The increased use of mass spectrometry in the clinical setting has led to a demand for high sample throughput. Developments such as ultra high performance liquid chromatography and the ambient ionization techniques enable high sample throughput by reducing chromatographic run times or by removing the requirement for sample preparation and fractionation prior to analysis. This thesis assesses the reproducibility and robustness of these high throughput techniques for the analysis of clinical and pharmaceutical samples by ion mobility-mass spectrometry. The rapid quantitative analysis of the urinary biomarkers of chronic obstructive pulmonary disease, desmosine and isodesmosine has been performed by ultra high performance liquid chromatography combined with ion mobility-mass spectrometry. The determination of health status based on the free unbound fraction rather than the total bound and unbound desmosine and isodesmosine, significantly reduces the time taken in sample preparation. The potential for direct analysis of the urinary metabolites from undeveloped TLC plates using a solvent extraction surface sample probe is demonstrated. The use of a solvent gradient for the extraction separates urinary metabolites from salts and other matrix components and allows fractionation of the sample as a result of differential retention on the undeveloped RP-TLC plate. This separation, combined with ion mobility-mass spectrometry provides a rapid ambient ionization method for urinary profiling. The combination of a thermal desorption probe with extractive electrospray ionization has been applied to the direct detection of a known genotoxic impurity from a surrogate active pharmaceutical ingredient. The volatility of the impurity compared to the matrix, allowed selective thermal desorption of the analyte, which was ionized by extractive electrospray and detected by mass spectrometry. The use of a rapid on-probe derivatisation reaction, combined with thermal desorption is demonstrated for the direct determination of urinary creatinine. The aqueous acylation of creatinine significantly increases the volatility of the analyte enabling separation from the urine matrix and analysis by thermal desorption extractive electrospray combined with ion mobility-mass spectrometry.

Breast cancers with overexpression or amplification of the HER2 tyrosine kinase receptor are more aggressive, resistant to chemotherapy, and associated with a worse prognosis. Currently, these breast cancers are treated with the monoclonal antibody trastuzumab (Herceptin®). Unfortunately, not all patients respond to trastuzumab drug therapy; some patients show de novo resistance, while others acquire resistance during treatment. This thesis describes our RNAi studies to identify novel regulators of the HER2 signaling pathway in breast cancer. Three kinome-wide siRNA screens were performed on five HER2 amplified and seven HER2 non-amplified breast cancer cell lines, two normal breast cell lines, as well as two HER2-positive breast cancer cell lines with acquired trastuzumab resistance and their isogenic trastuzumab-sensitive controls. To understand the main kinase drivers of HER2 signaling, we performed a comprehensive screen that selected against growth inhibitors of the non-HER2 amplified breast cancer cell lines. This screen identified the loss of the HER2/HER3 heterodimer as the most prominent selective inhibitor of HER2-amplified breast cancers. In a trastuzumab sensitization screen on five trastuzumab-treated breast cancer cell lines, we identified several siRNA against the PI3K pathway as well as various other signaling pathways that inhibited proliferation. Finally, in a screen for acquired trastuzumab resistance, PKCη and its downstream targets were identified. Loss of PKCη resulted in a decrease in G1/S transition and upregulation of the cyclin dependent kinase inhibitor p27. Initial data suggest that PKCη promotes p27 ubiquitination and degradation. Taken together, these studies provide novel insight into the complex signaling of HER2-positive breast cancers and the mechanisms of resistance to trastuzumab therapy. This work describes how various kinases can modulate cell proliferation, and points to possible novel drug targets for the treatment of HER2-positive breast cancers.

Glycosylation, the addition of one or more carbohydrates molecules to a protein, is crucial for many cellular processes. Aberrant glycosylation is a key marker for various diseases such as cancer and rheumatoid arthritis. It has also recently been discovered that glycosylation is important in the ability of the Human Immunodeficiency Virus (HIV) to evade recognition by the immune system. Given the importance of glycosylation in disease, major efforts are underway in life science research to investigate the glycome, the entire glycosylation profile of an organelle, cell or tissue type. To date, little bioinformatics research has been performed in glycomics due to the complexity of glycan structures and the low throughput of carbohydrate analysis. Recent advances in mass spectrometry (MS) have greatly facilitated the analysis of the glycome. Increasingly, this technology is preferred over traditional methods of carbohydrate analysis which are often laborious and unsuitable for low abundance glycoproteins. When subject to mass spectrometry with collision-induced dissociation, glycopeptides produce characteristic MS/MS spectra that can be detected by visual inspection. However, given the high volume of data output from proteome studies today, manually searching for glycopeptides is an impractical task. In this thesis, we present a tool to automate the identification of glycopeptide spectra from MS/MS data. Further, we discuss some methodologies to automate the elucidation of the structure of the carbohydrate moiety of glycopeptides by adapting traditional MS/MS ion searching techniques employed in peptide sequence determination. MS/MS ion searching, a common technique in proteomics, aims to interpret MS/MS spectra by correlating structures from a database to the patterns represented in the spectrum. The tool was tested on high throughput proteomics data and was shown to identify 97% of all glycopeptides present in the test data. Further, the tool assigned correct carbohydrate structures to many of these glycopeptide MS/MS spectra. Applications of the tool in a proteomics environment for the analysis of glycopeptide expression in cancer tissue are also be presented.

Master of Science / Department of Computing and Information Sciences / Mitchell L. Neilsen / This research is a part of BREAD PHENO, a PhenoApps BREAD project at K-State which combines contemporary advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. In this platform, novel image analysis segmentation algorithms are being developed to model and extract plant phenotypes. As a part of this research, the traditional Watershed segmentation algorithm has been extended and the primary goal is to accurately count and characterize the seeds in an image. The new approach can be used to characterize a wide variety of crops. Further, this algorithm is migrated into Android making use of the Android APIs and the first ever user-friendly Android application implementing the extended Watershed algorithm has been developed for Mobile field-based high-throughput phenotyping (HTP).

No description available.

In vitro protein selection has had major impacts in the field of protein engineering. Traditional screens assay individual proteins for specific function. Selection, however, analyzes a pool of mutants and yields the best variants. Phage display, a successful selection technique, also provides a reliable link between variant phenotype and genotype. It can also be coupled with high throughput sequencing to map protein mutations; potentially highlighting vital mutations in variants. We propose to apply this technique to cancer therapy. RAF, a serine/threonine kinase, is critical for cell regulation in mammals. RAF can be activated by oncogenic RAS, found in over 30% of cancers, to drive cancer proliferation. Rigosertib, a benzyl styryl sulfone in phase III clinical trials for myelodysplastic syndrome (MDS), is an inhibitor of the RAS binding domain (RBD) in RAF. Phage display can be used to select RAF mutants for RAS binding affinity, in the presence of Rigosertib. High-throughput sequencing of these variants can provide a means of anticipating, and mapping resistance to this anti-cancer drug.

In this thesis, a highly comparative framework for time-series analysis is developed. The approach draws on large, interdisciplinary collections of over 9000 time-series analysis methods, or operations, and over 30 000 time series, which we have assembled. Statistical learning methods were used to analyze structure in the set of operations applied to the time series, allowing us to relate different types of scientific methods to one another, and to investigate redundancy across them. An analogous process applied to the data allowed different types of time series to be linked based on their properties, and in particular to connect time series generated by theoretical models with those measured from relevant real-world systems. In the remainder of the thesis, methods for addressing specific problems in time-series analysis are presented that use our diverse collection of operations to represent time series in terms of their measured properties. The broad utility of this highly comparative approach is demonstrated using various case studies, including the discrimination of pathological heart beat series, classification of Parkinsonian phonemes, estimation of the scaling exponent of self-affine time series, prediction of cord pH from fetal heart rates recorded during labor, and the assignment of emotional content to speech recordings. Our methods are also applied to labeled datasets of short time-series patterns studied in temporal data mining, where our feature-based approach exhibits benefits over conventional time-domain classifiers. Lastly, a feature-based dimensionality reduction framework is developed that links dependencies measured between operations to the number of free parameters in a time-series model that could be used to generate a time-series dataset.

The homologous recombination (HR) pathway of DNA repair is essential for the faithful repair of double-stranded DNA breaks (DSBs) in all organisms and as such helps maintain genomic stability. Furthermore, HR is instrumental in the cellular response to exogenous DNA damaging agents such as those used in the clinic for chemo- and radiotherapy. HR in humans is a complex, incompletely understood process involving numerous stages and diverse biochemical activities. Advancing our knowledge of the HR pathway in humans aids the understanding of how chemo- and radiotherapies act and may be used to develop novel therapeutic strategies. Recent studies have identified inhibition of HR as one of the mechanisms via which a number of recently developed chemotherapeutics have their effect. Accordingly, the clinical potential of HR inhibitors is under investigation. My work has centred around the identification of both novel HR proteins and novel, small molecule HR inhibitors. To further these aims, I have successfully employed high-throughput RNAi and small molecule screening strategies. RNAi screens are commonly used to identify genes involved in a given cellular process via genetic loss of function, whilst small molecule, cell based screens are a powerful tool in the drug discovery process.

The goal of the work presented herein was to further our understanding of the rumen microbiota and microbiome of wild moose, and to use that understanding to improve other processes. The moose has adapted to eating a diet of woody browse, which is very high in fiber, but low in digestibility due to the complexity of the plant polysaccharides, and the presence of tannins, lignin, and other plant-secondary compounds. Therefore, it was hypothesized that the moose would host novel microorganisms that would be capable of a wide variety of enzymatic functions, such as improved fiber breakdown, metabolism of digestibility-reducing or toxic plant compounds, or production of functional metabolites, such as volatile fatty acids, biogenic amines, etc. The first aim, naturally, was to identify the microorganisms present in the rumen of moose, in this case, the bacteria, archaea, and protozoa. This was done using a variety of high-throughput techniques focusing on the SSU rRNA gene (see CHAPTERS 2-5). The second aim was to culture bacteria from the rumen of the moose in order to study their biochemical capabilities (see CHAPTERS 6-7). The final aim was to apply those cultured bacterial isolates to improve other systems. Specifically, bacteria from the rumen of the moose was introduced to young lambs in order to colonize the digestive tract, speed the pace of rumen development, and improve dietary efficiency (see CHAPTER 8).

The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well.