The effective treatment of individuals with HIV relies on maintaining therapeutic drug concentrations, necessitating accurate measurement of antiretroviral (ARV) drug levels. Current methods, such as liquid chromatography tandem mass spectrometry (LC-MS/MS), are limited by cost and accessibility. Our research addresses this gap by developing the INTEGRase activITY (INTEGRITY) assay for measuring integrase strand transfer inhibitors (INSTIs), a leading class of ARV drugs. This 2-step assay quantifies INSTIs using a DNA strand transfer reaction and quantitative polymerase chain reaction (qPCR). The presence of INSTI drugs disrupts the strand transfer reaction, inhibiting full-length target DNA formation, which is then measured through real-time qPCR. My work focused on optimizing the limit of detection of INTEGRITY by altering the strand transfer reaction conditions and protocol. Specifically, I conducted experiments altering INSTI drug concentrations and optimizing pre-incubation times of integrase with the drug to enhance the LOD. I observed that preliminary incubation of integrase and INSTI drugs for 5 minutes at 37 degrees Celsius improved the LOD of INTEGRITY by an order of magnitude. The simplicity of the INTEGRITY assay, utilizing standard laboratory equipment, holds immense promise for broadening access to routine clinic-based ARV drug level monitoring. This advancement has the potential to significantly enhance HIV care on a global scale by offering a cost-effective and accessible solution for monitoring therapeutic drug concentrations.

The four letters in DNA (ATGC) construct the basis of life as we know it. Unnatural base pairing xenonucleic acids (ubp XNAs) are synthetic nucleic acids that can be used orthogonally to the 4-letter code. XNAs have the potential to revolutionize a myriad of biotechnologies, but commercial sources of XNA nucleotides are limited and expensive. Here, we fill one step of an enzymatic cascade required to sustainably produce XNA nucleotides. Nucleoside phosphorylases (NPs) are enzymes that catalyze the reversible phosphorolysis of nucleosides to their base and sugar components. We purified and assayed promiscuous NPs from two thermophiles, Geobacillus thermoglucosidasius (GtNP) and Thermus thermophilus (TtNP). Using a combination of mass spectrometry and fluorescence assays, we show that these phosphorylases have activity on a subset of three XNA substrates (B, Sⁿ, and P). This enzymatic pathway allows us to synthesize non-standard nucleotides in a cost-efficient manner and provides a crucial tool for the biosynthesis of XNAs.

Hypoxic-Ischemic Encephalopathy (HIE) resulting from a lack of blood and oxygen to the brain is the leading cause of mortality in term newborns. Extracellular vesicles (EVs) serve as critical transporters of biomolecules between cells, with evidence of alleviating inflammation in models after hypoxic ischemia (HI) injury. Therapeutic efficacy of EVs has only been evaluated in males because males are more susceptible to worse outcomes following HIE injury, yet knowledge about EVs and their behavior when administered to females is still needed. In this study, I aimed to address this knowledge gap by systematically comparing the efficacy of male and female neonatal brain-derived EVs (mEVs, fEVs, respectively) applied on male and female neonatal rat ex vivo brain slices. I first confirmed the purity of isolated EVs with protein assays and immunoblots, and utilized an ex vivo oxygen-glucose deprivation (OGD) model of HI injury, applying fEVs and mEVs to sex-matched OGD-exposed brain slices. I evaluated cell viability after 24h of EV exposure, and my results show that fEVs decrease inflammation and cytotoxicity in OGD models. When compared to previous results using mEV treatment, my results suggest that females have a more robust anti-inflammatory response system to injury. Ongoing work to better understand the therapeutic effect of EVs involves further observing morphological shifts in microglia through confocal imaging, as fEV application will likely result in microglia shifting towards anti-inflammatory phenotypes, similar to what was previously observed after mEV application. I am also quantifying expression levels of various inflammatory and reparative genes through reverse transcription quantitative polymerase chain reactions (RT-qPCR). Overall, I have demonstrated in these pilot studies that fEVs have a different therapeutic effect in OGD injury compared to mEVs. This research is intended to open up pathways for more personalized sex-based treatments for various injuries and therapeutics in the future. 

Multiple Particle Tracking (MPT) is a powerful technique for studying the behavior of microscopic particles, such as viruses and nanoparticles, by tracking individual displacement and movement. One application of MPT is to measure microstructural changes in the brain extracellular environment (ECM) in development and aging, and in response to disease onset and progression. MPT of nanoparticle probes results in the generation of thousands of individual nanoparticle trajectories, from which geometric features, diffusion coefficients, and viscosities can be extracted. The vast array of trajectories contained within our dataset presents a good opportunity for integration into deep learning models that contains self-supervised learning, equivariant graph neural network, and Equivariant transformer. However, to enable MPT data to be trainable and predictable by deep learning models, we need to curate the data to be readable and useable by these models. To enable this, I have created a database and developed a data architecture that would allow MPT data to be passed into Deep learning models that use various techniques such as transformers. I am currently working on utilizing the data architecture on a Deep Learning model that uses transformers and self-supervised learning to predict trajectories of MPT particles. From this model, my expected accuracy of prediction of the trajectories for the MPT data is around 85%. This can allow us to learn complex features directly from raw MPT trajectory data, improve our predictions, and extract biological insights. The python package with our data architecture, the various SQL scripts, and the model will be provided as an open-source resource, allowing other researchers to expand upon my code and apply their unique modifications based on their own data and trajectories.

 Injectable biomaterials can be used for targeted, local delivery of drugs and small protein factors. However, designing a biomaterial that is self-healing, bioresorbable, highly-defined, and allows for the independent release of multiple protein drugs remains a challenge. Interpenetrating network (IPN) hydrogels formed by physical crosslinks provide a solution as they combine the properties of two networks into one material, as well as form gels that are injectable and self-healing. Our project aims to produce the first ever reported fully recombinant protein-based IPN hydrogel that can be used to deliver multiple drugs simultaneously. Recombinant protein-based polymers are advantageous for forming biomaterials due to their bioactivity, biocompatibility, and tunability. Each biopolymer contains coiled-coil proteins linked by a flexible hydrophilic linker that is bisected by a click-like chemistry protein system, known as a polyproteam. These polyproteams allow drugs or protein factors to be covalently ligated to the networks, so that drug delivery is dependent on material erosion instead of diffusion. We characterized the material’s properties using rheometry to measure material stiffness, percent strain at which the material liquifies, and self-healing behavior. Then, we tested the ability of our hydrogel to deliver drugs by ligating epidermal growth factor and insulin growth factor to the hydrogel and measured the release of protein over time through a Western Blot. The bioactivity of the growth factors was confirmed by culturing with human mesenchymal stem cells (hMSCs) and using a Western Blot to measure activation of the ERK signaling pathway. If successful, the IPN hydrogel can be used for clinical applications, as a tool for drug delivery.

Current technology to control 3D cell function takes advantage of bioorthogonal photochemistry to immobilize proteins into materials by using photocages—photoremovable molecular groups that block protein activity. However, diffusion limitations necessitate patterning times ranging from hours to weeks, far longer than the timescales of many biological processes. Protein activation aids signaling events within the extracellular matrix (ECM), leading to downstream changes in cell fate and physiological responses in our bodies. In order to probe and investigate biological systems, hydrogel materials provide an ideal synthetic platform, due to their polymeric, water-swollen characteristics that mimic the native ECM. In this project, I will use light to control the spatial and temporal presentation of biochemical cues through the photoactivation of proteins within hydrogels. We hypothesize that the kinetics of the protein activity between solution and biomaterial studies should correlate, given their dose-dependent response to light exposure—by varying the intensities of light and time intervals of exposure. By characterizing the photoactivatable protein system and controlling protein activity, we intend to use this platform to photoactivate biologically relevant proteins to control signaling that occurs on shorter time scales, applicable to biochemical processes.

Bioconjugation, or the covalent linkage between a biomolecule and another chemical group, creates hybrid "conjugates" that exhibit the properties of both biomolecules and exogenous moieties. The N-termini of proteins often fall outside of their final fold, making the N-terminus an optimal site for conjugation while preserving a protein’s native folding and bioactivity. Consequently, N-terminal modification of proteins and peptides has been a long-standing goal in fields like drug delivery, biotherapeutics, and cellular imaging. However, the current techniques for N-terminal protein conjugation are limited by either the introduction of bulky protein assemblies at the conjugation site, the need for multiple costly and complicated steps, or low site selectivity. In this project, we aimed to develop an improved route for N-terminal bioconjugation. We created a generalizable platform for single-step purification and near-scarless N-terminal bioconjugation of proteins by leveraging the chemistry of the atypically split intein VidaL. To evaluate the effectiveness of our platform, we first examined the kinetics and reaction conditions of VidaL bioconjugation, confirming its ability to modify the N-termini of proteins successfully and selectively. Then, we used our platform to conjugate an alkyne, biotin, or FAM-biotin moiety to the N-termini of fluorescent proteins (EGFP and mCherry), a model enzyme (beta-lactamase), and a model growth factor (EGF). Through measuring fluorescence and conducting nitrocefin and proliferation assays, I found that, regardless of the moiety added, bioconjugation did not impact the native function or activity of these proteins. In the future, we expect that this platform's ability to easily N-terminally bioconjugate proteins with minimal impact on their functionality will find use across the growing fields of applied chemical biology.

Biological processes depend on the intricate functions of proteins, which serve as the driving force in vital reactions. Given the importance of proteins in biological processes, several methods have been developed to control their functions in biomaterial and in vitro. Current methods often rely on secondary fixatives and caustic chemicals, posing risks of functional impairment in target proteins. To address these limitations, my project focuses on modifying cells with non-endogenous machinery, enabling expression of photoactivatable proteins – those with photocaged amino acids installed at essential locations, and using them to optically stimulate protein function. This strategy enables the triggered activation of target proteins with light, mitigating the risks associated with chemical interventions. We predict that the degree of protein activation can be controlled in a dose-dependent manner by manipulating light exposure duration and intensity. Genetic code expansion is used to integrate noncanonical amino acids, site specifically, into proteins expressed in bacteria and mammalian cells. By incorporating these photoactivatable proteins into biomaterials and living cells, our research aims to gain insights into fundamental cell signaling processes through control of targeted proteins. By analyzing biological mechanisms at the molecular level, we can guide cell processes and make informed decisions regarding cellular behavior and fates.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, is one of the deadliest pathogens in human history. The aim of this project is to begin probing the metabolic reprogramming that occurs in Mtb during treatment with isoniazid (INH), a commonly used frontline drug. Unpublished work from a collaborator has identified several common metabolites that change the minimum inhibitory concentration of INH needed to kill 99% of cells in a culture. The amino acid serine -- which is well known to be involved in one-carbon metabolism through interconversion with glycine -- was found to be of particular interest due to the increase in Mtb susceptibility to INH observed in serine-supplemented culture. To further explore this effect, I have generated timecourse growth curves using an avirulent Mtb (aMtb) model while under INH treatment with and without serine. I started cultures of aMtb at log-phase growth under the following conditions: without any additives, with serine, with INH, and with both INH and serine. I then plated aliquots of defined volumes on solid media to observe colony forming units (CFU) that assess how many cells were present in each culture. I repeated CFU plating 4 and 7 days after each culture was created, which gave me sufficient data to analyze the growth patterns of aMtb under these various conditions. My findings require validation but suggest serine supplementation may play a role in lowering INH tolerance in Mtb. If this result is found to be accurate, this may implicate one-carbon metabolism as a pathway with downstream effects on Mtb tolerance to INH.

Protein kinases have been found to regulate cell processes such as cell growth and adaptive responses to the environment. Thus, they act as excellent targets for tuberculosis treatment. The Mycobacterium tuberculosis (Mtb) genome encodes 11 separate serine/threonine protein kinases (STPKs). One of those STPKs is PknF, which our lab has previously found to be associated with a survival deficit in Mtb. Since Mtb utilizes multiple carbon sources during its infection in vivo, my research project aims to test the effect of perturbing pknF expression levels on Mtb survival in different carbon source conditions. I grew an avirulent Mycobacterium tuberculosis (aMtb) strain that conditionally overexpresses pknF upon exposure to anhydrotetracycline (pknF OE) in different media formulations that provided different carbon sources: glycerol and acetate. I measured colony-forming units (CFU) to quantify aMtb survival in these different growth conditions. I compared survival measurements of the pknF OE strain relative to an empty vector control strain. I found that the pknF OE strain showed a survival deficit by day 7 in both media conditions, with glycerol showing a higher survival deficit compared to acetate. The extent of the survival deficit seems to be affected by which carbon source is provided by the media. Previous literature has linked pknF to the regulation of glucose transport and phosphorylation of glucose catabolism-associated enzymes in the citric acid cycle. Based on this, we hypothesize that these processes may be involved with pknF's observed survival deficit in different media. In follow-up experiments, we plan to investigate how perturbing glucose transport or catabolism influences pknF's survival deficit. These findings can be used to inform future studies on pknF as a potential target for tuberculosis treatment during infection. 

Hypoxic ischemia (HI), the loss of blood and oxygen to the brain, is a common cause of neurological impairment and mortality. Astrocytes are one cell type that responds to acute trauma like HI and modulate the vascular-brain interface via their role in maintaining the blood-brain barrier (BBB). Therefore, astrocytes can be a potential therapeutic target; however, to screen methods to target astrocytes, there is still more to be discovered about astrocyte behavior in response to different stimuli. Towards this goal, this project aims to (1) create a robust and detailed characterization of cultured astrocytes over time, (2) evaluate astrocyte changes to different culturing conditions, and (3) measure the uptake of polymer nanoparticles, commonly used as drug delivery systems, on stimuli exposed astrocytes. My results show that after isolation, astrocytes are initially evenly distributed and form snowflake clusters, collectively assuming a star-shaped morphology. Over time, individual astrocytes move away from clusters and independently adopt the characteristic star shape. This suggests a dynamic process wherein astrocytes exhibit both collective and individual behaviors, contributing to the intricate architecture of astrocyte growth. Additionally, changes in the ratio of glial cells were observed. While microglia decreased in number and became less branched over time, oligodendrocyte populations remained relatively stable over time. Neurons that were initially sparse in the population decreased rapidly over time. Current studies involve the application of polymer nanoparticles to oxygen-glucose deprivation (OGD)-exposed cells immediately after OGD to evaluate uptake via imaging co-localization of particles with cells; OGD exposure induces HI injury in vitro. I have confirmed astrocyte response to OGD by analyzing cell morphology using imaging and cell viability assessments. These studies will establish an in vitro astrocyte model of HI and enable future studies incorporating additional BBB cells and other therapeutic platforms.

Industrial chemical separation processes, such as distillation, drying, and evaporation, consume 10-15% of the world’s annual energy production. Membranes, which act as a selective barrier to separate compounds, are substantially more energy efficient than traditional chemical separation methods that require heat. Inorganic membranes are inherently suitable for many separation processes because they are chemically and thermally stable; however, ceramic membranes are mechanically fragile and costly to produce, which limits their usefulness. Commercial polymeric membranes are economical compared to inorganic membranes but degrade in harsh organic solvents and high-temperature environments. To reduce the cost of chemical separations, methods to improve the chemical and thermal stability of polymeric membranes are needed. Vapor phase infiltration (VPI), a gas-phase synthesizing technique, is one possible strategy for improving membranes, consisting of sorption, diffusion, and entrapment of vapor-phase inorganic reactants within organic polymers. The infiltration of inorganics through VPI has been shown to enhance the properties of polymeric membranes by producing cost-effective, chemically stable, and temperature-tolerant hybrid membranes. However, the change in mechanical properties when converting polymeric membranes to hybrid membranes is generally not well understood, and this understanding is crucial for maximizing the lifetime and durability of organic-inorganic membranes. In this study, polyethersulfone (PES) membranes are subjected to trimethylaluminum and water under various VPI process conditions in a custom-built reactor. Thermogravimetric analysis is utilized to quantify the extent of inorganic infiltration by measuring the aluminum oxide loading within PES membranes. The mechanical properties of these hybrid membranes after VPI are characterized for their tensile stress, modulus, and maximum pressure through dynamic mechanical analysis and burst pressure testing. Ongoing research to establish the physical properties of organic membranes with inorganic nanostructures is discussed. These results help optimize the trade-off between membrane stability and mechanical properties, which may allow many industrial chemical operations to become more sustainable.

In the US, there is an average of 69,500 Traumatic Brain Injury (TBI) related deaths, 223,135 TBI-related hospitalizations, 326,600 inpatient stays, and 801,700 Emergency Department visits per year. The Centers for Disease Control report the annual cost of treating non-fatal TBIs to be over $40B. Currently, there is no pharmacological treatment for TBI, and 138 clinical treatment trials were completed since 2004 with a 100% failure rate. A rigorous screening model in vitro is needed to increase the probability of successful clinical trials. TBI is complex with many possible modalities of injury. The primary insult to brain tissue may result from compression or shear stress and strain, followed by swelling that compounds into the secondary insult. The cascade of TBI causes additional neuronal death and dysfunction to complicate injury and treatment further. The range of unknown potential injury to the brain during a TBI makes a single TBI model too simplistic to represent the full extent of injury accurately. I have developed a set of living-tissue organotypic whole hemisphere (OWH) brain slice models to mimic compressive damage with a whole slice and novel partial slice compression. The models simulate mild, moderate, and major TBI representing primary and secondary insult inflammation and cytotoxicity propagation across multiple brain regions. Future work will model shear strain damage and the neurochemical response to injury. This set of robust models will be used to screen treatments for TBI before in vivo and clinical trials to study how the compounds affect damaged tissues at a cellular and molecular level.

Bacterial metabolic engineering holds great promise for applications in medicinal, industrial, and climate technologies. A key element of metabolic engineering is the integration of non-native genes and pathways into microorganisms. However, the current state of technology is inefficient and time-intensive. Large cargo sizes of above 2-4 kilobases (kb) reduce integration efficiency, preventing entire metabolic pathways from being integrated into an organism at once. To maintain large heterologous genes and pathways in an organism’s genome, a seamless method for genomic integrations is necessary. A recent breakthrough in genetic engineering uses transposase enzymes and clustered regularly interspaced short palindromic repeat (CRISPR) machinery for more efficient and generalizable genomic integrations. Guided by RNA elements, this genomic integration system improves target site specificity and selection, as well as multiplexing capability (the direct insertion of genes at multiple genomic sites simultaneously). This system is expected to handle cargo insertions of around 10kb, meaning entire metabolic pathways can be implemented into a genome. My research aims to utilize this tool to demonstrate metabolic pathway integrations in non-model organisms and multiplexed knockouts for improved organism engineering. I plan to insert a fluorescent protein in 3 different industrially relevant organisms to demonstrate the generalizability of this genetic engineering toolkit. Additionally, I intend to establish multiplexing capability in multiple organisms by integrating the same genetic cargo at multiple sites using an array of guide RNAs, and determined results using polymerase chain reaction, gel electrophoresis, and DNA sequencing. Finally, using analytical methods such as liquid chromatography-mass spectrometry, I will measure the metabolic effects from integration of complete pathways. I will present the results of ongoing progress for all of the outlined tasks. Overall, my research on CRISPR RNA-guided transposases will enable the targeted, efficient integration of novel genes and pathways in bacteria, leading to significant advancements in therapeutics, biomanufacturing, and sustainable chemical conversion.

Climate change caused by CO2 emissions creates a need for greater energy efficiency, as energy production produces CO2. One area of improvement is in chemical separations, which consume roughly half of all industrial energy use. Most of these processes could be made ten times more efficient by switching from energy-intensive distillation and absorption processes to membrane separations. Mixed Matrix Membranes (MMMs) are a particularly promising option for use in gas separations. These MMMs incorporate Metal Organic Framework (MOF) crystallites within polymer membranes to balance the benefits of both. However, the parameters that determine the growth of some MOFs, like ZIF-8, are still unknown. Here, we explore the growth conditions of ZIF-8 to enable the production of these MMMs on an industrial scale. By exposing zinc oxide (ZnO) coated silicon wafers to 2-methylimidazole (HmIm) in a tube furnace, I measure ZIF-8 crystal formation at varying temperatures, temperature gradients, and anneal times. Crystal thickness is measured using ellipsometry to observe successful crystal formation. Using COMSOL software, we explore models of the HmIm concentration to determine what experimental variables could have the largest effect on ZnO to ZIF-8 conversion. Preliminary experimental results suggest a specific temperature profile is needed, as well as a required minimum anneal time for successful ZnO to ZIF-8 conversion. Furthermore, temperature gradients appear to impact HmIm concentration, which affects crystal growth. These findings may enable industrial-scale production methods for MMMs with ZIF-8, and may also prove useful when producing MMMs with other MOFs.

Hypoxic-ischemic encephalopathy (HIE), resulting from loss of oxygen and blood flow to the brain, remains a leading cause of death and disability in infants with no cure. Following the onset of HIE, inflammation and oxidative stress can drive ongoing injury in the newborn brain. Microglial and neuronal cell populations are promising therapeutic targets. However, drug delivery to the brain and into disease-mediating cells remains a challenge. Our prior work has demonstrated the ability of poly(ethylene glycol)-poly(lactic-co-glycolic acid) (PEG-PLGA) nanotherapeutics to overcome biological barriers in the brain. PLGA-PEG nanoparticles formulated with polysorbate 80 (P80) can further localize to microglia and neurons after systemic administration. We aimed to develop PLGA-PEG nanoparticles for cell-specific delivery of N-acetylcysteine (NAC), an anti-inflammatory agent, in neonatal hypoxia-ischemia (HI). We first varied formulation parameters to optimize a NAC-loaded PLGA-PEG/P80 nanoparticle platform. PLGA-PEG composition and surface-active agent concentration tuned particle size distribution, surface charge, and encapsulation efficiency characterized by dynamic light scattering and high-performance liquid chromatography. Leveraging an ex vivo rat brain slice model of neonatal HI, we investigated cellular uptake of fluorescently labeled nanoparticles. We observed particle localization in microglia and neurons, demonstrating cell-targeting ability following topical application to brain tissue. This work informs optimal particle design for delivering a therapeutically relevant dose of NAC, which is currently limited in clinical application due to unfavorable pharmacokinetic properties. Future experiments could apply the NAC nanoparticle platform using in vivo models to evaluate therapeutic potential for newborns with HIE.