Thioredoxin and Jab1 control estrogen- and antiestrogen-mediated progression of the cell cycle through p27 interactions

A major problem with breast cancer treatment is the prevalence of antiestrogen resistance, be it de novo or acquired after continued use. Many of the underlying mechanisms of antiestrogen resistance are not clear, although estrogen receptor-mediated actions have been identified as a pathway that is blocked by antiestrogens. Selective estrogen receptor modulators (SERMs), such as tamoxifen, are capable of producing reactive oxygen species (ROS) through metabolic activation, and these ROS, at high levels, can induce irreversible growth arrest that is similar to the growth arrest incurred by SERMs. This suggests that SERM-mediated growth arrest may also be through ROS accumulation. Breast cancer receiving long-term antiestrogen treatment appears to adapt to this increased, persistent level of ROS. This, in turn, leads to the disruption of reversible redox signaling that involves redox-sensitive phosphatases and protein kinases and transcription factors. This has downstream consequences for apoptosis, cell cycle progression, and cell metabolism. For this dissertation, we explored if altering the ROS formed by tamoxifen also alters sensitivity of the drug in resistant cells. We explored an association with a thioredoxin/Jab1/p27 pathway, and a possible role of dysregulation of thioredoxin-mediated redox regulation contributing to the development of antiestrogen resistance in breast cancer. We used standard laboratory techniques to perform proteomic assays that showed cell proliferation, protein concentrations, redox states, and protein-protein interactions. We found that increasing thioredoxin reductase levels, and thus increasing the amount of reduced thioredoxin, increased tamoxifen sensitivity in previously resistant cells, as well as altered estrogen and tamoxifen-induced ROS. We also found that decreasing levels of Jab1 protein also increased tamoxifen sensitivity, and that the downstream effects showed a decrease p27 phosphorylation in both cases. We conclude that the chronic use of tamoxifen can lead to an increase in ROS that alters cell signaling and causing cell growth in the presence of tamoxifen, and that this resistant cell growth can be reversed with an alteration to the thioredoxin/Jab1 pathway.

An investigation of the molecular interactions between statins and bacterial pathogens, and their combined impact on the human immune system

Statins are a class of drug that inhibits cholesterol biosynthesis, and are used to treat patients with high serum cholesterol levels. They exert this function by competitively binding to the enzyme 3-hydroxy-3-methylglutaryl-CoenzymeA reductase (HMGR), which catalyses the formation of mevalonate, a rate-limiting step in cholesterol biosynthesis. In addition, statins have what are called “pleiotropic effects”, which include the reduction of inflammation, immunomodulation, and antimicrobial effects. Statins can also improve survival of patients with sepsis and pneumonia. Cystic fibrosis (CF) is the most common recessive inherited disease in the Caucasian population, which is characterised by factors including, but not limited to, excessive lung inflammation and increased susceptibility to infection. Therefore, the overall objective of this study was to examine the effects of statins on CFassociated bacterial pathogens and the host response. In this work, the prevalence of HMGR was examined in respiratory pathogens, and several CF-associated pathogens were found to possess homologues of this enzyme. HMGR homology was analysed in Staphylococcus aureus, Burkholderia cenocepacia and Streptococcus pneumoniae, and the HMGR of B. cenocepacia was found to have significant conservation to that of Pseudomonas mevalonii, which is the most widely-characterised bacterial HMGR. However, in silico analysis revealed that, unlike S. aureus and S. pneumoniae, B. cenocepacia did not possess homologues of other mevalonate pathway proteins, and that the HMGR of B. cenocepacia appeared to be involved in an alternative metabolic pathway. The effect of simvastatin was subsequently tested on the growth and virulence of S. aureus, B. cenocepacia and S. pneumoniae. Simvastatin inhibited the growth of all 3 species in a dose-dependent manner. In addition, statin treatment also attenuated biofilm formation of all 3 species, and reduced in vitro motility of S. aureus. Interestingly, simvastatin also increased the potency of the aminoglycoside antibiotic gentamicin against B. cenocepacia. The impact of statins was subsequently tested on the predominant CF-associated pathogen Pseudomonas aeruginosa, which does not possess a HMGR homologue. Mevastatin, lovastatin and simvastatin did not influence the growth of this species. However, sub-inhibitory statin concentrations reduced the swarming motility and biofilm formation of P. aeruginosa. The influence of statins was also examined on Type 3 toxin secretion, quorum sensing and chemotaxis, and no statin effect was observed on any of these phenotypes. Statins did not appear to have a characteristic effect on the P. aeruginosa transcriptome. However, a mutant library screen revealed that the effect of statins on P. aeruginosa biofilm was mediated through the PvrR regulator and the Cup fimbrial biosynthesis genes. Furthermore, proteomic analysis demonstrated that 6 proteins were reproducibly induced by simvastatin in the P. aeruginosa swarming cells. The effect of statins on the regulation of the host-P. aeruginosa immune response was also investigated. Statin treatment increased expression of the pro-inflammatory cytokines IL-8 and CCL20 in lung epithelial cells, but did not attenuate P. aeruginosa-mediated inflammatory gene induction. In fact, simvastatin and P. aeruginosa caused a synergistic effect on CCL20 expression. The expression of the transcriptional regulators KLF2 and KLF6 was also increased by statins and P. aeruginosa, with the induction of KLF6 by simvastatin proving to be a novel effect. Interestingly, both statins and P. aeruginosa were capable of inducing alternative splicing of KLF6. P. aeruginosa was found to induce KLF6 alternative splicing by way of the type 3 secreted toxin ExoS. In addition, a mechanistic role was elucidated for KLF6 in the lung, as it was determined that statin-mediated induction of this protein was responsible for the induction of the host response genes CCL20 and iNOS. Moreover, statin treatment caused a slight increase in infection-related cytotoxicity, and increased bacterial adhesion to cells. Taken together, these data demonstrate that statins can reduce the virulence of CFassociated bacterial pathogens and alter host response effectors. Furthermore, novel statin effectors were identified in both bacterial and host cells.

Theranostic Fibers for Simultaneous Imaging and Drug Delivery

New methods for creating theranostic systems with simultaneous encapsulation of therapeutic, diagnostic, and targeting agents are much sought after. This work reports for the first time the use of coaxial electrospinning to prepare such systems in the form of core–shell fibers. Eudragit S100 was used to form the shell of the fibers, while the core comprised poly(ethylene oxide) loaded with the magnetic resonance contrast agent Gd(DTPA) (Gd(III) diethylenetriaminepentaacetate hydrate) and indomethacin as a model therapeutic agent. The fibers had linear cylindrical morphologies with clear core–shell structures, as demonstrated by electron microscopy. X-ray diffraction and differential scanning calorimetry proved that both indomethacin and Gd(DTPA) were present in the fibers in the amorphous physical form. This is thought to be a result of intermolecular interactions between the different components, the presence of which was suggested by infrared spectroscopy. In vitro dissolution tests indicated that the fibers could provide targeted release of the active ingredients through a combined mechanism of erosion and diffusion. The proton relaxivities for Gd(DTPA) released from the fibers into tris buffer increased (r1 = 4.79–9.75 s–1 mM–1; r2 = 7.98–14.22 s–1 mM–1) compared with fresh Gd(DTPA) (r1 = 4.13 s–1 mM–1 and r2 = 4.40 s–1 mM–1), which proved that electrospinning has not diminished the contrast properties of the complex. The new systems reported herein thus offer a new platform for delivering therapeutic and imaging agents simultaneously to the colon.

Interactions of Cytochrome P450 3A4 with Phospholipid Bilayer Nanodiscs

Thesis (Ph.D.)--University of Washington, 2016-08

Host cellular regulatory networks in dengue virus-human interactions

Dengue fever is one of the most important mosquito-borne diseases worldwide and is caused by infection with dengue virus (DENV). The disease is endemic in tropical and sub-tropical regions and has increased remarkably in the last few decades. At present, there is no antiviral or approved vaccine against the virus. Treatment of dengue patients is usually supportive, through oral or intravenous rehydration, or by blood transfusion for more severe dengue cases. Infection of DENV in humans and mosquitoes involves a complex interplay between the virus and host factors. This results in regulation of numerous intracellular processes, such as signal transduction and gene transcription which leads to progression of disease. To understand the mechanisms underlying the disease, the study of virus and host factors is therefore essential and could lead to the identification of human proteins modulating an essential step in the virus life cycle. Knowledge of these human proteins could lead to the discovery of potential new drug targets and disease control strategies in the future. Recent advances of high throughput screening technologies have provided researchers with molecular tools to carry out investigations on a large scale. Several studies have focused on determination of the host factors during DENV infection in human and mosquito cells. For instance, a genome-wide RNA interference (RNAi) screen has identified host factors that potentially play an important role in both DENV and West Nile virus replication (Krishnan et al. 2008). In the present study, a high-throughput yeast two-hybrid screen has been utilised in order to identify human factors interacting with DENV non-structural proteins. From the screen, 94 potential human interactors were identified. These include proteins involved in immune signalling regulation, potassium voltage-gated channels, transcriptional regulators, protein transporters and endoplasmic reticulum-associated proteins. Validation of fifteen of these human interactions revealed twelve of them strongly interacted with DENV proteins. Two proteins of particular interest were selected for further investigations of functional biological systems at the molecular level. These proteins, including a nuclear-associated protein BANP and a voltage-gated potassium channel Kv1.3, both have been identified through interaction with the DENV NS2A. BANP is known to be involved in NF-kB immune signalling pathway, whereas, Kv1.3 is known to play an important role in regulating passive flow of potassium ions upon changes in the cell transmembrane potential. This study also initiated a construction of an Aedes aegypti cDNA library for use with DENV proteins in Y2H screen. However, several issues were encountered during the study which made the library unsuitable for protein interaction analysis. In parallel, innate immune signalling was also optimised for downstream analysis. Overall, the work presented in this thesis, in particular the Y2H screen provides a number of human factors potentially targeted by DENV during infection. Nonetheless, more work is required to be done in order to validate these proteins and determine their functional properties, as well as testing them with infectious DENV to establish a biological significance. In the long term, data from this study will be useful for investigating potential human factors for development of antiviral strategies against dengue.

Applied evolution: An integrated approach to studying life history traits in response to drug selection

The use of chemical control measures to reduce the impact of parasite and pest species has frequently resulted in the development of resistance. Thus, resistance management has become a key concern in human and veterinary medicine, and in agricultural production. Although it is known that factors such as gene flow between susceptible and resistant populations, drug type, application methods, and costs of resistance can affect the rate of resistance evolution, less is known about the impacts of density-dependent eco-evolutionary processes that could be altered by drug-induced mortality. The overall aim of this thesis was to take an experimental evolution approach to assess how life history traits respond to drug selection, using a free-living dioecious worm (Caenorhabditis remanei) as a model. In Chapter 2, I defined the relationship between C. remanei survival and Ivermectin dose over a range of concentrations, in order to control the intensity of selection used in the selection experiment described in Chapter 4. The dose-response data were also used to appraise curve-fitting methods, using Akaike Information Criterion (AIC) model selection to compare a series of nonlinear models. The type of model fitted to the dose response data had a significant effect on the estimates of LD50 and LD99, suggesting that failure to fit an appropriate model could give misleading estimates of resistance status. In addition, simulated data were used to establish that a potential cost of resistance could be predicted by comparing survival at the upper asymptote of dose-response curves for resistant and susceptible populations, even when differences were as low as 4%. This approach to dose-response modeling ensures that the maximum amount of useful information relating to resistance is gathered in one study. In Chapter 3, I asked how simulations could be used to inform important design choices used in selection experiments. Specifically, I focused on the effects of both within- and between-line variation on estimated power, when detecting small, medium and large effect sizes. Using mixed-effect models on simulated data, I demonstrated that commonly used designs with realistic levels of variation could be underpowered for substantial effect sizes. Thus, use of simulation-based power analysis provides an effective way to avoid under or overpowering a study designs incorporating variation due to random effects. In Chapter 4, I 3 investigated how Ivermectin dosage and changes in population density affect the rate of resistance evolution. I exposed replicate lines of C. remanei to two doses of Ivermectin (high and low) to assess relative survival of lines selected in drug-treated environments compared to untreated controls over 10 generations. Additionally, I maintained lines where mortality was imposed randomly to control for differences in density between drug treatments and to distinguish between the evolutionary consequences of drug treatment versus ecological processes affected by changes in density-dependent feedback. Intriguingly, both drug-selected and random-mortality lines showed an increase in survivorship when challenged with Ivermectin; the magnitude of this increase varied with the intensity of selection and life-history stage. The results suggest that interactions between density-dependent processes and life history may mediate evolved changes in susceptibility to control measures, which could result in misleading conclusions about the evolution of heritable resistance following drug treatment. In Chapter 5, I investigated whether the apparent changes in drug susceptibility found in Chapter 4 were related to evolved changes in life-history of C. remanei populations after selection in drug-treated and random-mortality environments. Rapid passage of lines in the drug-free environment had no effect on the measured life-history traits. In the drug-free environment, adult size and fecundity of drug-selected lines increased compared to the controls but drug selection did not affect lifespan. In the treated environment, drug-selected lines showed increased lifespan and fecundity relative to controls. Adult size of randomly culled lines responded in a similar way to drug-selected lines in the drug-free environment, but no change in fecundity or lifespan was observed in either environment. The results suggest that life histories of nematodes can respond to selection as a result of the application of control measures. Failure to take these responses into account when applying control measures could result in adverse outcomes, such as larger and more fecund parasites, as well as over-estimation of the development of genetically controlled resistance. In conclusion, my thesis shows that there may be a complex relationship between drug selection, density-dependent regulatory processes and life history of populations challenged with control measures. This relationship could have implications for how resistance is monitored and managed if life histories of parasitic species show such eco-evolutionary responses to drug application.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Demonstration of the interactions between aromatic compound-loaded lipid nanocapsules and Acinetobacter baumannii bacterial membrane

International audience

Effects of the encapsulation of usnic acid into liposomes and interactions with antituberculous agents against multidrug-resistant tuberculosis clinical isolates

Mycobacterium tuberculosis (Mtb) has acquired resistance and consequently the antibiotic therapeutic options available against this microorganism are limited. In this scenario, the use of usnic acid (UA), a natural compound, encapsulated into liposomes is proposed as a new approach in multidrug-resistant tuberculosis (MDR-TB) therapy. Thus the aim of this study was to evaluate the effect of the encapsulation of UA into liposomes, as well as its combination with antituberculous agents such as rifampicin (RIF) and isoniazid (INH) against MDR-TB clinical isolates. The in vitro antimycobacterial activity of UA-loaded liposomes (UA-Lipo) against MDR-TB was assessed by the microdilution method. The in vitro interaction of UA with antituberculous agents was carried out using checkerboard method. Minimal inhibitory concentration values were 31.25 and 0.98 μg/mL for UA and UA-Lipo, respectively. The results exhibited a synergistic interaction between RIF and UA [fractional inhibitory concentration index (FICI) = 0.31] or UA-Lipo (FICI = 0.28). Regarding INH, the combination of UA or UA-Lipo revealed no marked effect (FICI = 1.30-2.50). The UA-Lipo may be used as a dosage form to improve the antimycobacterial activity of RIF, a first-line drug for the treatment of infections caused by Mtb.

Pathway analysis for identification of potential biomarkers in severe cutaneous drug hypersensitivity reactions

Purpose: To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using pathways analysis in order to identify some potential biomarkers that may be used for early detection of SJS and epidermal necrolysis (TEN) manifestations. Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which are online software and databases. Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for topological analysis. It was found by geneMANIA database search that majority of these genes were coexpressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction with regards to hypersensitivity. Conclusion: Analysis of differential gene expressions by topological and database approaches in the current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed.