Use of pyrosequencing to identify streptococci and to detect mutations causing antimicrobial resistance

Rapid identification and resistance determination of pathogens in clinical specimens is vital for accurate treatment and monitoring of infectious diseases. Antimicrobial drug resistance is increasing globally and healthcare settings are facing this cost-intensive and even life-threatening problem. The incidence of resistant pathogens in Finland has remained relatively steady and manageable at least for the time being. DNA sequencing is the gold standard method for genotyping, mutation analysis, and identification of bacteria. Due to significant cost decrease in recent years, this technique is available to many research and clinical laboratories. Pyrosequencing technique, a rapid real-time DNA sequencing method especially suitable for analyzing fairly short stretches of DNA, was used in this study. Due to its robustness and versatility, pyrosequencing was applied in this study for identification of streptococci and detection of certain mutations causing antimicrobial resistance in different bacteria. Certain streptococcal species such as S. pneumoniae and S. pyogenes are significantly important clinical pathogens. S. pneumoniae causes e.g. pneumonia and otitis media and is one of the most important community-acquired pathogens. S. pyogenes, also known as group A streptococcus, causes e.g. angina and erysipelas. In contrast, the socalled alpha-haemolytic streptococci, such as S. mitis and S. oralis, belong to the normal microbiota, which are regarded to be non-pathogenic and are nearly impossible to identify by phenotypic methods. In this thesis, a pyrosequencing method was developed for identification of streptococcal species based on the 16S rRNA sequences. Almost all streptococcal species could be differentiated from one another by the developed method, including S. pneumoniae from its close relatives S. mitis and S. oralis . New resistance genes and their variants are constantly discovered and reported. In this study, new methods for detecting certain mutations causing macrolide resistance or extended spectrum beta-lactamase (ESBL) phenotype were developed. These resistance detection approaches are not only suitable for surveillance of mechanisms causing antimicrobial resistance but also for routine analysis of clinical samples particularly in epidemic settings. In conclusion, pyrosequencing was found to be an accurate, versatile, cost-effective, and rapid DNA sequencing method that is especially suitable for mutation analysis of short DNA fragments and identification of certain bacteria.

Lethal weapons : novel approaches for receptor-targeted cancer cell elimination

The currently used forms of cancer therapy are associated with drug resistance and toxicity to healthy tissues. Thus, more efficient methods are needed for cancer-specific induction of growth arrest and programmed cell death, also known as apoptosis. Therapeutic forms of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) are investigated in clinical trials due to the capability of TRAIL to trigger apoptosis specifically in cancer cells by activation of cell surface death receptors. Many tumors, however, have acquired resistance to TRAIL-induced apoptosis and sensitizing drugs for combinatorial treatments are, therefore, in high demand. This study demonstrates that lignans, natural polyphenols enriched in seeds and cereal, have a remarkable sensitizing effect on TRAIL-induced cell death at non-toxic lignan concentrations. In TRAIL-resistant and androgen-dependent prostate cancer cells we observe that lignans repress receptor tyrosine kinase (RTK) activity and downregulate cell survival signaling via the Akt pathway, which leads to increased TRAIL sensitivity. A structure-activity relationship analysis reveals that the γ-butyrolactone ring of the dibenzylbutyrolactone lignans is essential for the rapidly reversible TRAIL-sensitizing activity of these compounds. Furthermore, the lignan nortrachelogenin (NTG) is identified as the most efficient of the 27 tested lignans and norlignans in sensitization of androgen-deprived prostate cancer cells to TRAIL-induced apoptosis. While this combinatorial anticancer approach may leave normal cells unharmed, several efficient cancer drugs are too toxic, insoluble or unstable to be used in systemic therapy. To enable use of such drugs and to protect normal cells from cytotoxic effects, cancer-targeted drug delivery vehicles of nanometer scale have recently been generated. The newly developed nanoparticle system that we tested in vitro for cancer cell targeting combines the efficient drug-loading capacity of mesoporous silica to the versatile particle surface functionalization of hyperbranched poly(ethylene imine), PEI. The mesoporous hybrid silica nanoparticles (MSNs) were functionalized with folic acid to promote targeted internalization by folate receptor overexpressing cancer cells. The presented results demonstrate that the developed carrier system can be employed in vitro for cancer selective delivery of adsorbed or covalently conjugated molecules and furthermore, for selective induction of apoptotic cell death in folate receptor expressing cancer cells. The tested carrier system displays potential for simultaneous delivery of several anticancer agents specifically to cancer cells also in vivo.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.