This dissertation delineates the development of a simplified, reproducible in vitro microbiome model of the human dental plaque, with the specific aims of minimizing procedural complexity and investigating the effects of preservation on a complex laboratory microbiome model. This model is based on a 24-well-plate anaerobic model that uses an enriched, undefined culture media, with complex carbohydrate sources and supplemented by nutritional components required by certain fastidious organisms. Preliminary work with the plaque inoculum from a single host served as a proof-of-concept and foundation for the model. In this stage of the project, the viability, membership and relative abundances of the preliminary model were assessed by fluorescence microscopy and 16S rRNA amplicon sequencing on the Illumina MiSeq platform. Processing and analysis of the sequencing data were performed using the mothur pipeline and phyloseq package in R. Preliminary work validated the cultivation, sequencing, and analytical procedures by showing members and abundances expected of early-stage human oral bacterial communities. These procedures were then adopted in the development of a temporal model with a maximum incubation time of 168 hours. The temporal model was fed on a defined schedules and minimal nutrients. Analysis of this model showed a temporal succession of bacteria that closely follows the order of succession in host oral cavities, as well as developmental differences across different hosts. These results show promise that the temporal model may be modified with periodic re-inoculation and lengthened to capture a broader temporal range and thus a more complex colonization succession in the dental plaque bacterial communities.

The combined results from the preliminary and temporal models formed the basis of the preservation experiments, which investigated the feasibility of refrigerating and cryopreserving 72-hour in vitro communities from three hosts for subsequent propagation. The results indicated that preservation of microbial communities did not lead to substantial changes in membership or relative abundance, though propagating the preserved communities led to relative abundance shifts in favor of early colonizers. What is worth noting is that differences from the host plaque inoculum dominated the observed dissimilarities among preserved and propagated communities. Examination of these communities also implicated potential synergy between members of the Streptococcus and the Veillonella genera, and potential antagonism between members of the Streptococcus and the Prevotella genera. These findings warrant further investigations of organismal relationships in a reduced microcosm, while offer promising potential applications of preserving in vitro dental microbiome models for therapeutic purposes.

Classical biological control aims to actively manage threats that cause immense losses in biological diversity. The introduction of a biological control agent Colletotrichum gloeosporioides f. sp. miconiae (Cgm) in April 2000 to Tahiti, French Polynesia was intended to control the massive spread of an invasive weed Miconia calvescens. However, while Cgm has subsequently spread to Mo’orea, its impact on reducing M. calvescens remains uncertain. The main objectives of this study are: (1) to quantify the amount of the fungal pathogen infecting the M. calvescens plants at three elevation ranges on Mo’orea, (2) to understand the impact of moisture on the proliferation of disease development, and (3) to test the influence of endophytic fungal communities on the competitive ability of Cgm. Results from quantifying leaf damage showed that at higher elevations, Cgm disease development is more rampant. In laboratory experiments, varying moisture did not significantly affect the health of the seedling. While Cgm growth rate correlates with competitive ability, endophytic fungal growth rate does not, leading to speculation that other modes such as chemical interactions allow for endophytic competitive ability. Lastly, data supports the hypothesis that Cgm becomes a better competitor against the endophytic fungal species at higher elevations. Results of this study suggest that other microclimatic factors such as temperature and humidity may play a role in disease development. While Cgm may decelerate the growth of M. calvescens, Cgm alone is not likely to obliterate the massive damage M. calvescens has done on the native flora of Mo’orea and its surrounding islands. Conservation biologists must urgently attack this pest, or the fragile ecosystem of the islands will lead to massive losses in biological diversity.

Protein aggregation is an important topic for human health and biotechnology, with consequences in areas ranging from neurodegenerative diseases to therapeutic protein production yields. With these concerns in mind, methods to modulate protein aggregation are therefore essential. One suggested method to reduce protein aggregation is the use of nucleic acid aptamers, i.e., single-stranded nucleic acids that have been selected to specifically bind to a target. Previous studies in some systems have demonstrated that aptamer binding to their protein target inhibit aggregation. However, the mechanisms by which aptamers might reduce aggregation have not been fully determined. In the studies described here, we investigated published aptamers that target a-synuclein and Green Fluorescent Protein (GFP) and their influence on protein aggregation. A kinetic and structural analyses were performed to assess the mechanistic effect of the selected aptamers on protein aggregation. An observation in each study indicated the presence of aptamers resulted in an increase of higher order intermediates (i.e., oligomers) in solution. The increase in oligomer population due to aptamers highlights a potential mechanism by which aptamers may modulate the aggregation properties of proteins. The modulatory effect of aptamers on protein aggregation can significantly impact biotechnology by proposing alternative methods to address disease and protein therapeutics.

As a leading global health concern, antimicrobial resistance threatens the continued reliance on antibiotics to treat bacterial infections in clinical settings. Bacteriophage (or phage) therapy as an alternative antimicrobial strategy has resurfaced in the recent years. Phage therapy relies on the idea that phages are natural viruses against bacteria, having evolved diverse mechanisms to bind, infect, and kill their hosts. Modern approaches to phage therapy use a combination of genetic, chemical, and biomolecular approaches to engineer novel antimicrobial functionalities. In this work, I characterize a novel receptor binding protein, named PAB, that was selected from a phage display library based on its ability to bind a wide variety of clinically isolated, drug- resistant Pseudomonas aeruginosa strains, which are known for their virulence, especially innosocomial infections. It is envisioned that with further characterization and optimization of the PAB protein, chimeric M13 phage displaying PAB on its surface can be applied towards the development of phage-based therapeutics and diagnostic tools against a broad spectrum of P. aeruginosa strains.

Protein aggregation can impact the product quality of pharmaceutical drugs in terms of efficacy, safety, and immunogenicity leading to economic and technical problems for biotechnology and pharmaceutical companies. Furthermore, protein aggregation can lead to the onset of a variety of diseases including amyloidosis, prion diseases, and other protein deposition disorders. To combat these issues, we investigated the potential of aptamers to prevent protein aggregation within our model, modified fluorescent protein. We have identified and successfully demonstrated that the previous selected AP3 aptamer has a high affinity for not only green fluorescent protein (GFP), but also to emerald GFP with a KD of 199 nM. Our work has successfully demonstrated that protein aggregation can be inhibited upon binding of RNA aptamers.

High-throughput sequencing (HTS) can identify unique DNA sequences and quantify their abundances from mixed DNA pools. HTS-based assays can profile complex biological or chemical systems with entities that can convert to unique DNA sequences. Computational models are also developed to analyze these HTS data at a larger scale. However, such data contain unique analytical challenges, including discrete counts, relative measurement, and small sample size. Careful assessments of these computational tools are required for robust interpretations of results.

In this dissertation, we investigated the computational challenges, proposed and assess the solutions for two applications of HTS-based assays. In the first work, we proposed k-Seq, a kinetic assay to measure the activity of self-aminoacylation ribozymes (catalytic RNA). Characterizing the kinetics for different molecules in a heterogeneous pool is challenging as their abundance and activities can vary in several orders of magnitude. We explored different designs of experiments and identified critical factors affecting the estimation of kinetic coefficients in the pseudo-first-order kinetic model for these ribozymes. Using bootstrapping, we robustly quantified the uncertainty of estimation for individual sequences and determined the minimum sequencing counts required for reliable estimations. Combining the improved experimental design and new analytical tools, we robustly quantified the kinetics for 10^5 different ribozymes.

In the second work, we constructed the correlation networks between microorganisms from metagenomic data and studied the structure of a human skin microbiome in patients with chronic wounds. We designed a variation of Gaussian graphical models to capture the direct correlations between the abundances of bacteria and viruses while accounting for the structure and limitations in the data. To minimize the discovery of false correlations from the small noisy dataset, we applied a two-step model selection to regularize the results. Lastly, we demonstrated the utility of the constructed correlation network in recovering the strong correlations between microbes, identifying potentially important microbes, and microbial clusters.