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Geobacillus thermoglucosidasius is a Gram-positive thermophilic eubacterium (45-70‰) that has the ability to convert pre-treated lignocellulosic material LCM into ethanol. This organism has been genetically engineered such that its yield of ethanol production is in excess of 90% of the theoretical maximum [38]. There remains considerable scope to develop G.thermoglucosidasius to produce alternative fuels and chemicals of industrial importance. For such a useful bacterium the understanding of the global metabolism remains poorly characterised. To gain a better insight into the metabolic pathways and capabilities of G. thermoglucosidasius a bottom-up approach to construct a comprehensive metabolic model of the organism was applied. The model was build from manually annotated genome and incorporates data from wet lab experiments for accurate in silico analyses. The model simulations has highlighted a potential experimental design for the in silico production of succinate and butane-2,3-diol. PathwayBooster is also introduced in this study as a tool for curating metabolic pathways. The methodology is based on the assumption that the core metabolic capabilities are shared among evolutionarily closely related species [80]. This approach led to the further analysis of members of the genus Geobacillus with respect to their core metabolic capabilities, genome re-arrangements and shared unique features. Theoretical route for the biosynthesis of Vitamin B12 is presented here, which is novel to the canonical aerobic and anaerobic pathways known to date and ubiquitous amongst Geobacillus spp. The analysis of the gene assignment for this bacterium has highlighted the presence of NADP-dependent GAPDH. The theoretical function of this novel and previously uncategorised enzyme in the genus Geobacillus has been confirmed through enzymatic assays.

Genes can be associated in numerous ways, e.g. by co-expression in micro-arrays, co-regulation in operons and regulons or co-localization on the genome. Association of genes often indicates that they contribute to a common biological function, such as a pathway. The aim of this thesis is to predict metabolic pathways from associated enzyme-coding genes. The prediction approach developed in this work consists of two steps: First, the reactions are obtained that are carried out by the enzymes coded by the genes. Second, the gaps between these seed reactions are filled with intermediate compounds and reactions. In order to select these intermediates, metabolic data is needed. This work made use of metabolic data collected from the two major metabolic databases, KEGG and MetaCyc. The metabolic data is represented as a network (or graph) consisting of reaction nodes and compound nodes. Interme- diate compounds and reactions are then predicted by connecting the seed reactions obtained from the query genes in this metabolic network using a graph algorithm. In large metabolic networks, there are numerous ways to connect the seed reactions. The main problem of the graph-based prediction approach is to differentiate biochemically valid connections from others. Metabolic networks contain hub compounds, which are involved in a large number of reactions, such as ATP, NADPH, H2O or CO2. When a graph algorithm traverses the metabolic network via these hub compounds, the resulting metabolic pathway is often biochemically invalid. In the first step of the thesis, an already existing approach to predict pathways from two seeds was improved. In the previous approach, the metabolic network was weighted to penalize hub compounds and an extensive evaluation was performed, which showed that the weighted network yielded higher prediction accuracies than either a raw or filtered network (where hub compounds are removed). In the improved approach, hub compounds are avoided using reaction-specific side/main compound an- notations from KEGG RPAIR. As an evaluation showed, this approach in combination with weights increases prediction accuracy with respect to the weighted, filtered and raw network. In the second step of the thesis, path finding between two seeds was extended to pathway prediction given multiple seeds. Several multiple-seed pathay prediction approaches were evaluated, namely three Steiner tree solving heuristics and a random-walk based algorithm called kWalks. The evaluation showed that a combination of kWalks with a Steiner tree heuristic applied to a weighted graph yielded the highest prediction accuracy. Finally, the best perfoming algorithm was applied to a microarray data set, which measured gene expression in S. cerevisiae cells growing on 21 different compounds as sole nitrogen source. For 20 nitrogen sources, gene groups were obtained that were significantly over-expressed or suppressed with respect to urea as reference nitrogen source. For each of these 40 gene groups, a metabolic pathway was predicted that represents the part of metabolism up- or down-regulated in the presence of the investigated nitrogen source. The graph-based prediction of pathways is not restricted to metabolic networks. It may be applied to any biological network and to any data set yielding groups of associated genes, enzymes or compounds. Thus, multiple-end pathway prediction can serve to interpret various high-throughput data sets.

Background Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a “global” carotenoid network – comprising of all known enzymatic reactions among naturally occurring carotenoids – with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. Results We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network – compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. Conclusions The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.

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Research in our lab has predicted hundreds of bacterial genes that influence nine different traits in the fruit fly, Drosophila melanogaster. As a practical alternative to creating site-directed mutants for each of the predicted genes, we created an arrayed transposon insertion library using a strain of Acetobacter fabarum DsW_054 isolated from fruit flies. Creation of the Acetobacter fabarum DsW_054 gene knock-out library was done through random transposon insertion, combinatorial mapping and Illumina sequencing. Successful mapping of transposon insertion was achieved for 6418 mutants with hits within 63% of annotated genes within Acetobacter fabarum DsW_054. Insertion sites were verified in 40 mutants through arbitrary PCR and sequencing. To test the utility of the library, genes were selected from MGWAS results on host colonization which show LPS pathway enrichment in the significant gene predicctions. Genes upstream of Lipid-A creation show significant differences in host colonization whereas downstream genes show no effect. In addition, genes were selected from MGWAS results on Drosophila starvation resistance which show Methionine/Cysteine synthesis, Cobalamin synthesis, and Biotin synthesis pathway enrichment. Under our experimental conditions we could not verify influence of these pathways on host starvation resistance. However, they do appear to influence host colonization abundance. This transposon insertion mutant library will be useful for ongoing research in our lab as well as any field studying Acetobacter species, such as other insect microbiome and fermentation research.

Unusual Fe-rich mineral formations were collected from the Calvert Cliffs area of Maryland. Surficial features such as wire-like filaments and columnar “chimneys” indicated a potential biological origin for the samples. Reference samples were collected from an Fe-rich acid mine drainage site near Carbondale, IL to serve as a comparison. The Chesapeake Bay samples were subjected to X-ray diffraction analysis, Scanning Electron Microscope-Electron Dispersive Spectroscopy analysis and Next-Generation Sequencing microbial assay. Minor ferrihydrite in the surficial wires and extensive microcrystalline goethite throughout the rest of the samples indicates a relatively recent origin. The small particle size and unusual microscale morphologies of iron (oxy)hydroxides and the presence of birnessite suggest that microbial metabolism was involved in the formation of these Fe minerals. EDS data indicate a strong relationship between Fe and C, as well as between Fe and P, while a lack of inorganic phosphate and carbonate minerals also indicates biological input. Genetic analysis reveals distinct internal and external microbial communities and the most common taxon within the sample interior was a novel bacterial phylum, indicating the mineralization may be a product of previously undescribed metabolic pathways. The presence of SO4- reducing, nitrogen-reducing and Fe-oxidizing bacteria as described by NGS analysis lends support to a microbially-mediated origin. Microbially driven oxidation of Fe and minor Mn into metal hydroxides is the proposed formation mechanism.

Solution nuclear magnetic resonance (NMR) spectroscopy is a valuable analytical technique that is nondestructive, highly reproducible, and relatively quick to identify and quantify many chemical compounds. Quantitative NMR is a technique commonly used in many medical applications such as drug analysis, metabolomics, and protein-nanoparticle (P-NP) interactions. The most common technique used is the proton (1H) NMR experiment. The 1H NMR analysis provides a quick snapshot of the interested compounds in solution. However, as the compounds become more complex the spectrum becomes overpopulated. This dissertation focuses on various quantitative NMR techniques applied to metabolic and protein competition studies. Specifically, we investigated the effect of biochar on Escherichia coli (E. coli) growth to provide insight on how the metabolic pathways were influenced with the addition of biochar in the RPMI media. A 1H NMR spectrum was recorded at various time points to monitor the metabolic changes over time as E. coli grew in the presence and absence of biochar. The spectra were compared to an in-house metabolite library to identify and quantify the metabolic changes in E. coli. To enhance our metabolic library analysis, we utilized a pure shift analysis attached to the TOCSY pulse program to deconvolute spin systems by using a second dimension for analysis. DIPSI-PSYCHE TOCSY was applied to investigate a metabolite mixture sample and Streptococcus pneumoniae (S. pneumoniae) extracellular metabolites to better resolve the spin systems that significantly overlap each other in the 1H NMR spectra. Our novel approach suggests that adding a pure shift to the TOCSY pulse program is extremely beneficial to investigate various metabolic profiles. Finally, we investigated the protein competition to the AuNP surfaces using a 2D 1H-15N HSQC pulse program. Specifically, we used 1H-15N HSQC technique to quantify the binding capacity for each protein to the AuNP surface before we investigated the competition of two proteins, GB3-Ubq (model protein mixture) or AM-R2ab (biofilm forming protein mixture) to the surface. We also employed a model to study the kinetics of the protein competition to the surface. Our model suggests that GB3-Ubq does not specifically behave kinetically but AM-R2ab is strictly kinetically controlled.

No description available.

MiRNAs são pequenas moléculas de RNAs endógenos não codificantes com aproximadamente 23nt que atuam na regulação da expressão gênica. A sua função é inibir a tradução de genes transcritos através de um mecanismo que viabiliza a ligação do miRNA com o mRNA alvo levando à inibição da tradução ou a degradação do RNA mensageiro. Estudos evidenciam a relação dos miRNAs com diversos processos biológicos como proliferação celular, diferenciação, desenvolvimento e doenças. Uma vez que estão envolvidos na regulação gênica, também alteram as vias metabólicas. Atualmente, as ferramentas computacionais disponíveis para o estudo dos miRNAs são o miRBase, microCosm, o miRGen e o miRNAmap. Elas possuem informações sobre as sequências dos miRNAs, genes alvos e sobre elementos que estão próximos à região dos miRNAs. Embora o avanço até o momento, não existia que relacionasse os miRNAs com as vias metabólicas, para isso foi construída a plataforma miRNApath que auxilia no estudo da função dos miRNAs por meio da análise do seus alvos dentro vias metabólicas. De modo semelhante, também não existia uma abordagem que relacione dados de expressão miRNAs e seus alvos dentro de um mesmo experimento. Para tanto, neste trabalho foi feita uma abordagem utilizando bibliotecas de SAGE (Serial Analysis of Gene Expression) que será incorporada no miRNApath. O miRNApath encontra-se disponível em http://lgmb.fmrp.usp.br/mirnapath. / MiRNAs are small molecules of endogenous non-coding RNAs with approximately 23nt in length that acts over gene expression regulation. Its function is inhibit the translation of gene transcripts through a mechanism that links the miRNA with its mRNA target leading to a translational repression or degradation. Studies show the relation of RNAs in many biological processes like cell proliferation, dierentiation and development of diseases. Since they are involved in gene regulation, they also change the metabolic pathways. Currently, the available computational tools for the study of miRNAs are miRBase, microCosm, miRGen and miRNAmap. They have information about miRNAs sequences, targets and features. Despite the the advances, until now, there is no tool that correlates the miRNAs with metabolic pathways, therefore we developed the miRNApath platform that helps in the analysis of miRNAs function through the study of its targets that are into the metabolic pathway. In the same way, there is no approach that put together information of expression of miRNAs and its targets in the same experiment. In this work we develop an approach with SAGE (Serial Analysis of Gene Expression ) libraries that will be integrated to miRNApath. The plataform is avaible at http://lgmb.fmrp.usp.br/mirnapath.