Mass spectrometry has been shown to be a powerful tool that allows researchers toquery and understand different biological systems through the analysis of the chemistry surrounding the system. Specifically, the fields of proteomics and metabolomics have benefited greatly from both hardware and software innovations that have been developed over the decades. Improvements in mass spectrometry hardware allows for the development of novel acquisition methods to collect data types previously impossible to collect, while improvements in mass spectrometry software are two fold: 1) novel robust software and data analysis pipelines allow for researchers to efficiently analyze increasingly large amounts of various types of data, and 2) improvements in software accessibility increases researcher productivity by offloading menial informatics work to computers. Importantly, novel hardware and software are still being developed daily with ever increasing frequency. This thesis leverages existing mass spectrometry software to create data analysis workflows that allow researchers to query the chemical space of vastly different biological systems, from small molecules native to the cheese rind microbiome to proteins derived from the vaginal microenvironment. First, untargeted metabolomics profiling was used in the cheese rind microbiome in an effort to gain a high level understanding of bacterial-fungal interactions (BFIs) native to this model system. Understanding microbial interactions is essential for intelligently designing synthetic communities to study microbiomes, which in turn can lead to discoveries from human microbiomes that can positively contribute to knowledge of human health. In the cheese rind microbiome, fungi were found to be the major chemical drivers of microbial interactions, and one specific fungal-fungal interaction was scrutinized to attempt to identify the source of selective antifungal bioactivity. Furthermore, preliminary work was done to begin assessment of novel acquisition methods utilizing ion mobility-mass spectrometry. Next, a workflow was developed leveraging mass spectrometry based bottom-up proteomics to annotate putative biomarkers for early-stage ovarian cancer. High-grade serous ovarian cancer has one of the highest rates of cancer related death among women. Cystatin A was identified as a promising hit from a murine xenograft model transfected with OVCAR-8- RFP cells. However, detection of cystatin A from a dilute, complex biofluid proved to be difficult; therefore, a label free detection method called frequency-locked optical whispering evanescent resonator (FLOWER) utilized a cystatin A - Ab functionalized to the surface of a microtoroid resonator, which allowed detection of cystatin A as low as 100 pM. Finally, due to the amount and complexity of multidimensional mass spectrometry data in both metabolomics and proteomics datasets, software such as TIMSCONVERT, BLANKA2, and pyMALDIproc were developed with the goal of being easily integrated into data analysis pipelines utilizing existing software including but not limited to Global Natural Products Social molecular networking, Cardinal MSI, MZmine2, XCMS, MaxQuant, and more. These software also allow researchers to handle newly incorporated data types such as collisional cross section values obtained from ion mobility-mass spectrometry. Overall, the work described in this thesis serves to 1) further the understanding of select biological systems described above and 2) provide researchers with the tools to conduct their own investigations.

Ovarian cancer (OvC) is the most lethal gynecologic malignancy, with high-grade serous ovarian cancer (HGSOC) being the most common and deadly subtype. HGSOC is characterized by an aggressive metastatic pattern, originating in the fallopian tube where fallopian tube epithelial (FTE) cells transform and metastasize first to the ovary and then to the omentum. This metastatic behavior suggests that the ovary and omentum are preferred sites for HGSOC metastasis, though the reasons for this preference are not well understood and are likely influenced by metabolite exchange between OvC cells and healthy tissues.Mass spectrometry imaging (MSI) is a powerful technique for visualizing the spatial distribution of small molecules within biological samples. Dr. Katherine Zink, a previous graduate student, developed an innovative MSI protocol for evaluating 3D cocultures of tumorigenic FTE cells and murine explant tissues. Using this protocol, Dr. Zink identified norepinephrine (NE), a chemoattractant secreted from the ovary during primary metastasis. This thesis builds on her work by further investigating NE’s role in metastasis and adapting the MSI protocol for use with omental tissue to study secondary metastasis. MSI and quantitative liquid chromatography-mass spectrometry (LC-MS) were used to investigate chemical exchanges in the metastasis of HGSOC to both the ovary and omentum. An LC-MS based assay for quantifying NE was developed to assess NE secretion levels in ovarian cocultures with various OvC cell lines, aiming to relate NE production to cancer-specific pathway modifications or survival times in murine models. Although NE production varied in ovarian cocultures and did not consistently correlate with specific genetic modifications or survival times, data suggested that TP53 mutations might be associated with increased NE production and responsiveness to NE signaling. The quantitative assay also assessed NE levels in human follicular fluid (FF) samples spiked into cultures of human tumorigenic FTE cells, indicating a possible correlation between NE levels and the development of anoikis resistance in human tumorigenic FTE cells. During this dissertation, our lab transitioned from using a Bruker AutofleX MALDI-TOF mass spectrometer (MS) to a Bruker timsTOF fleX MS for MSI analysis. The new instrument's sensitivity to height differences on coculture sample surfaces necessitated adapting the sample preparation protocol. An automated spinner for sample desiccation was developed, improving MSI data quality and protocol robustness. After optimization, the MSI protocol identified the FTE secreted factor that stimulates NE secretion from the ovary during primary metastasis. Additionally, the MSI protocol was adapted for use with omental tissue to study secondary metastasis, revealing several upregulated signals in OvC cell/omentum cocultures. Increased BCAA catabolism was observed at the interface of OvC cells and omental tissue in coculture, and ion suppression was found to impact MSI data. Overall, this thesis enhances our understanding of metabolic exchanges and their role in the metastatic progression of HGSOC, providing insights that could inform future research and therapeutic strategies.

Cancer is the second leading cause of death in the United States, with many of those deaths attributed to lung cancer. NSCLC accounts for nearly 85% of all lung cancer cases, making NSCLC a leading cause of cancer-related death in the United States. A chemistry-driven de novo discovery strategy utilizing cheminformatics recently identified ikarugamycin (IKA) as a potent and selective inhibitor of cellular proliferation amongst NSCLC cell lines. However, a detailed characterization of IKA and several analogs has yet to be performed within the context of NSCLC. This work aimed to further investigate the relationship between IKA analogs and the effect that structural diversity may have on the potency and selectivity of its’ antiproliferative properties concerning NSCLC. The chemical characterization and biological cytotoxicity profiling of IKA and its’ analogs against several NSCLC cell lines will drive the development of IKA towards clinical relevance. Biological evaluation of several IKA analogs revealed that the double bond within the 5-6-5 ring moiety is crucial to selective antiproliferative activity against NSCLC HCC44, H23, and Calu-1. All selective analogs exhibited an IC50 value within the 0.09-1.00 M range. Along with the strong toxicity trends we have already observed, IKA also appears to be interacting with a novel biological target outside of the commonly acted on genes (i.e., EGFR, ALK, BRAF, ROS1). This work also explored strategies to embed synthetic handles for future click pull-down experiments for target identification.Concurrently, this thesis contains work on the utilization of the NP Atlas as a compound repository for virtual drug screening – a first of its kind. To address the emerging viral epidemic of SARS-CoV-2, I employed the open-source Chemprop algorithm to train a Directed-Message Passing Neural Network (DMPNN) to identify key chemical descriptors that can be attributed to disrupting the viral replication mechanism. I utilized open-source screening data provided by the National Center for Biotechnology Information (NCBI) to train the DMPNN and subsequently utilized the trained neural network to score compounds found within the NP Atlas. Through this process, I was able to identify and validate the targeted interaction between closthioamide and the main protease of SARS-CoV-2. This work serves as a proof of principle for adjacent computational drug discovery strategies that may help scientist prioritize their efforts and lower the cost of resources necessary to screen libraries that are greater than 24,000 molecules.