ASU Scholarship Showcase

Attempts to prepare low-valent molybdenum complexes that feature a pentadentate 2,6-bis(imino)pyridine (or pyridine diimine, PDI) chelate allowed for the isolation of two different products. Refluxing Mo(CO)₆ with the pyridine-substituted PDI ligand, ^PyEtPDI, resulted in carbonyl ligand substitution and formation of the respective bis(ligand) compound (^PyEtPDI)₂Mo (1). This complex was investigated by single-crystal X-ray diffraction, and density functional theory calculations indicated that 1 possesses a Mo(0) center that back-bonds into the π*-orbitals of the unreduced PDI ligands. Heating an equimolar solution of Mo(CO)[subscript 6] and the phosphine-substituted PDI ligand, ^Ph2PPrPDI, to 120 °C allowed for the preparation of (^Ph2PPrPDI)Mo(CO) (2), which is supported by a κ5-N,N,N,P,P-^Ph2PPrPDI chelate. Notably, 1 and 2 have been found to catalyze the hydrosilylation of benzaldehyde at 90 °C, and the optimization of 2-catalyzed aldehyde hydrosilylation at this temperature afforded turnover frequencies of up to 330 h^–1. Considering additional experimental observations, the potential mechanism of 2-mediated carbonyl hydrosilylation is discussed.

A brief review of manganese-catalyzed hydrosilylation is presented along with a personal account of how the design for the highly active catalyst, (^Ph2PPrPDI)Mn, was conceived. The reductive transformations achieved using this catalyst are described and put into further context by comparing the observed activities with those attained for leading late first-row transition-metal catalysts.

Background: Androgens bind to the androgen receptor (AR) in prostate cells and are essential survival factors for healthy prostate epithelium. Most untreated prostate cancers retain some dependence upon the AR and respond, at least transiently, to androgen ablation therapy. However, the relationship between endogenous androgen levels and cancer etiology is unclear. High levels of androgens have traditionally been viewed as driving abnormal proliferation leading to cancer, but it has also been suggested that low levels of androgen could induce selective pressure for abnormal cells. We formulate a mathematical model of androgen regulated prostate growth to study the effects of abnormal androgen levels on selection for pre-malignant phenotypes in early prostate cancer development.

Results: We find that cell turnover rate increases with decreasing androgen levels, which may increase the rate of mutation and malignant evolution. We model the evolution of a heterogeneous prostate cell population using a continuous state-transition model. Using this model we study selection for AR expression under different androgen levels and find that low androgen environments, caused either by low serum testosterone or by reduced 5α-reductase activity, select more strongly for elevated AR expression than do normal environments. High androgen actually slightly reduces selective pressure for AR upregulation. Moreover, our results suggest that an aberrant androgen environment may delay progression to a malignant phenotype, but result in a more dangerous cancer should one arise.

Conclusions: The model represents a useful initial framework for understanding the role of androgens in prostate cancer etiology, and it suggests that low androgen levels can increase selection for phenotypes resistant to hormonal therapy that may also be more aggressive. Moreover, clinical treatment with 5α-reductase inhibitors such as finasteride may increase the incidence of therapy resistant cancers.

Most current approaches for quantification of RNA species in their natural spatial contexts in single cells are limited by a small number of parallel analyses. Here we report a strategy to dramatically increase the multiplexing capacity for RNA analysis in single cells in situ. In this method, transcripts are detected by fluorescence in situ hybridization (FISH). After imaging and data storage, the fluorescence signal is efficiently removed by photobleaching. This enables the reinitiation of FISH to detect other RNA species in the same cell. Through reiterative cycles of hybridization, imaging and photobleaching, the identities, positions and copy numbers of a large number of varied RNA species can be quantified in individual cells in situ. Using this approach, we analyzed seven different transcripts in single HeLa cells with five reiterative RNA FISH cycles. This approach has the potential to detect over 100 varied RNA species in single cells in situ, which will have wide applications in studies of systems biology, molecular diagnosis and targeted therapies.

We described the rapid production of the domain III (DIII) of the envelope (E) protein in plants as a vaccine candidate for West Nile Virus (WNV). Using various combinations of vector modules of a deconstructed viral vector expression system, DIII was produced in three subcellular compartments in leaves of Nicotiana benthamiana by transient expression. DIII expressed at much higher levels when targeted to the endoplasmic reticulum (ER) than that targeted to the chloroplast or the cytosol, with accumulation level up to 73 μg DIII per gram of leaf fresh weight within 4 days after infiltration. Plant ER-derived DIII was soluble and readily purified to > 95% homogeneity without the time-consuming process of denaturing and refolding. Further analysis revealed that plant-produced DIII was processed properly and demonstrated specific binding to an anti-DIII monoclonal antibody that recognizes a conformational epitope. Furthermore, subcutaneous immunization of mice with 5 and 25 μg of purified DIII elicited a potent systemic response. This study provided the proof of principle for rapidly producing immunogenic vaccine candidates against WNV in plants with low cost and scalability.

The increasing world demand for human biologics cannot be met by current production platforms based primarily on mammalian cell culture due to prohibitive cost and limited scalability [1]. Recent progress in plant expression vector development, downstream processing, and glycoengineering has established plants as a superior alternative to biologic production [2–4]. Plants not only offer the traditional advantages of proper eukaryotic protein modification, potential low cost, high scalability, and increased safety but also allow the production of biologics at unprecedented speed to control potential pandemics or with specific glycoforms for better efficacy or safety (biobetters) [5, 6]. The approval of the first plant-made biologic (PMB) by the United States Food and Drug Administration (FDA) for treating Gaucher’s disease heralds a new era for PMBs and sparks new innovations in this field [7, 8].

The notable increase in biofuel usage by the road transportation sector in Brazil during recent years has significantly altered the vehicular fuel composition. Consequently, many uncertainties are currently found in particulate matter vehicular emission profiles. In an effort to better characterise the emitted particulate matter, measurements of aerosol physical and chemical properties were undertaken inside two tunnels located in the São Paulo Metropolitan Area (SPMA). The tunnels show very distinct fleet profiles: in the Jânio Quadros (JQ) tunnel, the vast majority of the circulating fleet are light duty vehicles (LDVs), fuelled on average with the same amount of ethanol as gasoline. In the Rodoanel (RA) tunnel, the particulate emission is dominated by heavy duty vehicles (HDVs) fuelled with diesel (5% biodiesel). In the JQ tunnel, PM_2.5 concentration was on average 52 μg m^-3, with the largest contribution of organic mass (OM, 42%), followed by elemental carbon (EC, 17%) and crustal elements (13%). Sulphate accounted for 7% of PM_2.5 and the sum of other trace elements was 10%. In the RA tunnel, PM_2.5 was on average 233 μg m^-3, mostly composed of EC (52%) and OM (39%). Sulphate, crustal and the trace elements showed a minor contribution with 5%, 1%, and 1%, respectively. The average OC : EC ratio in the JQ tunnel was 1.59 ± 0.09, indicating an important contribution of EC despite the high ethanol fraction in the fuel composition. In the RA tunnel, the OC : EC ratio was 0.49 ± 0.12, consistent with previous measurements of diesel-fuelled HDVs. Besides bulk carbonaceous aerosol measurement, polycyclic aromatic hydrocarbons (PAHs) were quantified. The sum of the PAHs concentration was 56 ± 5 ng m^-3 and 45 ± 9 ng m^-3 in the RA and JQ tunnel, respectively. In the JQ tunnel, benzo(a)pyrene (BaP) ranged from 0.9 to 6.7 ng m^-3 (0.02–0.1‰ of PM_2.5)] whereas in the RA tunnel BaP ranged from 0.9 to 4.9 ng m^-3 (0.004–0. 02‰ of PM_2.5), indicating an important relative contribution of LDVs emission to atmospheric BaP.

About 2.5 × 10⁶ snapshots on microcrystals of photoactive yellow protein (PYP) from a recent serial femtosecond crystallographic (SFX) experiment were reanalyzed to maximum resolution. The resolution is pushed to 1.46 Å, and a PYP structural model is refined at that resolution. The result is compared to other PYP models determined at atomic resolution around 1 Å and better at the synchrotron. By comparing subtleties such as individual isotropic temperature factors and hydrogen bond lengths, we were able to assess the quality of the SFX data at that resolution. We also show that the determination of anisotropic temperature factor ellipsoids starts to become feasible with the SFX data at resolutions better than 1.5 Å.

Predicting the timing of a castrate resistant prostate cancer is critical to lowering medical costs and improving the quality of life of advanced prostate cancer patients. We formulate, compare and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). We accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). While these models are simplifications of a previously published model, they fit data with similar accuracy and improve forecasting results. Both models describe the progression of androgen resistance. Although Model 1 is simpler than the more realistic Model 2, it can fit clinical data to a greater precision. However, we found that Model 2 can forecast future PSA levels more accurately. These findings suggest that including more realistic mechanisms of androgen dynamics in a two population model may help androgen resistance timing prediction.

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.