Human enzyme-mediated, systemic depletion of methionine for glioblastoma treatment

Glioblastoma multiforme (GBM) is the most lethal and common type of malignant brain tumor in adults. To date, no curative treatment exists for GBM despite continuous research efforts. Like many other cancers, GBM requires higher levels of methionine for survival compared with normal cells. We aim to exploit GBM methionine dependency as a therapeutic target for this lethal cancer. Our results showed that methionine depletion with an engineered human methionine-γ-lyase (hMGL) reduced GBM cell survival in vitro. Metabolic profiling and MSEA revealed that aminoacyl tRNA biosynthesis, glutathione metabolism, and nucleotide metabolism were significantly changed by hMGL treatment. Mechanistic study showed hMGL treatment resulted in notable increases in oxidative stress in GBM cell lines, leading to DNA damage, and caused cell cycle arrest at the S/G2 phase. In line with this, thioredoxin reductase inhibitor, auranofin, and ATR inhibitor, ceralasertib, showed synergistic effects with hMGL in inhibiting GBM cells. Furthermore, hMGL treatment caused a decrease of global DNA methylation and altered histone methylation patterns. This upregulated the expression of tumor-suppressive microRNAs miR-124 and miR-137, which are frequently silenced in gliomagenesis due to aberrant DNA methylation. Accordingly, hMGL inhibited the phosphorylation and activation of their downstream target, STAT3, a central mediator of GBM growth. Finally, hMGL inhibited the growth of orthotopic human GBM xenografts in vivo and prolonged survival time of tumor-bearing animals. Our data provides strong rationale to investigate the efficacy of hMGL in the treatment for GBM.Cellular and Molecular Biolog

Improving surveillance and prediction of emerging and re-emerging infectious diseases

Infectious diseases are emerging at an unprecedent rate in recent years, such as the flu pandemic initialized from Mexico in 2009, the 2014 Ebola epidemic in West Africa, and the 2016-2017 expansion of Zika across Americas. They rarely happened previously and thus lack resources and data to detect and predict their spread. This highlights the challenges in emerging an re-emerging infectious disease surveillance. In the dissertation, I mainly put efforts in developing methods for early detection of such diseases, and assessing predictive power of various models in early phase of an epidemic. In Chapter 2, I developed a two-layer early detection framework which provides early warning of emerging epidemics based on the idea of anomaly detection. The framework could evaluate and identify data sources to achieve the best performance automatically from available data, such as data from the Internet and public health surveillance systems. I demonstrated the framework using historical influenza data in the US, and found that the optimal combination of predictors includes data sources from Google search query and Wikipedia page view. The optimized system is able to detect the onset of seasonal influenza outbreaks an average of 16.4 weeks in advance, and the second wave of the 2009 flu pandemic 5 weeks ahead. In Chapter 3, I extended the framework in Chapter 2 to identify large dengue outbreaks from small ones. The results show that the framework could personalize optimal combinations of predictors for different locations, and an optimal combination for one location might not perform well for other locations. In Chapter 4, I investigated the contribution of different population structures to total epidemic incidence, peak intensity and timing, and also explored the ability of various models with different population structures in predicting epidemic dynamics. The results suggest that heterogeneous contact pattern and direct contacts dominate the evolution of epidemics, and a homogeneous model is not able to provide reliable prediction for an epidemic. In summary, my dissertation not only provides method frameworks for building early detection systems for emerging and re-emerging infectious diseases, but also gives insight to the effects of various models in predicting epidemics.Cellular and Molecular Biolog

Combined high-pressure and multiquantum NMR and molecular simulation propose a role for N-terminal salt bridges in amyloid-beta

Salts, Aggregation, Molecular structure, Cell and molecular biology, Post-translational modificatio

Evidence of Polycomb-independent Catalysis of Histone H3 Lysine 27 Trimethylation

Honors (Bachelor's)Cell and Molecular Biology (CMB)University of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/120582/1/loriad.pd

Toward Establishing the Role of FGFs in Differentiation of Human Stem Cells for Otic Specification

Honors (Bachelor's)Cell and Molecular Biology (CMB)University of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/112149/1/sanvarma.pd

Myosin Binding Protein C Mutation Locus and Type Affect Hypertrophic Cardiomyopathy Disease Mechanism

Honors (Bachelor's)Cell and Molecular Biology (CMB)University of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/147361/1/nhafeez.pd

Molecular Characterization of Mouse Models of High Grade Serous Carcinoma Based on Oviductal Epithelial Transformation

Honors (Bachelor's)Cell and Molecular Biology (CMB)University of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/147391/1/stephne.pd

Nitric Oxide Signal Stimulated by the Calreticulin and Shared Epitope Interaction

Honors (Bachelor's)Cell and Molecular Biology (CMB)University of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/112135/1/mjhannah.pd

Catecholamines induce a regulatory macrophage phenotype and confer protection during acute lung injury and endotoxemia via activation of the beta-2 adrenergic receptor

Honors (Bachelor's)Cell and Molecular Biology (CMB)University of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/134720/1/haggamd.pd