Microbial and non-microbial volatile fingerprints : potential clinical applications of electronic nose for early diagnoses and detection of diseases

This is the first study to explore the potential applications of using qualitative volatile fingerprints (electronic nose) for early detection and diagnosis of diseases such as dermatophytosis, ventilator associated pneumonia and upper gastrointestinal cancer. The investigations included in vitro analysis of various dermatophyte species and strains, antifungal screening, bacterial cultures and associated clinical specimens and oesophageal cell lines. Mass spectrometric analyses were attempted to identify possible markers. The studies that involved e-nose comparisons indicated that the conducting polymer system was unable to differentiate between any of the treatments over the experimental period (120 hours). Metal oxide-based sensor arrays were better suited and differentiated between four dermatophyte species within 96 hours of growth using principal component analysis and cluster analysis (Euclidean distance and Ward’s linkage) based on their volatile profile patterns. Studies on the sensitivity of detection showed that for Trichophyton mentagrophytes and T. rubrum it was possible to differentiate between log3, log5 and log7 inoculum levels within 96 hours. The probabilistic neural network model had a high prediction accuracy of 88 to 96% depending on the number of sensors used. Temporal volatile production patterns studied at a species level for a Microsporum species, two Trichophyton species and at a strain level for the two Trichophyton species; showed possible discrimination between the species from controls after 120 hours. The predictive neural network model misclassified only one sample. Data analysis also indicated probable differentiation between the strains of T. rubrum while strains of T. mentagrophytes clustered together showing good similarity between them. Antifungal treatments with itraconazole on T. mentagrophytes and T. rubrum showed that the e-nose could differentiate between untreated fungal species from the treated fungal species at both temperatures (25 and 30°C). However, the different antifungal concentrations of 50% fungal inhibition and 2 ppm could not be separated from each other or the controls based on their volatiles. Headspace analysis of bacterial cultures in vitro indicated that the e-nose could differentiate between the microbial species and controls in 83% of samples (n=98) based on a four group model (gram-positive, gram-negative, fungi and no growth). Volatile fingerprint analysis of the bronchoalveolar lavage fluid accurately separated growth and no growth in 81% of samples (n=52); however only 63% classification accuracy was achieved with a four group model. 12/31 samples were classified as infected by the e-nose but had no microbiological growth, further analysis suggested that the traditional clinical pulmonary infection score (CPIS) system correlated with the e-nose prediction of infection in 68% of samples (n=31). No clear distinction was observed between various human cell lines (oesophageal and colorectal) based on volatile fingerprints within one to four hours of incubation, although they were clearly separate from the blank media. However, after 24 hours one of the cell lines could be clearly differentiated from the others and the controls. The different gastrointestinal pathologies (forming the clinical samples) did not show any specific pattern and thus could not be distinguished. Mass spectrometric analysis did not detect distinct markers within the fungal and cell line samples, but potential identifiers in the fungal species such as 3-Octanone, 1-Octen-3-ol and methoxybenzene including high concentration of ammonia, the latter mostly in T. mentagrophytes, followed by T. rubrum and Microsporum canis, were found. These detailed studies suggest that the approach of qualitative volatile fingerprinting shows promise for use in clinical settings, enabling rapid detection/diagnoses of diseases thus eventually reducing the time to treatment significantly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of computational methods for metabolic network analysis based on metabolomics data

The baker';s yeast, Saccharomyces cerevisiae, is a simple eukaryotic organism with approximately 6000 genes. Saccharomyces cerevisiae is an ideal model organism for large-scale functional studies and provides a system in which genes can be systematically inactivated by way of gene-knockout methods. A substantial fraction of the 6000 genes in Saccharomyces cerevisiae encode proteins for which currently we do not know any confirmed or putative function. Prediction of the functional role of these proteins is a challenging problem in systems biology, especially as many of these genes have no overt phenotypes. In our study, we aim at a better understanding of the underlying functional relationships between genes working across diverse metabolic pathways using intracellular metabolite profiling studies. We applied bioinformatics methods and statistical analysis techniques in combination with metabolic profiling to understand the function and the regulatory mechanisms of specific genes involved in central carbon metabolism and amino acid biosynthesis. The experimental work was carried out by the group of Prof. Elmar Heinzle (Biochemical Engineering, Saarland University), our collaboration partner. 13C stable isotope substrates can be used as tracers to generate detailed metabolic profiles of gene knockouts. Detailed and quantitative information on the physiological cellular states is measured by 13C -metabolic profiling of cultures grown on novel high throughput oxygen sensor microtiter plates. In this dissertation, we worked towards developing systematic approaches for study of Saccharomyces cerevisiae genes of unknown function based on the metabolic profiles of knockout mutants under varied environmental conditions. In the first step, we have developed a software tool called CalSpec for automation of Gas Chromatography Mass Spectrometry data acquisition and analysis routine, as this is a bottleneck in the metabolic profiling studies. In the next step, we worked on large scale statistical analysis of metabolic profiling data. We applied various algorithms for finding closely related mutants which show similar metabolic profiles. According to our hypothesis, similarity in the metabolic profiles can be used to find functionally linked genes. Saccharomyces cerevisiae is known to be robust to majority of genetic perturbations. In these cases where the mutants show no overt 4 phenotypes, we developed a sensitive outlier detection method to detect those subsets of metabolic profile features which are most differentiating (outliers) for all mutants. The second part of this dissertation involves developing computational tools for metabolic pathway analysis on the basis of genome scale metabolic models, as well as integration of various newly emerging experimental techniques. In recent years, genome scale metabolic models have been and are continuing to be assembled for various organisms. In the year 2003, first comprehensive genome scale metabolic model for yeast became publicly available. With the emergence of system biology area of research, diverse computational approaches have been developed. In this work, we developed a new webserver called MetaModel, for analysis of genome scale metabolic networks of eukaryotic organisms

Inferring structures, free energy differences, and kinetic rates of biological macromolecular assemblies by integrative modeling

Biological macromolecular assemblies play crucial roles in most cellular processes. The determination of their structures, thermodynamics, and kinetics is essential to understand their function, evolution, modulation, and design. Determining such models, however, remains challenging. One particularly powerful approach to constructing models in general is integrative modeling. Integrative modeling aims to maximize the accuracy, precision, and completeness of models, by simultaneously utilizing all available information, including experimental data, physical principles, statistical analyses, and other prior models. The goal of this thesis is to expand the scope of integrative modeling to the inference of spatial, thermodynamic, and kinetic aspects of macromolecular assemblies. In Chapter I, I introduce the integrative modeling framework for spatiotemporal modeling of biological macromolecular assemblies. In Chapter II, I demonstrate how the synergy between multi-chemistry cross-linking mass spectrometry and integrative modeling can map the structural dynamics of macromolecular assemblies, by application to the human Cop9 signalosome complex. In Chapter III, I present a method for determining structures, free energy differences, and kinetic rates of macromolecular assemblies along their functional cycle, mainly from negative stain electron microscopy (EM). We apply the method to the yeast Hsp90 to estimate the free energy differences and kinetic parameters along its nucleotide hydrolysis cycle, which includes open and closed states of Hsp90. In Chapter IV, I describe a validation of stochastic sampling in integrative modeling. The remaining chapters describe applications of integrative modeling to assemblies of various sizes and scales, using various sources of information, thus illustrating the flexibility of the integrative modeling approach. Specifically, I apply integrative modeling to the human ECM29-Proteasome assembly under oxidative stress (Chapter V), the yeast nuclear pore complex (NPC) cytoplasmic mRNA export platform (Chapter VI), the major membrane ring component of the yeast NPC (Chapter VII), the entire yeast NPC (Chapter VIII), and the reconstruction of 3D structures of MET antibodies (Chapter IX)

Prevotella copri and microbiota members mediate the beneficial effects of a therapeutic food for malnutrition

Microbiota-directed complementary food (MDCF) formulations have been designed to repair the gut communities of malnourished children. A randomized controlled trial demonstrated that one formulation, MDCF-2, improved weight gain in malnourished Bangladeshi children compared to a more calorically dense standard nutritional intervention. Metagenome-assembled genomes from study participants revealed a correlation between ponderal growth and expression of MDCF-2 glycan utilization pathways by Prevotella copri strains. To test this correlation, here we use gnotobiotic mice colonized with defined consortia of age- and ponderal growth-associated gut bacterial strains, with or without P. copri isolates closely matching the metagenome-assembled genomes. Combining gut metagenomics and metatranscriptomics with host single-nucleus RNA sequencing and gut metabolomic analyses, we identify a key role of P. copri in metabolizing MDCF-2 glycans and uncover its interactions with other microbes including Bifidobacterium infantis. P. copri-containing consortia mediated weight gain and modulated energy metabolism within intestinal epithelial cells. Our results reveal structure-function relationships between MDCF-2 and members of the gut microbiota of malnourished children with potential implications for future therapies

QUANTITATIVE CHARACTERIZATION OF PROTEINS AND POST-TRANSLATIONAL MODIFICATIONS IN COMPLEX PROTEOMES USING HIGH-RESOLUTION MASS SPECTROMETRY-BASED PROTEOMICS

Mass spectrometry-based proteomics is focused on identifying the entire suite of proteins and their post-translational modifications (PTMs) in a cell, organism, or community. In particular, quantitative proteomics measures abundance changes of thousands of proteins among multiple samples and provides network-level insight into how biological systems respond to environmental perturbations. Various quantitative proteomics methods have been developed, including label-free, metabolic labeling, and isobaric chemical labeling. This dissertation starts with systematic comparison of these three methods, and shows that isobaric chemical labeling provides accurate, precise, and reproducible quantification for thousands of proteins. Based on these results, we applied this approach to characterizing the proteome of Arabidopsis seedlings treated with Strigolactones (SLs), a new class of plant hormones that modulate various developmental processes. Our study reveals that SLs regulate the expression of a range of proteins that have not been assigned to SL pathways, which provides novel targets for follow-up genetic and biochemical characterization of SL signaling. The same approach was also used to measure how elevated temperature impacts the physiology of individual microbial groups in an acid mine drainage (AMD) microbial community, and shows that related organisms differed in their abundance and functional responses to temperature. Elevated temperature repressed carbon fixation by two Leptospirillum genotypes, whereas carbon fixation was significantly up-regulated at higher temperature by a third member of this genus. Further, we developed a new proteomic approach that harnessed high-resolution mass spectrometry and supercomputing for direct identification and quantification of a broad range of PTMs from an AMD microbial community. We find that PTMs are extraordinarily diverse between different growth stages and highly divergent between closely related bacteria. The findings of this study motivate further investigation of the role of PTMs in the ecology and evolution of microbial communities. Finally, a computational approach has been developed to improve the sensitivity of phosphopeptide identification. Overall, the research presented in the dissertation not only reveals biological insights with existing quantitative proteomics methods, but also develops novel methodologies that open up new avenues in studying PTMs of proteins (e.g. PTM cross-talk)

D3CAS: un algoritmo de clustering para el procesamiento de flujos de datos en spark

En este trabajo se presenta una prueba de concepto de un algoritmo de clustering basado en densidad, denominado D3CAS, el cual fue implementado para ser ejecutado bajo el framework Spark Streaming y que permite el procesamiento de flujos de datos. La principal característica del algoritmo presentado es que es dinámico, es decir selecciona automáticamente el número de clusters del flujo de datos. El algoritmo fue probado datasets de CLUTO, midiendo la calidad de los clusters obtenidos. Los resultados, obtenidos en un ambiente virtualizado, fueron comparados con otro algoritmo de clustering (CluStream), demostrando que D3CAS arroja mejores resultados.XV Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

D3CAS: un algoritmo de clustering para el procesamiento de flujos de datos en spark

En este trabajo se presenta una prueba de concepto de un algoritmo de clustering basado en densidad, denominado D3CAS, el cual fue implementado para ser ejecutado bajo el framework Spark Streaming y que permite el procesamiento de flujos de datos. La principal característica del algoritmo presentado es que es dinámico, es decir selecciona automáticamente el número de clusters del flujo de datos. El algoritmo fue probado datasets de CLUTO, midiendo la calidad de los clusters obtenidos. Los resultados, obtenidos en un ambiente virtualizado, fueron comparados con otro algoritmo de clustering (CluStream), demostrando que D3CAS arroja mejores resultados.XV Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI