Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks

Metabolism is the cellular subsystem responsible for generation of energy from nutrients and production of building blocks for larger macromolecules. Computational and statistical modeling of metabolism is vital to many disciplines including bioengineering, the study of diseases, drug target identification, and understanding the evolution of metabolism. In this thesis, we propose efficient computational methods for metabolic modeling. The techniques presented are targeted particularly at the analysis of large metabolic models encompassing the whole metabolism of one or several organisms. We concentrate on three major themes of metabolic modeling: metabolic pathway analysis, metabolic reconstruction and the study of evolution of metabolism. In the first part of this thesis, we study metabolic pathway analysis. We propose a novel modeling framework called gapless modeling to study biochemically viable metabolic networks and pathways. In addition, we investigate the utilization of atom-level information on metabolism to improve the quality of pathway analyses. We describe efficient algorithms for discovering both gapless and atom-level metabolic pathways, and conduct experiments with large-scale metabolic networks. The presented gapless approach offers a compromise in terms of complexity and feasibility between the previous graph-theoretic and stoichiometric approaches to metabolic modeling. Gapless pathway analysis shows that microbial metabolic networks are not as robust to random damage as suggested by previous studies. Furthermore the amino acid biosynthesis pathways of the fungal species Trichoderma reesei discovered from atom-level data are shown to closely correspond to those of Saccharomyces cerevisiae. In the second part, we propose computational methods for metabolic reconstruction in the gapless modeling framework. We study the task of reconstructing a metabolic network that does not suffer from connectivity problems. Such problems often limit the usability of reconstructed models, and typically require a significant amount of manual postprocessing. We formulate gapless metabolic reconstruction as an optimization problem and propose an efficient divide-and-conquer strategy to solve it with real-world instances. We also describe computational techniques for solving problems stemming from ambiguities in metabolite naming. These techniques have been implemented in a web-based sofware ReMatch intended for reconstruction of models for 13C metabolic flux analysis. In the third part, we extend our scope from single to multiple metabolic networks and propose an algorithm for inferring gapless metabolic networks of ancestral species from phylogenetic data. Experimenting with 16 fungal species, we show that the method is able to generate results that are easily interpretable and that provide hypotheses about the evolution of metabolism.

The genome packaging machinery of dsDNA bacteriophage PRD1

Viral genomes are encapsidated within protective protein shells. This encapsidation can be achieved either by a co-condensation reaction of the nucleic acid and coat proteins, or by first forming empty viral particles which are subsequently packaged with nucleic acid, the latter mechanism being typical for many dsDNA bacteriophages. Bacteriophage PRD1 is an icosahedral, non-tailed dsDNA virus that has an internal lipid membrane, the hallmark of the Tectiviridae family. Although PRD1 has been known to assemble empty particles into which the genome is subsequently packaged, the mechanism for this has been unknown, and there has been no evidence for a separate packaging vertex, similar to the portal structures used for packaging in the tailed bacteriophages and herpesviruses. In this study, a unique DNA packaging vertex was identified for PRD1, containing the packaging ATPase P9, packaging factor P6 and two small membrane proteins, P20 and P22, extending the packaging vertex to the internal membrane. Lack of small membrane protein P20 was shown to totally abolish packaging, making it an essential part of the PRD1 packaging mechanism. The minor capsid proteins P6 was shown to be an important packaging factor, its absence leading to greatly reduced packaging efficiency. An in vitro DNA packaging mechanism consisting of recombinant packaging ATPase P9, empty procapsids and mutant PRD1 DNA with a LacZ-insert was developed for the analysis of PRD1 packaging, the first such system ever for a virus containing an internal membrane. A new tectiviral sequence, a linear plasmid called pBClin15, was identified in Bacillus cereus, providing material for sequence analysis of the tectiviruses. Analysis of PRD1 P9 and other putative tectiviral ATPase sequences revealed several conserved sequence motifs, among them a new tectiviral packaging ATPase motif. Mutagenesis studies on PRD1 P9 were used to confirm the significance of the motifs. P9-type putative ATPase sequences carrying a similar sequence motif were identified in several other membrane containing dsDNA viruses of bacterial, archaeal and eukaryotic hosts, suggesting that these viruses may have similar packaging mechanisms. Interestingly, almost the same set of viruses that were found to have similar putative packaging ATPases had earlier been found to share similar coat protein folds and capsid structures, and a common origin for these viruses had been suggested. The finding in this study of similar packaging proteins further supports the idea that these viruses are descendants of a common ancestor.

Comparative and functional genome analysis of fungi for development of the protein production host Trichoderma reesei

Filamentous fungi of the subphylum Pezizomycotina are well known as protein and secondary metabolite producers. Various industries take advantage of these capabilities. However, the molecular biology of yeasts, i.e. Saccharomycotina and especially that of Saccharomyces cerevisiae, the baker's yeast, is much better known. In an effort to explain fungal phenotypes through their genotypes we have compared protein coding gene contents of Pezizomycotina and Saccharomycotina. Only biomass degradation and secondary metabolism related protein families seem to have expanded recently in Pezizomycotina. Of the protein families clearly diverged between Pezizomycotina and Saccharomycotina, those related to mitochondrial functions emerge as the most prominent. However, the primary metabolism as described in S. cerevisiae is largely conserved in all fungi. Apart from the known secondary metabolism, Pezizomycotina have pathways that could link secondary metabolism to primary metabolism and a wealth of undescribed enzymes. Previous studies of individual Pezizomycotina genomes have shown that regardless of the difference in production efficiency and diversity of secreted proteins, the content of the known secretion machinery genes in Pezizomycotina and Saccharomycotina appears very similar. Genome wide analysis of gene products is therefore needed to better understand the efficient secretion of Pezizomycotina. We have developed methods applicable to transcriptome analysis of non-sequenced organisms. TRAC (Transcriptional profiling with the aid of affinity capture) has been previously developed at VTT for fast, focused transcription analysis. We introduce a version of TRAC that allows more powerful signal amplification and multiplexing. We also present computational optimisations of transcriptome analysis of non-sequenced organism and TRAC analysis in general. Trichoderma reesei is one of the most commonly used Pezizomycotina in the protein production industry. In order to understand its secretion system better and find clues for improvement of its industrial performance, we have analysed its transcriptomic response to protein secretion stress conditions. In comparison to S. cerevisiae, the response of T. reesei appears different, but still impacts on the same cellular functions. We also discovered in T. reesei interesting similarities to mammalian protein secretion stress response. Together these findings highlight targets for more detailed studies.

Optimization of sample preparation for next-generation sequencing with Illumina Genome Analyzer II

The growing interest for sequencing with higher throughput in the last decade has led to the development of new sequencing applications. This thesis concentrates on optimizing DNA library preparation for Illumina Genome Analyzer II sequencer. The library preparation steps that were optimized include fragmentation, PCR purification and quantification. DNA fragmentation was performed with focused sonication in different concentrations and durations. Two column based PCR purification method, gel matrix method and magnetic bead based method were compared. Quantitative PCR and gel electrophoresis in a chip were compared for DNA quantification. The magnetic bead purification was found to be the most efficient and flexible purification method. The fragmentation protocol was changed to produce longer fragments to be compatible with longer sequencing reads. Quantitative PCR correlates better with the cluster number and should thus be considered to be the default quantification method for sequencing. As a result of this study more data have been acquired from sequencing with lower costs and troubleshooting has become easier as qualification steps have been added to the protocol. New sequencing instruments and applications will create a demand for further optimizations in future.