the role of DspA in stress response regulation as well as regular cell maintenance. We will also be able to apply the neural network to future Synechocystis images and automate their analysis, allowing researchers to analyze image data more quickly and effectively with fewer sources of variation. These results will add to the body of knowledge about cyanobacteria, photosynthesis, and biological applications of high-powered computational methods.

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