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Methods for the Refinement of Genome-Scale Metabolic Networks

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Methods for the Refinement of Genome-Scale Metabolic Networks Methods for the refinement of genome-scale metabolic networks by Rodrigo Liberal Fernandes A thesis submitted for the degree of Doctor of Philosophy of Imperial College London Division of Molecular Biosciences Imperial College London London SW7 2AZ, England 1 c 2012 Rodrigo Liberal Fernandes All rights reserved Typeset in Times by LATEX This report is the result of my own work. No part of this dissertation has already been, or is currently being submitted by the author for any other degree or diploma or other qualification. The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work This dissertation does not exceed 100,000 words, excluding appendices, bibliography, footnotes, tables and equations. It does not contain more than 150 figures. This work is supported by a Portuguese Foundation for Science and Technology (FCT) grant and completed in the Division of Molecular Biosciences at Imperial College, London. All trademarks used in this dissertation are acknowledged to be the property of their respective owners. 2 Abstract More accurate metabolic networks of pathogens and parasites are required to support the identification of important enzymes or transporters that could be potential targets for new drugs. The overall aim of this thesis is to contribute towards a new level of quality for metabolic network reconstruction, through the application of several different approaches. After building a draft metabolic network using an automated method, a large amount of manual curation effort is still necessary before an accurate model can be reached. Path- wayBooster, a standalone software package, which I developed in Python, supports the first steps of model curation, providing easy access to enzymatic function information and a visual pathway display to enable the rapid identification of inaccuracies in the model. A major current problem in model refinement is the identification of genes encoding en- zymes which are believed to be present but cannot be found using standard methods. Current searches for enzymes are mainly based on strong sequence similarity to proteins of known function, although in some cases it may be appropriate to consider more dis- tant relatives as candidates for filling these pathway holes. With this objective in mind, a protocol was devised to search a proteome for superfamily relatives of a given enzymatic function, returning candidate enzymes to perform this function. Another, related approach tackles the problem of misannotation errors in public gene databases and their influence on metabolic models through the propagation of erroneous annotations. I show that the topological properties of metabolic networks contains useful 3 4 information about annotation quality and can therefore play a role in methods for gene function assignment. An evolutionary perspective into functional changes within homologous domains opens up the possibility of integrating information from multiple genomes to support the re- construction of metabolic models. I have therefore developed a methodology to predict functional change within a gene superfamily phylogeny. Contents Abstract 3 Acknowledgements 10 1 Introduction 12 1.1 Background . 12 1.2 Application . 25 1.3 Thesis Overview . 28 2 A tool to support the curation of metabolic pathways 31 2.1 Introduction . 31 2.2 Colaboration . 33 2.3 PathwayBooster . 35 2.4 Case studies . 41 2.5 Conclusion . 45 5 CONTENTS 6 3 Protocol to identify candidate genes for a missing enzymatic function 47 3.1 Introduction . 47 3.2 Methods . 51 3.3 Results and Discussion . 52 3.4 Conclusion . 59 4 Topological analyses predict misannotations in a metabolic network 62 4.1 Introduction . 62 4.2 Methods . 64 4.3 Results/Discussion . 70 4.4 Conclusion . 87 5 Phylogenetic analyses predict functional changes in an enzyme superfamily 89 5.1 Introduction . 89 5.2 Methods . 92 5.3 Results and Discussion . 100 5.4 Conclusion . 108 6 Discussion 109 6.1 Conclusions . 109 6.2 Future Work . 114 CONTENTS 7 A PathwayBooster 116 A.1 Introduction . 116 A.2 Setup instructions . 117 List of Figures 2.1 Cysteine and methionine metabolism pathway. An example pathway diagram produced by PathwayBooster, showing the cysteine and methio- nine metabolism. On the top are the tabs directing to different informa- tion sources. The coloured blocks show an automated model produced by ERGOTM (Overbeek et al. (2003)) for the thermophilic bacterium G. ther- moglucosidasius NCIMB 11955 (red) in comparison to selected reference organisms: G. thermoglucosidasius C56-YS93 (brown), G. kaustophilus (yellow), G.thermodenitrificans (green), B. subtilis (blue) and E. coli (pur- ple). 36 2.2 Visual representation of the methionine salvage pathway, where G. ther- moglucosidasius NCIMB 11955 (red) is compared to selected reference organisms: G. thermoglucosidasius C56-YS93 (brown), G. kaustophilus (yellow), G.thermodenitrificans (green), B. subtilis (blue) and E. coli (pur- ple). 43 2.3 Hamming distance heatmap for cysteine and methionine metabolism, show- ing the similarity between the query species (marked ‘Ergo’) and refer- ence organisms. 44 8 LIST OF FIGURES 9 2.4 Information tables for EC 4.2.1.109 (5-methylthioribulose-1-phosphate dehydratase). A - Annotated genes; B - BLAST bidirectional hits; C - Three best BLAST hits . 44 3.1 An example of an unpaired compound (A), chokepoint (B) and a com- pound that is neither an unpaired or a chokepoint (C). The circles are compounds and the rectangles are reactions. 50 3.2 Hole candidate search protocol. 51 3.3 Part of the Plamodium falciparum Folate Biosynthesis pathway. High- lighted by a purple ellipse is the missing link referred by the paper Dit- trich et al. (2008). All the reactions that do not have an enzyme assigned to them have a white background. The two main candidates found by the Dittrich study were originally assigned to the functions EC3.5.4.16 (PFL1155w) and EC4.2.3.12 (PFF1360w) that also belong to the Folate Biosynthesis pathway (with green and red background, respectively). The missing link experimentally validated is the gene PFF1360w. As can be seen, the reaction catalysed by PTPS (red background) is not supported by the rest of the pathway, being in a dead end. 61 4.1 Feature histograms - Local topological features. Visualisation of the potential value of each attribute in distinguishing the correct functional assignments from the incorrect ones (red - incorrect annotations; blue - correct annotations). 71 LIST OF FIGURES 10 4.2 Feature histograms - Semi-local topological features. Visualisation of the potential value of each attribute in distinguishing the correct func- tional assignments from the incorrect ones (red - incorrect annotations; blue - correct annotations). 72 4.3 Feature histograms - Global topological features. Visualisation of the potential value of each attribute in distinguishing the correct functional assignments from the incorrect ones (red - incorrect annotations; blue - correct annotations). 73 4.4 Feature predictiveness. These scores, obtained from the importance function of the randomForest R package, are used to assess the relative contribution of each feature to the performance of the predictor. left: av- erage accuracy decrease when each feature is removed. right: average entropy decrease for each feature. 73 4.5 Cross validation ROC curves. A- fivefold cross-validation ROC curves; B- superfamily cross-validation ROC curves; C- classifier performance in curated models ROC curves . 75 4.6 Classifier probability distribution for KEGG’s enzymatic annotations for Plasmodium falciparum ........................ 79 4.7 Predicted quality of draft metabolic networks across a prokaryote phylogeny. The classifier was applied to all prokaryote species present in the iTOL phylogeny (Letunic and Bork (2007, 2011)). Coloured clades represent the different phyla present (only phyla with more than 1 species were coloured). The names of the phyla are shown to the right. Predicted annotation quality values are represented by grey bars next to the species name. 82 LIST OF FIGURES 11 4.8 Predicted quality of draft metabolic networks across a eukaryote phy- logeny. The classifier was applied to all eukaryote species present in iTOL. To the left is the eukaryote phylogenetic tree. The quality values are represented by bars next to the species names. 83 4.9 Variation of predicted quality of draft metabolic networks. left: Scatter- plot showing predicted annotation quality (precision of annotated reac- tions according to the classifier) for eukaryotes against phylogenetic dis- tance to H. sapiens. right: Scatter-plot showing predicted annotation quality (precision of annotated reactions according to the classifier) for prokaryotes against phylogenetic distance to E. coli (Ciccarelli et al. (2006)).The shaded region shows the 95% confidence interval for the regression line. 84 4.10 Variation of predicted quality of draft metabolic networks. left: Scatter- plot showing predicted annotation quality against genome size in eukary- otes: The species are divided in Animals, Fungi, Plants, Protists and oth- ers. The shaded region shows the 95% confidence interval for the regres- sion line. right: Scatter-plot showing predicted annotation quality against genome size: orange - well studied species (E. coli strains and the very closely related species (Salmonella and Yersinia)); green intracellular ob- ligate species. The shaded region shows the 95% confidence interval for the regression line. 85 4.11 Variation of predicted quality of draft metabolic networks. Box-plot of the distribution of the quality scores in different sets of prokaryote species: orange and light green - same as in B; purple species for which there is a GENRE (Price et al. (2004)) available; dark green facultative intracellular species; blue all the species. 86 LIST OF FIGURES 12 5.1 Phylogenetic tree analyses objective.
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