Native mass spectrometry (MS) experiments provide direct mass measurements of intact proteins and protein complexes. Protein samples for native MS are prepared in solutions that mimic physiological conditions, which maintain a protein’s native folded state before entering the gas phase of the mass spectrometer. Ammonium acetate solution is typically used due to its volatility and relevant ionic strength. However, protein purification protocols typically require inorganic salts and detergents to maintain protein stability. Native MS experiments can be hindered or made uninterpretable by those salts and detergents. Furthermore, the presence of protein modifications or multiple proteins can make native mass spectra difficult to interpret. Anion exchange chromatography (AEX) is well suited for the requirements of native MS, as it can simultaneously desalt, remove non-ionic detergents, and separate proteins or proteoforms directly into an ammonium acetate solution. This project seeks to develop a comprehensive method for desalting, removing non-ionic detergents, and separating proteins through an ammonium acetate-based anion exchange chromatography method. Preliminary experiments in egg whites, a complex matrix with a high sodium concentration, showed separation and four distinct proteins using an AEX pH gradient from pH 10 100 mM ammonium acetate to pH 4 100 mM ammonium acetate. Native MS analysis showed low interference from sodium or other contaminants and the various modified forms of those proteins were identified. Refinement of this preparation technique can result in the improvement and efficiency of native MS analysis of proteins.

Each year, nearly 6 million deaths worldwide are caused by lower respiratory tract infections, diarrhoeal diseases, and tuberculosis. These infectious diseases are leading causes of death worldwide. Currently, the pharmacological treatment of infection is encumbered by the presence of inaccessible intracellular pathogen reservoirs, and the need for prolonged treatment regimes. Drug delivery systems can be engineered to overcome these biological barriers for effective treatment by facilitating intracellular delivery and tailored release. Extended release of drugs alleviates the need for exhaustive treatment regimes and increases patient compliance. Furthermore, this can decrease treatment duration, reduce the cost of treatment, and improve access for disadvantaged populations. Our research utilizes modular polymeric prodrugs composed of molecular targeting agents, cleavable linkers, and antimicrobial drugs. This platform permits facile alteration of functional modalities, enabling custom tailored treatments for each disease setting. By utilizing tunable linkers, we can control the precise delivery mechanism and therefore direct the localized release of drugs. To characterize the controlled release of antimicrobial drugs from our polymeric prodrugs, we are designing high performance liquid chromatography (HPLC) and liquid chromatography mass spectrometry (LC-MS) assays. The developed assay will evaluate the release mechanism with stability and release studies. Furthermore, the robust methodology will enable the determination of the pharmacokinetics of the polymeric prodrug delivery system. The assay and results from this study will ultimately support the development of improved therapies.

Cystic fibrosis (CF) is a genetic disorder affecting the lungs, and chronic polymicrobial lung infections are responsible for decreased life expectancy and poor quality of life of CF patients. Staphylococcus aureus (SA) is a microbe commonly found in the respiratory tract of CF patients, and this organism adapts within the lung environment to establish chronic infections. Among the most common bacterial adaptations is the emergence of mutants known as small colony variants (SCVs). There are multiple subtypes of SCVs that arise from mutations in different metabolic pathways. Recent studies have demonstrated that SCVs are prevalent in the CF respiratory tract, are more difficult to treat with antibiotics, and are associated with worse lung health. SCVs are very difficult to detect in clinical laboratories, thus complicating the selection of appropriate treatment by physicians to improve the health of CF patients. The goal of this study is to determine if SCVs can be more readily detected than with standard culture by using mass spectrometry to identify proteins that distinguish these variants from normal colony S. aureus. Matrix Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) will be used to identify proteins unique to specific SCV subtypes by separating those proteins using ionization and Tandem Mass Spectrometry. This analysis will generate isolate-specific spectra of peaks which will subsequently be compared to each other using Principal Coordinate Analysis (PCoA). We hypothesize this technique will identify differences between proteins produced by each SCV type, which can then be distinguished from normal colony S. aureus, allowing the rapid identification of these variants. As a result, we anticipate the detection of SCV’s will improve, which will help inform physicians to select appropriate treatments to target SCVs.

Numerous interventions and genetic modifications have been shown to extend lifespan across a diversity of species. However, these studies often assume that extended lifespan is synonymous with extended healthspan. Recent research in the nematode worm, Caenorhabditis elegans, has questioned this assumption, and suggests that increasing lifespan can prolong the frailty associated with old age. This is particularly important for humans, as increasing lifespan without a corresponding increase in healthspan could spell disaster. The majority of healthcare costs are associated with aging-related pathologies, and prolonging life without prolonging health could radically inflate these costs. To parse out the genetic relationship between healthspan and lifespan, we have turned to Drosophila melanogaster, a well characterized model organism for studies on the genetics of aging. We have collected lifespan data as well as multiple measures of healthspan, such as negative geotaxis (climbing), intestinal permeability, Cold stress resistance, and metabolomics data across 16 inbred genotypes. We found a strong positive correlation between lifespan and climbing, and no correlation between cold stress resistance and lifespan. This confirms the importance of lifespan as a primary parameter in aging studies, but suggests additional measures of health are needed to accurately assess health. 

Trace metals are a vital part of primary production for phytoplankton, and are a scarce resource around the world. Iceland's unique geology allows for these metals to be recycled through geologic processes as they are released from volcanic ash and trapped in ice, frozen in time until being released again through melt. Iceland's glaciers are melting at a rapid pace due to the warming climate, and are releasing and transporting these trace metals that have not been exposed to the environment for an extended period of time. Sólheimajökull, a glacier located in Southeast Iceland, is no exception and has seen an increasing retreat as it melts at an accelerated pace. The metals being released and transported in this melt are found to be magnitudes higher in abundance relative to the open waters surrounding the island. Introducing these metals to the open ocean could cause rapid changes in the biogeochemical processes of the surface ocean. With this increased melt, we're likely to see an increase in release and transportation of these metals, some of which may reach the coast and cause ecological devastation. In this study, I aim to quantify the trace metal deposition through various freshwater streams of Sólheimajökull.

Every minute, Twitter users send hundreds of thousands of tweets, providing a rich resource of publicly available text data. Our goal is to use this data to learn from and imitate the sentence structure of specific accounts. To this end, we develop mRkov, a statistical tool that takes the username of a Twitter account, also known as a handle, as input and outputs fake tweets that mimic the linguistic style of the tweets from that handle. We built mRkov into an R software package as well as an interactive and user-friendly web tool that walks the user through the process of using our software. mRkov first scrapes tweets posted from the input Twitter handle, and then after processing the text and sentiments of the scraped tweets, generates new tweets using Markov chain simulation. Markov chains consist of a sequence of items, where each item is probabilistically sampled dependent only on the preceding item in the chain. By using non-independent sampling, the Markov chain method generates a sample that mimics the true distribution. In this application, the Markov chain is a sequence of words, and the distribution is the sentence structure of the tweets. mRkov also allows users to provide input that influences the sentiment of tweets in order to generate tweets that tend to be more “positive” or “negative” in sentiment. Tools such as mRkov help us better understand patterns of speech and writing. This has many useful applications, including identifying if multiple accounts are coming from the same source or writer, analyzing and comparing how the style and sentiment of different accounts change over time, and detecting bots or other fake accounts.