Mining the Metabiome: Identifying Novel Natural Products from Microbial Communities

Microbial-derived natural products provide the foundation for most of the chemotherapeutic arsenal available to contemporary medicine. In the face of a dwindling pipeline of new lead structures identified by traditional culturing techniques and an increasing need for new therapeutics, surveys of microbial biosynthetic diversity across environmental metabiomes have revealed enormous reservoirs of as yet untapped natural products chemistry. In this review, we touch on the historical context of microbial natural product discovery and discuss innovations and technological advances that are facilitating culture-dependent and culture-independent access to new chemistry from environmental microbiomes with the goal of reinvigorating the small molecule therapeutics discovery pipeline. We highlight the successful strategies that have emerged and some of the challenges that must be overcome to enable the development of high-throughput methods for natural product discovery from complex microbial communities

Structural studies of the SARS virus Nsp15 endonuclease and the human innate immunity receptor TLR3

Three-dimensional (3D) structural determination of biological macromolecules is not only critical to understanding their mechanisms, but also has practical applications. Combining the high resolution imaging of transmission electron microscopy (TEM) and efficient computer processing, protein structures in solution or in two-dimensional (2D) crystals can be determined. The lipid monolayer technique uses the high affinity binding of 6His-tagged proteins to a Ni-nitrilotriacetic (NTA) lipid to create high local protein concentrations, which facilitates 2D crystal formation. In this study, several proteins have been crystallized using this technique, including the SARS virus Nsp15 endonuclease and the human Toll-like receptor (TLR) 3 extracellular domain (ECD). Single particle analysis can determine protein structures in solution without the need for crystals. 3D structures of several protein complexes had been solved by the single particle method, including IniA from Mycobacterium tuberculosis, Nsp15 and TLR3 ECD. Determining the structures of these proteins is an important step toward understanding pathogenic microbes and our immune system

Doctor of Philosophy

dissertationNanoinformatics is a relatively young field of study that is important due to its implications in the field of nanomedicine, specifically toward the development of nanoparticle drug delivery systems. As more structural, biochemical, and physiochemical data become available regarding nanoparticles, the greater the knowledge-gain from using nanoinformatics methods will become. While there are challenges that exist with nanoparticle data, including heterogeneity of data and complexity of the particles, nanoinformatics will be at the forefront of processing these data and aid in the design of nanoparticles for biomedical applications. In this dissertation, a review of data mining and machine learning studies performed in the field of nanomedicine is presented. Next, the use of natural language processing methods to extract numeric values of biomedical property terms of poly(amido amine) (PAMAM) dendrimers from nanomedicine literature is demonstrated, along with successful extraction results. Following this is an implementation and its results of data mining techniques used for the development of predictive models of cytotoxicity of PAMAM dendrimers using their chemical and structural properties. Finally, a method and its results for using molecular dynamics simulations to test the ability of EDTA, as a gold standard, and generation 3.5 (G3.5) PAMAM dendrimers to chelate calcium

Raman spectroscopy: techniques and applications in the life sciences

Raman spectroscopy is an increasingly popular technique in many areas including biology and medicine. It is based on Raman scattering, a phenomenon in which incident photons lose or gain energy via interactions with vibrating molecules in a sample. These energy shifts can be used to obtain information regarding molecular composition of the sample with very high accuracy. Applications of Raman spectroscopy in the life sciences have included quantification of biomolecules, hyperspectral molecular imaging of cells and tissue, medical diagnosis, and others. This review briefly presents the physical origin of Raman scattering explaining the key classical and quantum mechanical concepts. Variations of the Raman effect will also be considered, including resonance, coherent, and enhanced Raman scattering. We discuss the molecular origins of prominent bands often found in the Raman spectra of biological samples. Finally, we examine several variations of Raman spectroscopy techniques in practice, looking at their applications, strengths, and challenges. This review is intended to be a starting resource for scientists new to Raman spectroscopy, providing theoretical background and practical examples as the foundation for further study and exploration

Investigating the biomedical applications of coordination cages

Metallocages represent an exciting field of supramolecular chemistry concerned with the assembly of specific ligands and metals to form discrete structures. Coordination cages have applications in catalysis, molecular recognition and drug delivery. Accessing the biomedical applications of cages has received growing interest over the past two decades with, a more recent focus on utilising the cage cavity for the encapsulation of radioisotopes as a means of fast and efficient radiolabelling. This work follows on from previous work by the Lusby group, which demonstrated the synthesis of a kinetically locked and robust Coᴵᴵᴵ tetrahedral capable of hosting [⁹⁹ᵐTc]TcO₄− and altering the radioisotopes biodistribution. The change in bioaccumulation from the stomach and thyroid to the liver indicated a potential interaction with proteins. Chapter two describes an investigation into the binding between a series of coordination complexes and Human Serum Albumin (HSA) using techniques including NMR, MS, dialysis and isothermal titration calorimetry. The study showed a strong interaction (Kd ≈ 2 μM) between the CoIII tetrahedral from the previous study and HSA, compared to the CoIII mononuclear complex, which exhibited much weaker binding (Kd ≈ 200 μM). Computational modelling indicated the interaction was likely the result of multiple electrostatic interactions, with potential applications in protein-mediated cage delivery. Chapter three described the formation of three novel Coᴵᴵᴵ tetrahedra whereby the in vivo stability and host-guest chemistry could be changed by altering the external functionality of the cages. Using NMR and radiochemical TLC, the stability of the new systems under a range of conditions was determined, and the new Coᴵᴵᴵ ethanolamine functionalised cage exhibited a similar radiochemical encapsulation (EC₉₅ = 4.4 μM) as the previously defined Coᴵᴵᴵ tetrahedral (EC₉₅ = 1.6 μM). The new cage systems also serve as a scaffold for which further bioconjugation could occur through the binding of peptides to alter the delivery of the system, highlighting the potential of these kinetically inert Coᴵᴵᴵ tetrahedra as targeted delivery vessels. Chapter four focused on determining the scope of cages applicable for biomedicine by synthesising a series of novel Pd₂L₄ systems. The cages were tested for their water solubility and stability in the presence of bio-prevalent species. NMR studies indicated that the underivatised Pd₂L₄ systems possessed a half-life of <10 minutes in the presence of NaCl, indicating a lack of biological stability. A series of more strongly coordinating ligands were synthesised, and attempts to assemble the cages resulted in low symmetry NMRs, presumably due to the increased strength of ligand coordination hindering the assembly equilibrium. Overall a comprehensive investigation was completed into the activities of coordination cages in vivo and their potential applications. Whereby the external functionality of the cage is imperative for the bioactivity and stability of the system

MRI-Based Attenuation Correction in Emission Computed Tomography

The hybridization of magnetic resonance imaging (MRI) with positron emission tomography (PET) or single photon emission computed tomography (SPECT) enables the collection of an assortment of biological data in spatial and temporal register. However, both PET and SPECT are subject to photon attenuation, a process that degrades image quality and precludes quantification. To correct for the effects of attenuation, the spatial distribution of linear attenuation coefficients (μ-coefficients) within and about the patient must be available. Unfortunately, extracting μ-coefficients from MRI is non-trivial. In this thesis, I explore the problem of MRI-based attenuation correction (AC) in emission tomography. In particular, I began by asking whether MRI-based AC would be more reliable in PET or in SPECT. To this end, I implemented an MRI-based AC algorithm relying on image segmentation and applied it to phantom and canine emission data. The subsequent analysis revealed that MRI-based AC performed better in SPECT than PET, which is interesting since AC is more challenging in SPECT than PET. Given this result, I endeavoured to improve MRI-based AC in PET. One problem that required addressing was that the lungs yield very little signal in MRI, making it difficult to infer their μ-coefficients. By using a pulse sequence capable of visualizing lung parenchyma, I established a linear relationship between MRI signal and the lungs’ μ-coefficients. I showed that applying this mapping on a voxel-by-voxel basis improved quantification in PET reconstructions compared to conventional MRI-based AC techniques. Finally, I envisaged that a framework for MRI-based AC methods would potentiate further improvements. Accordingly, I identified three ways an MRI can be converted to μ-coefficients: 1) segmentation, wherein the MRI is divided into tissue types and each is assigned an μ-coefficient, 2) registration, wherein a template of μ-coefficients is aligned with the MRI, and 3) mapping, wherein a function maps MRI voxels to μ-coefficients. I constructed an algorithm for each method and catalogued their strengths and weaknesses. I concluded that a combination of approaches is desirable for MRI-based AC. Specifically, segmentation is appropriate for air, fat, and water, mapping is appropriate for lung, and registration is appropriate for bone

Widefield Computational Biophotonic Imaging for Spatiotemporal Cardiovascular Hemodynamic Monitoring

Cardiovascular disease is the leading cause of mortality, resulting in 17.3 million deaths per year globally. Although cardiovascular disease accounts for approximately 30% of deaths in the United States, many deleterious events can be mitigated or prevented if detected and treated early. Indeed, early intervention and healthier behaviour adoption can reduce the relative risk of first heart attacks by up to 80% compared to those who do not adopt new healthy behaviours. Cardiovascular monitoring is a vital component of disease detection, mitigation, and treatment. The cardiovascular system is an incredibly dynamic system that constantly adapts to internal and external stimuli. Monitoring cardiovascular function and response is vital for disease detection and monitoring. Biophotonic technologies provide unique solutions for cardiovascular assessment and monitoring in naturalistic and clinical settings. These technologies leverage the properties of light as it enters and interacts with the tissue, providing safe and rapid sensing that can be performed in many different environments. Light entering into human tissue undergoes a complex series of absorption and scattering events according to both the illumination and tissue properties. The field of quantitative biomedical optics seeks to quantify physiological processes by analysing the remitted light characteristics relative to the controlled illumination source. Drawing inspiration from contact-based biophotonic sensing technologies such as pulse oximetry and near infrared spectroscopy, we explored the feasibility of widefield hemodynamic assessment using computational biophotonic imaging. Specifically, we investigated the hypothesis that computational biophotonic imaging can assess spatial and temporal properties of pulsatile blood flow across large tissue regions. This thesis presents the design, development, and evaluation of a novel photoplethysmographic imaging system for assessing spatial and temporal hemodynamics in major pulsatile vasculature through the sensing and processing of subtle light intensity fluctuations arising from local changes in blood volume. This system co-integrates methods from biomedical optics, electronic control, and biomedical image and signal processing to enable non-contact widefield hemodynamic assessment over large tissue regions. A biophotonic optical model was developed to quantitatively assess transient blood volume changes in a manner that does not require a priori information about the tissue's absorption and scattering characteristics. A novel automatic blood pulse waveform extraction method was developed to encourage passive monitoring. This spectral-spatial pixel fusion method uses physiological hemodynamic priors to guide a probabilistic framework for learning pixel weights across the scene. Pixels are combined according to their signal weight, resulting in a single waveform. Widefield hemodynamic imaging was assessed in three biomedical applications using the aforementioned developed system. First, spatial vascular distribution was investigated across a sample with highly varying demographics for assessing common pulsatile vascular pathways. Second, non-contact biophotonic assessment of the jugular venous pulse waveform was assessed, demonstrating clinically important information about cardiac contractility function in a manner which is currently assessed through invasive catheterization. Lastly, non-contact biophotonic assessment of cardiac arrhythmia was demonstrated, leveraging the system's ability to extract strong hemodynamic signals for assessing subtle fluctuations in the waveform. This research demonstrates that this novel approach for computational biophotonic hemodynamic imaging offers new cardiovascular monitoring and assessment techniques, which can enable new scientific discoveries and clinical detection related to cardiovascular function