The spread of misinformation has increased rapidly in the last few years on many social media platforms. Our understanding of its effects, strategies, and influence is growing along with the information in real time. During political elections, we have seen that misinformation can become contagious and pose harmful threats to many aspects of our society and our political environment. How exactly does misinformation disseminate online, and are these social media posts used as a political strategy? To delve deeper into this study, I examine the relationship between false information online and legal cases that challenge election results. Using a mix of qualitative and quantitative methods, I analyze articles, data, and social media posts concerning the 2020 and 2022 elections in Maricopa County. With this data, I identify recurring narratives and influential political figures, using Python visualizations and codebooks for the empirical evidence found online. I anticipate that my findings reveal a pattern of legal cases being used to spread false political narratives that mislead the public about the voting process in Maricopa County. Since Maricopa County is the fourth most populous county in the United States, this study provides insight into how online users receive information about political elections and voting processes. I also anticipate that utilizing courts and election lawsuits can be an effective strategy to uphold and spread misinformation. Further research into other counties may demonstrate similar patterns and narratives with misinformation and U.S. elections.

One way to obtain plasma is by using a Direct Current (DC) discharge. Plasma is an ionized gas, meaning the separation of positive ions and electrons in a gas. There are three main variables when it comes to a DC discharge configuration. A gas forms into a plasma in an isolated space of low pressure between 2 electrodes, a cathode and an anode. Voltage must constantly be applied across the cathode and the anode to maintain the plasma. The initial voltage needed to initiate the separation of electrons and protons in a gas to produce a plasma is called the breakdown voltage. Our study investigates the configuration of a DC discharge plasma and the correlation between electrode separation, breakdown voltage, and pressures in a DC discharge environment. We constructed an environment consisting of long oval glass tube housing an anode and cathode on each side. A vacuum pump is attached to the glass container to extract air to reduce pressure in our glass tube. To maintain an ideal pressure, we established a concealed air tube connected to our glass tube with a fine adjust valve to let air into our glass tube at the same rate as our vacuum pump extraction resulting in a stable low pressure in our experimental configuration. We designed and conducted a series of tests to investigate the properties of a DC plasma formation. Moreover, we wanted to establish evidence of the Paschen Curve, which relates the breakdown voltage and the product of electrode distance and pressure in DC discharge. We experimentally determined the optimum pressure and electrode separation distance product for plasma breakdown in air and Argon gas. DC plasmas can be utilized as sputter sources to deposit thin films for solar panels; characterizing the breakdown voltage is significant at low pressures and short spacing to control the sputtering rate.

My project involves making new tools for the genetic manipulation of Staphylococcus aureus. S. aureus is an important and pervasive human pathogen, however, much is still not known about its pathogenesis pathway and virulence genes. To better understand the genes and mutations that make S. aureus successful at causing disease, it is necessary to induce specific genetic changes and assess their impact on the organism’s virulence. However, currently available tools for bacterial genome engineering have yet to be optimized for S. aureus. Building on a successful recombineering system, previously developed by the Salipante lab, that is able to edit the S. aureus genome, we aim to address this need by using a dominant negative mutant protein of the mismatch repair (MMR) system to achieve suppression of DNA repair. Recombineering is the process of incorporating mutagenic DNA molecules into a host genome through the recombinase enzymes. Dominant negative mutations in the highly conserved MutL protein have been shown to disrupt the DNA base MMR pathway in several other microorganisms but have not yet been evaluated in S. aureus. My project aims to construct a temperature sensitive vector that highly expresses a ssDNA recombinase that is active in S. aureus along with a dominant negative mutL mutant able to bypass the MMR pathway. We anticipate that WT mutL should be able to repair a single base mutation since it has a functional MMR, reducing the number of successful recombinants relative to experiments performed with the multiple base pair mutations. The dominant negative mutL strains should not be able to correct a single base pair mutation, resulting in a similar number of drug-resistant transformats when recombineering with either a single base pair or many base pair mutations. This will allow us to better generate custom mutant strains and subsequently test the functions of various S. aureus genes towards a variety of clinically relevant phenotypes.

Cyanobacteria are tiny photosynthetic microbial organisms responsible for producing roughly an eighth of the oxygen we breathe. Synechococcus is a model cyanobacteria, meaning the species has characteristics making it easy to study and modify. As a scientific community, we don’t know the function or purpose of many genes expressed by Synechococcus. The goal of this project is to determine the function of particular genes hypothesized to be important to the adaptive survival of Synechococcus in different environments. We are approaching this by building a start-to-finish gene characterization method, starting with computational analysis to identify genes of interest, followed by knocking out, or disabling these genes and observing the effect on the growth of the culture. On the computational side, I’m now analyzing residual gene expression, using that information to characterize gene clusters, and analyzing external data to infer genetic context. On the laboratory side, I’ve characterized the growth of the un-modified base strains and developed procedures for genetic modification. Identifying the function of Synechococcus genes allows scientists to better study the response of Synechococcus to varying environments, which is especially important in a changing climate. Increasing understanding of the molecular mechanisms of Synechococcus also opens the door to genome engineering for the production of biofuels, plastics, and other commodities, or for using Synechococcus as a tool for bioremedial carbon sequestration. Additionally, the genes of Synechococcus are similar to those in other related oceanic microbes such as Prochlorococcus, the most ubiquitous photosynthetic organism in the world. For all these reasons, Synechococcus is an important model organism, and a deeper understanding of its biology will bolster our sparse understanding of marine genomics.

Half a million people develop drug resistant tuberculosis (TB) each year. Cases of drug resistant TB often result in poorer outcomes both healthwise and economically for patients, and many populations lack access to the resources needed to treat resistant TB. Increased antibiotic resistance has resulted in an urgent need to develop new, cost-effective drugs that are effective against Mycobacterium tuberculosis, the bacteria responsible for TB. In my research, I am using deep learning methods to design peptides that bind to the enzyme ClpP, a vital protease and known antibiotic target in M. tuberculosis. A class of drugs called Acyldepsipeptides (ADEP) have been shown to bind to ClpP and cause cell death in M. tuberculosis by preventing the formation of the ClpP complex with necessary ATPases, resulting in significantly lower proteolytic activity. We used the structure of ADEP as a basis for the peptide design and employed Rosetta, a macromolecular prediction and design software, to generate cyclic peptides bound to ClpP. I then used a sequence based deep learning tool to generate multiple sequences for each backbone design and computationally validated the resulting structures with AlphaFold, a highly accurate, machine learning based structure prediction tool. The structure of the ClpP binding interface resulted in it being a difficult target to design for with current deep learning methods. One peptide binder was predicted to bind to ClpP in our preliminary design rounds. We will chemically synthesize this binder and test it against ClpP in an enzyme inhibition assay. If the binder inhibits ClpP, it can serve as a basis for an effective and low cost drug that targets the ClpP enzyme in drug resistant TB. We will also expand and refine our design pipeline to produce more binder designs that can serve as viable drug candidates.

Cyanobacteria are ancient single-celled photosynthetic organisms, prevalent throughout Earth's oceans. Over billions of years, cyanobacteria have evolved genes that enable them to survive across a diversity of adverse, ever-changing environmental conditions. However, researchers are faced with the problem of not understanding the role of many of these genes. This research project entails tracking down the function of some high variance genes in marine Synechococcus, an important model organism and a genus of cyanobacteria. We will test gene function by generating knockout strains in which a gene of interest is inactivated, and testing the growth of these mutant strains in various conditions. In particular, our project focuses on the importance of the flavodoxin gene, which codes for an electron transport protein that is involved in photosynthesis and is expressed in response to iron scarcity. This gene inactivation is done with a plasmid, which is a genetic structure in bacteria that can replicate itself independent of bacterial chromosomal replication, that’s enabled to knock out the flavodoxin I gene when inserted into Synechococcus cells. We insert the plasmid into our Synechococcus cells and once the DNA is taken up by the cell, we then use CRISPR technology to remove the gene. Successfully creating the flavodoxin knockout of Synechococcus establishes the procedures necessary for generating knockouts of other genes that could be expressed in similar patterns as flavodoxin. Ultimately, this research furthers our understanding of how Synechococcus’ genes allow it to adapt to various environments and contributes to ongoing research on how organisms might withstand the pressures of Earth’s ever-changing climate.

The mammalian target of Rapamycin (mTOR) signaling cascade plays an important role in a variety of cellular processes, such as autophagy, cell proliferation, and protein synthesis. Previous depictions of signaling through the mTOR pathway have suggested linear signal transduction; however, this does not accurately represent the network of interactions between proteins in complexes of this pathway nor their dynamics in response to stimuli. To better characterize mTOR protein interaction network (PIN) dynamics, SEPS lab has developed a panel of antibodies targeting key proteins in mTOR signaling for use in quantitative multiplex co-immunoprecipitation (QMI), a method of detecting changes in protein interactions using flow cytometry. Following QMI of mTOR signaling proteins in serum-starved and serum-refed mouse 3T3 fibroblasts, I validated changes in select interactions from this pool separately via co-immunoprecipitation and western blot analysis. The lab then applied inhibitors of mTOR pathway constituents, including PI3K, AKT, MEK, ERK, and mTOR, to define modules of interactions that comprise the PIN and observe changes in these interactions with stimulation after application of each inhibitor, which I again validated via co-immunoprecipitation and western blot analysis. Finally, to validate antibody specificity in human cells, I prepared human embryonic kidney 293 (HEK293) cells for short interfering RNA (siRNA) transfection and knockdown of mTOR pathway proteins targeted by antibodies from the initial panel. Assuming these HEK293 cells lack any additional proteins with high affinity for these antibodies, I expect flow cytometry data to reflect specificity seen in the 3T3 fibroblasts. Conducting this validation is critical for ensuring the reliability of the PIN changes observed in QMI analysis. These experiments allow us to evaluate coordinated interactions between mTOR pathway proteins and their dynamics during signaling events, which is highly useful in developing treatment strategies for mTOR pathway-associated disorders.