Terpenes are secondary metabolites produced by plants and they have promising roles in plant defense and pharmaceuticals. They are synthesized by terpene synthases and these enzymes are part of a complex plant metabolic pathway. Diterpene biosynthesis requires co-expression of class II and class I diterpene synthases (diTPSs) to convert geranylgeranyl diphosphate (GGPP), the common precursor, into a C20 intermediate substrate. These substrates then use cytochrome p450s (CYPs) as their final steps to form diterpene scaffolds. CYPs are monooxygenases that change the redox status of their substrates into final diterpene products. Medicago truncatula was used as my model organism to investigate how legumes synthesize these secondary metabolites to contribute to crop defense improvement in the future. Seven diTPSs - MtTPS17, MtTPS18, MtTPS19, MtTPS37, MtTPS38, MtTPS39, and MtTPS40 - in M. truncatula have been identified. MtTPS38 was found to produce ent-CPP and MtTPS37 used ent-CPP to yield ent-kaurene. Combinatorial expression showed that MtTPS38 and MtTPS37 react together to produce ent-kaurene, a precursor for an important plant hormone gibberellin (GA). CYPs have also been discovered to be clustered around MtTPS19, suggesting the possibility of MtTPS19 utilizing these CYPs for downstream reactions.

Indiana University-Purdue University Indianapolis (IUPUI) / Canines can detect different diseases simply by smelling different biological sample types, including urine, breath and sweat. This has led researchers to try and discovery unique volatile organic compound (VOC) biomarkers. The power of VOC biomarkers lies in the fact that one day they may be able to be utilized for noninvasive, rapid and accurate diagnostics at a point of care using miniaturized biosensors. However, the identity of the specific VOC biomarkers must be demonstrated before designing and fabricating sensing systems. Through an extensive series of experiments, VOCs in urine are profiled by solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS) to identify biomarkers for breast cancer using murine models. The results from these experiments indicated that unique classes of urinary VOCs, primarily terpene/terpenoids and carbonyls, are potential biomarkers of breast cancer. Through implementing chemometric approaches, unique panels of VOCs were identified for breast cancer detection, identifying tumor location, determining the efficacy of dopaminergic antitumor treatments, and tracking cancer progression. Other diseases, including COVID-19 and hypoglycemia (low blood sugar) were also probed to identify volatile biomarkers present in breath samples. VOC biomarker identification is an important step toward developing portable gas sensors, but another hurdle that exists is that current sensors lack selectivity toward specific VOCs of interest. Furthermore, testing sensors for sensitivity and selectivity is an extensive process as VOCs must be tested individually because the sensors do not have modes of chromatographic separation or compound identification. Another set of experiments is presented to demonstrate that SPME fibers can be coated with materials, used to extract standard solutions of VOCs, and analyzed by GC-MS to determine the performance of various gas sensing layers. In the first of these experiments, polyetherimide (PEI) was coated onto a SPME fiber and compared to commercial polyacrylate (PAA) fibers. The second experiment tuned the extraction efficiency of polyvinylidene fluoride (PVDF) - carbon black (CB) composites and showed that they had higher sensitivity for urinary VOC extraction relative to a polydimethylsiloxane (PDMS) SPME fiber. These results demonstrate SPME GC-MS can rapidly characterize and tune the VOC adsorption capabilities of gas sensing layers.

Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time alignment is one of the most important yet challenging preprocessing steps, in order to ensure that ion intensity measurements among multiple LC-MS runs are comparable. In this dissertation, we propose a Bayesian alignment model (BAM) for analysis of LC-MS data. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and provides estimates of the retention time variability along with uncertainty measures, enabling a natural framework to integrate information of various sources. From methodology development to practical application, we investigate the alignment problem through three research topics: 1) development of single-profile Bayesian alignment model, 2) development of multi-profile Bayesian alignment model, and 3) application to biomarker discovery research. Chapter 2 introduces the profile-based Bayesian alignment using a single chromatogram, e.g., base peak chromatogram from each LC-MS run. The single-profile alignment model improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler using a block Metropolis-Hastings algorithm, and 2) an adaptive mechanism for knot specification using stochastic search variable selection (SSVS). Chapter 3 extends the model to integrate complementary information that better captures the variability in chromatographic separation. We use Gaussian process regression on the internal standards to derive a prior distribution for the mapping functions. In addition, a clustering approach is proposed to identify multiple representative chromatograms for each LC-MS run. With the Gaussian process prior, these chromatograms are simultaneously considered in the profile-based alignment, which greatly improves the model estimation and facilitates the subsequent peak matching process. Chapter 4 demonstrates the applicability of the proposed Bayesian alignment model to biomarker discovery research. We integrate the proposed Bayesian alignment model into a rigorous preprocessing pipeline for LC-MS data analysis. Through the developed analysis pipeline, candidate biomarkers for hepatocellular carcinoma (HCC) are identified and confirmed on a complementary platform. / Ph. D.

The purpose of this study was to find an alternative instrumental method to sensory analysis and to further investigate the aroma properties of spoiling blue crab meat. This was accomplished by use of a Cyranose 320™ Electronic Nose, Draeger-Tubes®, and solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS). These techniques were compared to the more established techniques for determining quality of blue crab meat of sensory and microbiological analysis. Three different electronic nose methods were used to evaluate five sequentially spoiled groups of crab meat. The manufacturer's recommended setup only resulted in a 30 % correct separation of the known groups, and only 10 % of the samples were correctly identified when coded unknown samples were used to validate the electronic nose training results. The compressed air method which utilized compressed tank breathing air, filtered through activated carbon and moisture traps resulted in 100 % separation of the known groups, but only correctly identified 20 % of the coded unknown samples. Draeger-Tubes® were found to be more accurate and precise compared with the electronic nose. All 5 groups of samples analyzed using Draeger-Tubes® were found to be significantly different at α = 0.05 using a Tukey-Kramer ANOVA statistical procedure. The coded unknown samples were correctly identified at a rate of 83 %. The simplicity and rapidness of this procedure allows it to possibly be an alternative for the crab industry as an alternative to sensory analysis. SPME-GC-MS found trimethylamine (TMA), ammonia, and indole to best correlate with spoilage of blue crab meat. TMA was found to be sensitive to the minor changes in the early stages (0 - 4 days of refrigerated storage) of spoilage for blue crab meat. Indole corresponded well with sensory results, which suggests that indole may be a promising indicator for detecting early, mid, and highly spoiled samples. It is feasible that these methods can be applied to other crustaceans to determine spoilage level. / Master of Science in Life Sciences

Identification of disease-related alterations in molecular and cellular mechanisms may reveal useful biomarkers for human diseases including cancers. High-throughput omic technologies for identifying and quantifying multi-level biological molecules (e.g., proteins, glycans, and metabolites) have facilitated the advances in biological research in recent years. Liquid (or gas) chromatography coupled with mass spectrometry (LC/GC-MS) has become an essential tool in such large-scale omic studies. Appropriate LC/GC-MS data preprocessing pipelines are needed to detect true differences between biological groups. Challenges exist in several aspects of MS data analysis. Specifically for biomarker discovery, one fundamental challenge in quantitation of biomolecules is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based omic studies. Purification of mass spectometric data is highly desired prior to subsequent differential analysis. In this research dissertation, we majorly target at addressing the purification problem through probabilistic modeling. We propose an intensity-level purification model (IPM) to computationally purify LC/GC-MS based cancerous data in biomarker discovery studies. We further extend IPM to scan-level purification model (SPM) by considering information from extracted ion chromatogram (EIC, scan-level feature). Both IPM and SPM belong to the category of topic modeling approach, which aims to identify the underlying "topics" (sources) and their mixture proportions in composing the heterogeneous data. Additionally, denoise deconvolution model (DMM) is proposed to capture the noise signals in samples based on purified profiles. Variational expectation-maximization (VEM) and Markov chain Monte Carlo (MCMC) methods are used to draw inference on the latent variables and estimate the model parameters. Before we come to purification, other research topics in related to mass spectrometric data analysis for cancer biomarker discovery are also investigated in this dissertation. Chapter 3 discusses the developed methods in the differential analysis of LC/GC-MS based omic data, specifically for the preprocessing in data of LC-MS profiled glycans. Chapter 4 presents the assumptions and inference details of IPM, SPM, and DDM. A latent Dirichlet allocation (LDA) core is used to model the heterogeneous cancerous data as mixtures of topics consisting of sample-specific pure cancerous source and non-cancerous contaminants. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum and tissue proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis. Chapter 5 elaborates these applications in cancer biomarker discovery, where typical single omic and integrative analysis of multi-omic studies are included. / Ph. D. / This dissertation documents the methodology and outputs for computational deconvolution of heterogeneous omics data generated from biospecimens of interest. These omics data convey qualitative and quantitative information of biomolecules (e.g., glycans, proteins, metabolites, etc.) which are profiled by instruments named liquid (or gas) chromatography and mass spectrometer (LC/GC-MS). In the scenarios of biomarker discovery, we aim to find out the significant difference on intensities of biomolecules with respect to two specific phenotype groups so that the biomarkers can be used as clinical indicators for early stage diagnose. However, the purity of collected samples constitutes the fundamental challenge to the process of differential analysis. Instead of experimental methods that are costly and time-consuming, we treat the purification task as one of the topic modeling procedures, where we assume each observed biomolecular profile is a mixture of hidden pure source together with unwanted contaminants. The developed models output the estimated mixture proportion as well as the underlying “topics”. With different level’s purification applied, improved discrimination power of candidate biomarkers and more biologically meaningful pathways were discovered in LC/GC-MS based multi-omic studies for liver cancer. This research work originates from a broader scope of probabilistic generative modeling, where rational assumptions are made to characterize the generation process of the observations. Therefore, the developed models in this dissertation have great potential in applications other than heterogeneous data purification discussed in this dissertation. A good example is to uncover the relationship of human gut microbiome with the host’s phenotypes of interest (e.g., disease like type-II diabetes). Similar challenges exist in how to infer the underlying intestinal flora distribution and estimate their mixture proportions. This dissertation also covers topics of related data preprocessing and integration, but with a consistent goal in improving the performance of biomarker discovery. In summary, the research help address sample heterogeneity issue observed in LC/GC-MS based cancer biomarker discovery studies and shed light on computational deconvolution of the mixtures, which can be generalized to other domains of interest.

No / There is increasing evidence for complexity in mortuary practices in Britain during the Roman period. One class of burials demonstrates an association between inhumation in stone sarcophagi or lead-lined coffins, 'plaster' coatings, textile shrouds and natural resins. It has been suggested that this 'package' represents a deliberate attempt at body preservation. Fragments with a resinous appearance found in one such burial from Arrington, Cambridgeshire, UK were analysed using gas-chromatography-mass spectrometry. The triterpenic compounds identified are biomarkers for the genus Pistacia and provide the first chemical evidence for an exotic resin in a mortuary context in Roman Britain. / AHRC

No / Gas chromatography–mass spectrometry (GC–MS) and bulk carbon isotope determinations have been performed on samples (‘cemented organic residues’, charcoal, sediment and fire-cracked rock) excavated from 12 slab-lined pits from various locations in Arctic Norway to test the premise that these archaeological features were used for the extraction of oil from the blubber of marine mammals, such as seal, whale and walrus. A wide range of lipid compound classes were detected especially in the cemented organic residues and in the charcoal samples. The presence of long-chain unsaturated and isoprenoid fatty acids together with oxidation and thermal alteration products of unsaturated acids such as dicarboxylic acids, dihydroxyfatty acids and ω-(o-alkylphenyl)alkanoic acids suggests that these features were used for marine oil extraction at elevated temperatures. Notably the location of the hydroxyl groups in the dihydroxyfatty acids provides a record of the positional isomer of the precursor fatty acid and allows confirmation that 11-docosenoic (cetoleic) acid, the most abundant C22:1 isomer in marine oil, was a major component of the original lipid. Further information was provided by the presence of long-chain fatty acyl moieties in surviving triacylglycerols and the presence of cholesterol. A fungal metabolite, mycose (trehalose), was found in all samples apart from a fire-cracked rock and points to microbiological activity in the pits. Bulk isotope analysis conducted on the ‘cemented organic residues’ is consistent with modern reference samples of blubber and oil from seal and whale. These data provide clear analytical evidence of the function of slab-lined pits in the archaeological record and suggest widespread exploitation of marine mammals for producing oil for heating, lighting and myriad other uses in the past.

Metabolomics aims at the detection and quantitation of metabolites within a biological system. As the most direct representation of phenotypic changes, metabolomics is an important component in system biology research. Recent development on high-resolution, high-accuracy mass spectrometers enables the simultaneous study of hundreds or even thousands of metabolites in one experiment. Liquid chromatography-mass spectrometry (LC-MS) is a commonly used instrument for metabolomic studies due to its high sensitivity and broad coverage of metabolome. However, the identification of metabolites remains a bottle-neck for current metabolomic studies. This thesis focuses on utilizing computational approaches to improve the accuracy and efficiency for metabolite identification in LC-MS/MS-based metabolomic studies. First, an outlier screening approach is developed to identify those LC-MS runs with low analytical quality, so they will not adversely affect the identification of metabolites. The approach is computationally simple but effective, and does not depend on any preprocessing approach. Second, an integrated computational framework is proposed and implemented to improve the accuracy of metabolite identification and prioritize the multiple putative identifications of one peak in LC-MS data. Through the framework, peaks are likely to have the m/z values that can give appropriate putative identifications. And important guidance for the metabolite verification is provided by prioritizing the putative identifications. Third, an MS/MS spectral matching algorithm is proposed based on support vector machine classification. The approach provides an improved retrieval performance in spectral matching, especially in the presence of data heterogeneity due to different instruments or experimental settings used during the MS/MS spectra acquisition. / Master of Science

Orientadores: Felix Guillermo Reyes Reyes, Maria Teresa de Alvarenga Freire / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos / Made available in DSpace on 2018-08-16T23:01:27Z (GMT). No. of bitstreams: 1 Dutra_CamilaBraga_D.pdf: 1974104 bytes, checksum: 7a0aae96f3654e27fb7d553ee756e522 (MD5) Previous issue date: 2010 / Doutorado / Doutor em Ciência de Alimentos

Understanding the complex metabolic networks present in organisms, through the use of high throughput liquid chromatography coupled mass spectrometry, will give insight into the physiological changes responding to stress. However the lack of a proper work flow and robust methodology hinders verifiable biological interpretation of mass profiling data. In this study a novel workflow has been developed. A novel Kernel based feature alignment algorithm, which outperformed Agilent’s Mass profiler and showed roughly a 20% increase in alignment accuracy, is presented for the alignment of mass profiling data. Prior to statistical analysis post processing of data is carried out in two stages, noise filtering is applied to consensus features which were aligned at a 50% or higher rate. Followed by missing value imputation a method was developed that outperforms both at model recovery and false positive detection. The use of parametric methods for statistical analysis is inefficient and produces a large number of false positives. In order to tackle this three non-parametric methods were considered. The histogram method for statistical analysis was found to yield the lowest false positive rate. Data is presented which was analysed using these methods to reveal metabolomic changes during plant pathogenesis. A high resolution time series dataset was produced to explore the infection of Arabidopsis thaliana by the (hemi) biotroph Pseudomonas syringe pv tomato DC3000 and its disarmed mutant DC3000hrpA, which is incapable of causing infection. Approximately 2000 features were found to be significant through the time series. It was also found that by 4h the plants basal defence mechanism caused the significant ‘up-regulation’ of roughly 400 features, of which 240 were found to be at a 4-fold change. The identification of these features role in pathogenesis is supported by the fact that of those features found to discriminate between treatments a number of pathways were identified which have previously been documented to be active due to pathogenesis