Infection from Mycobacterium tuberculosis is the leading cause of death due to infectious disease worldwide, with rates of tuberculosis infection greatest in low and middle-income countries (LMICs). Tuberculous meningitis (TBM) is the most severe form of M. tuberculosis disease with nearly half of all cases resulting in death or neurological consequences. Recent studies in our lab have found that single-nucleotide polymorphisms (SNPs) in MUC5AC, a secretory lung mucin, are associated with increased TBM susceptibility, morbidity, and mortality. The purpose of my study is to identify the functional MUC5AC SNP. Four candidate SNPs were selected within the MUC5AC promoter region based on high linkage-disequilibrium scores across multiple global populations with a SNP in the MUC5AC promoter, rs28737416. I utilized molecular cloning techniques to combine a luciferase-expressing plasmid with isolated regions of the human MUC5AC promoter containing the SNPs of interest and subsequently transformed this recombinant plasmid into competent cells. Next, I performed site-directed mutagenesis at the SNPs of interest and am currently transfecting mutants into HEK293T cells to investigate how genotypic variation in each candidate SNP influences promoter function by measuring luciferase expression. I anticipate variants in at least one SNP of interest will reduce gene expression (measured by luciferase expression), indicating functionality. Characterization of this genetic mutation will provide insight into TBM susceptibility across populations and could inform studies of novel therapeutics to treat TBM.


Tuberculosis is the leading cause of infectious disease death globally, but understanding of the host factors that contribute to immune control are incompletely understood. Mtb-infected macrophages, the primary host cell defense cell and replicative niche of Mycobacterium tuberculosis (Mtb), become lipid-laden, or "foamy," which promotes Mtb growth. Toll-interacting protein (TOLLIP) is a tuberculosis-resistant protein that metabolizes lipids in other cell types. Our study objective was to determine whether TOLLIP was responsible for preventing lipid accumulation and impairing host defense in macrophages. I harvested alveolar macrophages of B6 and Tollip^-/- mice via bronchoalveolar lavage and measured lipid accumulation, cell death, and cytokine production. Macrophages were incubated with the Mtb cell wall lipid mycolic acid and labeled with a fluorescent neutral lipid stain to measure intracellular lipid content. Mycolic acid was also instilled intratracheally into the lungs of mice and lipid content in lung-resident macrophages was measured using flow cytometry. Since lipid accumulation can induce cell death, cellular apoptosis and necrosis were measured via Annexin V and membrane permeability staining. Cytokine responses were determined via enzyme-linked immunosorbent assay (ELISA), and data was gathered through a standard curve. Statistical significance was determined using a two-sided t-test, with a threshold p-value of less than 0.05. Mycolic acid-stimulated Tollip^-/- macrophages developed increased lipid accumulation and cell death in a dose-dependent fashion. Tollip^-/- alveolar macrophages selectively accumulated more lipids than B6 mice (mean B6 0.45%, mean Tollip^-/- 0.68%). Tollip^-/- macrophages secreted increased cytokines (mean B6 131pg/mLvs Tollip^-/- 233 pg/mL, p=0.05) after mycolic acid stimulation. We conclude that Tollip^-/- macrophages accumulate excess lipids and produce more cytokines, which may contribute to its role in Mtb host defense in macrophages. These results suggest that excess cytokine production leads to worsened defense mechanisms and increased lipid production induces macrophage immune response. 

Alzheimer's disease (AD) is the most common type of dementia with an estimated 1 in 85 people affected worldwide. It is characterized by accumulations of amyloid beta which result in difficulties with memory and other cognitive activities. While there are treatments that show promise, these are only effective in the preclinical phase of the disease where there are no symptoms, which take approximately 20 years to appear. Unfortunately, measuring amyloid beta directly is invasive and costly, so functional magnetic resonance imaging (fMRI) is used to detect changes in the brain caused by amyloid beta accumulation. Moreover, established techniques for using fMRI for detecting AD show limited success. Therefore, the goal of my project is to generate biomarkers of the earliest neural communication changes associated with AD to allow the application of treatment when it is most effective. This will be done through the use of a graph diffusion autoregressive (GDAR) model, recently developed in my lab. The GDAR model is capable of estimating dynamic changes of neural communication, but has only been applied to electrical recordings. I have demonstrated its validity for fMRI data using a healthy control group through analyzing the model’s prediction capabilities and test-retest consistency, which are both strong. Furthermore, I correlated the GDAR model’s outputs with the primary method in the field, functional connectivity analysis, showing only loose correlation and the potential for biomarkers that are novel from what is currently known. Currently, I am applying the model to AD patients, and anticipate its outputs to contrast those of healthy patients through the use of unsupervised clustering techniques. This would allow the model’s outputs to be used as biomarkers for AD and improve the feasibility of its treatment and prevention.