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Next generation of sequencing (NGS) technologies have taken life science research into a new era. With the rapid advances in these technologies and the associated reduction in overall costs, the sequencing and assembly of genomes have come within reach of most laboratories. Studies related to the evolution, ecology and biology of an organism now rely heavily on genomic data and obtaining a genome sequence has become an essential resource for the rapid progress and success of these studies. Pantoea ananatis is recognised as an emerging but rather unconventional pathogen capable of infecting a wide range of different hosts. Numerous plants of agricultural and economic importance including maize, rice, onion, pineapple, melon, sudan grass and Eucalyptus trees have been affected. With the outbreak of P. ananatis in a South African Eucalyptus nursery in 1998, it was realised that very little is known about this pathogen. A better understanding of the pathogenicity, metabolism and ecology of the bacterium is required to develop strategies for the control of the disease. During this study, the genome sequence of P. ananatis strain LMG 20103 was obtained using the Roche 454 technology. To aid in the assembly of this Eucalyptus pathogen’s genome sequence, the type strain of P. ananatis LMG 2665 was also sequenced using Illinima’s Genome Analyzer (GA). A draft assembly of P. ananatis LMG 20103, consisting of 117 contigs, was generated after optimization of the Newbler assembly parameters and comparison with other genome assemblies and genomes. This study demonstrated that the assembly could be completed using both in-vitro, and in-silico approaches such as contig scaffolding, gap closure with conventional PCR reactions and sequencing, manual curation and automated genome annotation. The final complete genome consisted of a 4 386 227 bp chromosome and a 317 146 bp mega-plasmid. With the complete genome sequence available, the reconstruction of metabolic network of P. ananatis LMG 20103 was attempted using two pathways reconstruction pipelines namely, Pathway Tools and Model SEED. It was found that missing metabolic reactions and incomplete pathways in the draft metabolic networks were mainly caused by incorrect gene annotations or bioinformatic errors during the automated network reconstruction. These two pipelines differed substantially in the way network reconstruction is undertaken. Performing a comparison between the two proposed networks, annotation errors could be detected and corrected. Although some improvement could be made to the predicted network further experimental data is still required to improve the accuracy of the draft metabolic network. Despite the amount of effort and cost, it is believed that the complete genome and a draft metabolic network of P. ananatis LMG 20103 will be a valuable resource for many subsequent studies to investigate the evolution and biology of this emerging plant pathogen. This information will be essential for the development of strategies to predict and control future disease outbreaks associated with this pathogen. / Dissertation (MSc)--University of Pretoria, 2012. / Microbiology and Plant Pathology / unrestricted

The metabolism of pure cis- and trans- chlordane was studied in vitro. Microsomal preparations from the livers of male rats induced with cis- or trans-chlordane in feed for ten days were used to metabolize the pure compound corresponding to the inducer. Subsequent extraction, column fractionation and combined gas chromatography-mass spectroscopy resulted in the characterization of four compounds not previously reported from an in vitro system. In addition to the substrate, trans-chlordane extracts contained species with the following molecular weights and empirical formulae: m/e 370, c 10 H 5 Cl 7 , heptachlor; m/e 352, c 10 H 6 0C1 6, a hydroxylated chlordene; and m/e 422, c 10 H 6 0C1 8 , a hydroxylated chlordane. Dichlo rochlo rdene, oxychlordane and 1- chloro- 2 -hydroxychlordene chlorohydrin were also present, With the exception of the hydroxychlordane and heptachlor, cis - chlordane extracts contained all of the metabolites found in the trans-incubates. Additionally, a fully saturated compound m/e 372, c 10 H 7 c1 7, a dihydroheptachlor, was present. The 1, 2-trans-dihydrodiol of heptachlor found in previous in vitro incubates of cis-chlordane was not present in this extract. This information has been incorporated into a proposed route for the biotransformation of the chlordanes that offers an explanation for the observed differences in the metabolism of cis - and trans-chlordane . The pathway is based on the reductive dechlorination of the chlordanes through dihydroheptachlor to dihydrochlordene. Parallel pathways of hydroxylation, desaturation and epoxide formation arise at each of these species and at chlordane itself. The trans-isomer is predominantly desaturated or hydroxylated while the cis-isomer mainly undergoes dehaloge nation.

A biological pathway database is a database that describes biochemical pathways, reactions, enzymes that catalyze the reactions, and the substrates that participate in these reactions. A pathway genome database (PGDB) integrates pathway information with information about the complete genome of various sequenced organisms. Two of the popular PGDBs available today are the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaCyc. The proliferation of biological databases in general raises several questions for the life scientist. Which of these databases is most accurate, most current, or most comprehensive? Do they have a standard format? Do they complement each other? Overall, which database should be used for what purpose? If more than one database is deemed relevant, it is desirable to have a unified database containing information from all the shortlisted databases. There is no standard methodology yet for integrating biological pathway databases and, to the best of our knowledge, no commercial software that can perform such integration tasks. While XML based pathway data exchange standards such as BioPAX and SBML are emerging, these do not address the basic problems such as inconsistent nomenclature and substrate matching between databases in the unification of pathway databases. Here, we present the PathMeld methodology to unify KEGG and MetaCyc databases starting from their flat files. Individual PGDBs are transformed into a unified schema that we design. With individual PGDBs in the common unified schema, the key to the PathMeld methodology is to find the entity correspondences between the KEGG and MetaCyc substrates. We present a heuristic driven approach for one-to-one mapping of the substrates between KEGG and MetaCyc. Using the exact name and chemical formula match criteria, 82.6% of the substrates in MetaCyc were matched accurately to corresponding substrates in KEGG. The substrate names in the MetaCyc database contain html tags and non-characters such as <sub>, <sup>, <i>, <l>, &, and $. The MetaCyc chemical formula are stored in lisp format in the database while KEGG stores them as continuous strings. Hence, we subject MetaCyc chemical formulae to transformation into KEGG format to make them directly comparable. Applying pre-processing to transform MetaCyc substrate names and formulae improved substrate matching by 2%. To investigate how many of the remaining 17:4% substrates are indeed absent from KEGG, we employ a standard UNIX based approximate string matching tool called agrep. The resulting matches are curated into four mutually exlusive groups: 3:83% are correct matches, 3:17% are close matches, and 7:45% are incorrect matches. 3:68% of MetaCyc substrate names are not matched at all. This shows that 11:13% of MetaCyc substrate names are absent in KEGG. We note some of the implementation issues we solved. First, parsing only one flat file to populate one database table is not sufficient. Second, intermediate database tables are needed. Third, transformation of substrate names and chemical formula from one of the component databases is required for comparison. Fourth, a biochemist's intervention is needed in evaluating the approximate substrate matches from agrep. In conclusion, the PathMeld methodology successfully uni¯es KEGG and MetaCyc °at ¯le databases into a uni¯ed PostgreSQL database. Matching substrates between databases is the key issue in the uni¯cation process. About 83% of the substrate correspondences can be computationally achieved, while the remaining 17% substrates require approximate matching and manual curation by a biochemist. We presented several di®erent techniques for substrate matching and showed that about 10% of the MetaCyc substrates do not match and hence are absent from KEGG. / Master of Science

RNA-Seq is an increasingly popular approach to transcriptome profiling that uses the capabilities of next generation sequencing technologies and provides better measurement of levels of transcripts and their isoforms. In this thesis, we apply RNA-Seq protocol and transcriptome quantification to estimate gene expression and pathway activity levels. We present a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. In the first version of IsoDE, we compared the tool against four existing methods: Fisher's exact test, GFOLD, edgeR and Cuffdiff on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. We also introduce the second version of IsoDE which runs 10 times faster than the first implementation due to some in-memory processing applied to the underlying gene expression frequencies estimation tool and we also perform more optimization on the analysis. The second part of this thesis presents a set of tools to differentially analyze metabolic pathways from RNA-Seq data. Metabolic pathways are series of chemical reactions occurring within a cell. We focus on two main problems in metabolic pathways differential analysis, namely, differential analysis of their inferred activity level and of their estimated abundance. We validate our approaches through differential expression analysis at the transcripts and genes levels and also through real-time quantitative PCR experiments. In part Four, we present the different packages created or updated in the course of this study. We conclude with our future work plans for further improving IsoDE 2.0.

BACKGROUND:Widely considered probiotic organisms, Bifidobacteria are common inhabitants of the alimentary tract of animals including insects. Bifidobacteria identified from the honey bee are found in larval guts and throughout the alimentary tract, but attain their greatest abundance in the adult hind gut. To further understand the role of Bifidobacteria in honey bees, we sequenced two strains of Bifidobacterium cultured from different alimentary tract environments and life stages.RESULTS:Reflecting an oxygen-rich niche, both strains possessed catalase, peroxidase, superoxide-dismutase and respiratory chain enzymes indicative of oxidative metabolism. The strains show markedly different carbohydrate processing capabilities, with one possessing auxiliary and key enzymes of the Entner-Doudoroff pathway.CONCLUSIONS:As a result of long term co-evolution, honey bee associated Bifidobacterium may harbor considerable strain diversity reflecting adaptation to a variety of different honey bee microenvironments and hive-mediated vertical transmission between generations.

BACKGROUND: Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes. RESULTS: Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint. CONCLUSIONS: We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.

The network-mapping tool integrated with protein database search can be used for filling pathway holes. A metabolic pathway under consideration (pattern) is mapped into a known metabolic pathway (text), to find pathway holes. Enzymes that do not show up in the pattern may be a hole in the pattern pathway or an indication of alternative pattern pathway. We present a data-mining framework for filling holes in the pattern metabolic pathway based on protein function, prosite scan and protein sequence homology. Using this framework we suggest several fillings found with the same EC notation, with group neighbors (enzymes with same EC number in first three positions, different in the fourth position), and instances where the function of an enzyme has been taken up by the left or right neighboring enzyme in the pathway. The percentile scores are better when closely related organisms are mapped as compared to mapping distantly related organisms.

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