Interactions of Cytochrome P450 3A4 with Phospholipid Bilayer Nanodiscs

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

Characterizing bupropion metabolism, pharmacokinetics and drug-drug interaction liability

Thesis (Ph.D.)--University of Washington, 2016-06

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.

Comparing the expression of human DNA topoisomerase I in KM71H and X33 strains of Pichia pastoris

Background: Human is an essential cellular enzyme that is found in all human cells. As this enzyme is upregulated in cancer cells exceedingly, it is used as a target for cancer chemotherapeutic drug development. As such, producing the in-house enzyme for the purpose to speed up the search for more cost-effective and target specific hTopoI inhibitors is warranted. This study aims to compare the optimised conditions for the expression of hTopoI in KM71H (MutS) and X33 (Mut+) strains of Pichia pastoris P. pastoris transfected with an hTopoI recombinant vector was used for the optimization of a higher level of hTopoI expression. Results: In the process, fed-batch cultivation parameters that influence the expression of hTopoI, such as culture temperature, methanol induction and feeding strategy, were optimised in the transfected KM71H and X33 P. pastoris strains in a shake flask system. The cell density and total protein concentration (protein level) of transfected P. pastoris were compared to determine the optimum culture conditions for each transfected P. pastoris strain. A higher hTopoI level was observed in the transfected KM71H culture supernatant (2.26 ng/mL) when the culture was incubated in the optimum conditions. Conclusions: This study demonstrated that MutS strain (KM71H) expressed and secreted a higher level of hTopoI heterologous protein in the presence of methanol compared to the Mut+ strain; X33 (0.75 ng/mL). However, other aspects of optimization, such as pH, should also be considered in the future, to obtain the optimum expression level of hTopoI in P. pastoris.

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.

Análise do efeito de polimorfismos não-sinônimos em genes de reparo de DNA da via BER na resposta inflamatória da meningite

In vitro and in animal models, APE1, OGG1, and PARP-1 have been proposed as being involved with inflammatory response. In this work, we have investigated if the SNPs APE1 Asn148Glu, OGG1 Ser326Cys, and PARP-1 Val762Ala are associated to meningitis and also developed a system to enable the functional analysis of polymorphic proteins. Patients with bacterial meningitis (BM), aseptic meningitis (AM) and controls (non-infected) genotypes were investigated by PIRA-PCR or PCR-RFLP. DNA damages were detected in genomic DNA by Fpg treatment. IgG and IgA were measured from plasma and the cytokines and chemokines were measured from cerebrospinal fluid samples using Bio-Plex assays. The levels of NF-κB and c-Jun were measured in CSF by dot blot assays. A significant (P<0.05) increase in the frequency of APE1 148Glu allele in BM and AM patients was observed. A significant increase in the genotypes Asn/Asn in control group and Asn/Glu in BM group was also found. For the SNP OGG1 Ser326Cys, the genotype Cys/Cys was more frequent (P<0.05) in BM group. The frequency of PARP-1 Val/Val genotype was higher in control group (P<0.05). The occurrence of combined SNPs increased significantly in BM patients, indicating that these SNPs may be associated to the disease. Increasing in sensitive sites to Fpg was observed in carriers of APE1 148Glu allele or OGG1 326Cys allele, suggesting that SNPs affect DNA repair activity. Alterations in IgG production were observed in the presence of SNPs APE1Asn148Glu, OGG1Ser326Cys or PARP-1Val762Ala. Reductions in the levels ofIL-6, IL-1Ra, MCP-1/CCL2and IL-8/CXCL8 were observed in the presence of APE1148Glu allele in BM patients, however no differences were observed in the levels of NF-κB and c-Jun considering genotypes and analyzed groups. Using APE1 as model, a system to enable the analysis of cellular effects and functional characterization of polymorphic proteins was developed using strategies of cloning APE1 cDNA in pIRES2-EGFP vector, cellular transfection of the construction obtained, siRNA for endogenous APE1 and cellular cultures genotyping. In conclusion, we obtained evidences of an effect of SNPs in DNA repair genes on the regulation of immune response. This is a pioneering work in the field that shows association of BER variant enzymes with an infectious disease in human patients, suggesting that the SNPs analyzed may affect immune response and damage by oxidative stress level during brain infection. Considering these data, new approaches of functional characterization must be developed to better analysis and interactions of polymorphic proteins in response to this context

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

International audience

A molecular platform for the diagnosis of multidrug-resistant and pre-extensively drug-resistant tuberculosis based on single nucleotide polymorphism mutations present in Colombian isolates of Mycobacterium tuberculosis

Developing a fast, inexpensive, and specific test that reflects the mutations present in Mycobacterium tuberculosis isolates according to geographic region is the main challenge for drug-resistant tuberculosis (TB) control. The objective of this study was to develop a molecular platform to make a rapid diagnosis of multidrug-resistant (MDR) and extensively drug-resistant TB based on single nucleotide polymorphism (SNP) mutations present in the rpoB, katG, inhA, ahpC, and gyrA genes from Colombian M. tuberculosis isolates. The amplification and sequencing of each target gene was performed. Capture oligonucleotides, which were tested before being used with isolates to assess the performance, were designed for wild type and mutated codons, and the platform was standardised based on the reverse hybridisation principle. This method was tested on DNA samples extracted from clinical isolates from 160 Colombian patients who were previously phenotypically and genotypically characterised as having susceptible or MDR M. tuberculosis. For our method, the kappa index of the sequencing results was 0,966, 0,825, 0,766, 0,740, and 0,625 for rpoB, katG, inhA, ahpC, and gyrA, respectively. Sensitivity and specificity were ranked between 90-100% compared with those of phenotypic drug susceptibility testing. Our assay helps to pave the way for implementation locally and for specifically adapted methods that can simultaneously detect drug resistance mutations to first and second-line drugs within a few hours.

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.

Investigating the role and function of Tribbles 2 (TRIB2) in drug resistance within cancer

Dissertação de Mestrado, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2014

Development of a novel platform for high-throughput gene design and artificial gene synthesis to produce large libraries of recombinant venom peptides for drug discovery

Tese de Doutoramento em Ciências Veterinárias na Especialidade de Ciências Biológicas e Biomédicas

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.

Early and late effects of Ibuprofen on mouse sperm parameters, chromatin condensation, and DNA integrity in mice

Background: There are few studies indicating the detrimental effects of ibuprofen on sperm fertility potential and DNA integrity. Objective: To determine the effects of Ibuprofen on sperm parameters, chromatin condensation and DNA integrity of mice. Materials and Methods: In this experimental study, 36 adult male mice with average weight 37 gr were divided into three groups, including control (group I, n=12), normal dosage of ibuprofen (group II, n=12) and high dosage (group III, n=12). Ibuprofen with different doses was dissolved in daily water of animals. After 35, 70 and 105 days, the cauda epididymis of mice were cut and incubated in Ham’s F10 media. Sperm samples were analyzed for parameters (motility, morphology and count), DNA integrity (SCD test) and chromatin condensation (chromomycin A3 and Aniline blue staining). Results: After 35 days, in addition to above mentioned sperm parameters, all of the treated mice showed statistically significant increase in spermatozoa with immature chromatin (P<0.05). However, after 70 days, the rate of sperm DNA fragmentation assessed by SCD was increased in group II (66.5±0.7) and the percentage of immature spermatozoa (AB+ and CMA3+) was higher in group III (77.5±0.7 and 49.5±6.3 respectively) than other groups. After 105 days, the AB+ spermatozoa were increased in both normal dose and high dose groups. Conclusion: Ibuprofen may cause a significant reduction in sperm parameters and sperm chromatin/DNA integrity in mice. It should be noted that these deleterious effects are dose-dependent and can be seen in early and late stage of drug treatments.