Reconciled Rat and Human Metabolic Networks for Comparative Toxicogenomics Analyses A computational systems biology framework to facilitate preclinical drug development and biomarker discovery A Dissertation presented to the faculty of the School of Engineering and Applied Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Edik Matthew Blais May 2016 Department of Biomedical Engineering University of Virginia 1 2 3 Contents Introduction ................................................................................................................................................... 11 Abstract........................................................................................................................................................ 11 Background and Significance ....................................................................................................................... 11 Dissertation Aims ......................................................................................................................................... 13 Specific Aims – Dissertation Outcomes .................................................................................................... 14 Dissertation Preview .................................................................................................................................... 14 Chapter 1: Systems applications of metabolic networks ........................................................................... 15 Synopsis ...................................................................................................................................................... 15 Figure 1.1 – The stoichiometric matrix ...................................................................................................... 16 Constraint-based modeling and flux balance analysis .................................................................................. 16 Figure 1.2 – Gene-protein-reaction (GPR) relationship rules .................................................................... 17 Figure 1.3 – Preclinical discovery of metabolic biomarkers ....................................................................... 18 Toycon1: a simple metabolic network for educational purposes ................................................................... 19 Figure 1.4 – Toycon1, a toy metabolic network that captures energy metabolism..................................... 20 Recapitulating biological functions with metabolic tasks ............................................................................... 20 Table 1.1 – Toycon1 metabolic tasks........................................................................................................ 22 Genetic perturbations and gene essentiality ................................................................................................. 22 Table 1.2 – Deleting individual reactions from Toycon1 ............................................................................ 23 Table 1.3 – Pairwise reaction deletion screen with Toycon1 ..................................................................... 24 Flux variability analysis ................................................................................................................................ 24 Minimization of total flux principle ................................................................................................................. 25 Figure 1.5 – Comparison of optimization problems defined by FBA, pFBA, and TIMBR ........................... 25 Metabolic biomarker prediction strategies .................................................................................................... 26 4 Figure 1.6 – Metabolic network modeling strategies for predicting biomarkers ......................................... 27 Figure 1.7 – Different classes and subclasses of reactions that can be included in a metabolic network .. 28 Chapter 2: Reconstruction of a rat metabolic network based on a human metabolic network ............... 30 Synopsis ...................................................................................................................................................... 30 Figure 2.1 – The human metabolic network, iHsa, was converted into a rat network, iRno ....................... 31 Table 2.1 – Comparison between rat and human genome sizes and characteristics ................................ 31 Figure 2.2 – Inferring function between orthologs is not trivial .................................................................. 32 Figure 2.3 – Summary of orthology annotations five databases ................................................................ 33 Figure 2.4 – GPR size comparison throughout the reconstruction process ............................................... 34 Motivation to reconstruct the first genome-scale rat metabolic network ........................................................ 34 Survey of automated reconstruction approaches ......................................................................................... 35 Outlook ........................................................................................................................................................ 36 Converting human GPR rules into rat GPR rules using orthology annotations ............................................. 37 Figure 2.5 – Orthology information can be used to transform a human GENRE into a rat GENRE ........... 37 Survey of mammalian metabolic networks ................................................................................................... 38 Figure 2.6 – History and comparability of mammalian metabolic networks ............................................... 39 Comparing mouse and human metabolic networks ...................................................................................... 40 Inferring metabolic function through orthology annotations is not trivial ........................................................ 41 Survey of five distinct orthology databases .................................................................................................. 42 An optimization algorithm to preserve complexity of rat and human GPR rules ............................................ 42 Figure 2.7 – Numbers of rat and human genes associated with reactions in the KEGG database ............ 43 Figure 2.8 – Converting GPR rules using a consensus approach ............................................................. 44 Chapter 3: Reconciliation of rat and human metabolic networks .............................................................. 46 Synopsis ...................................................................................................................................................... 46 5 Figure 3.1 – iRno and iHsa were manually curated in parallel .................................................................. 46 Genome-scale differences after network reconciliation ................................................................................ 47 Figure 3.2 – Literature gap between rat and human metabolism .............................................................. 47 Table 3.1 – Comparison of reconciled rat and human GENREs with previous mammalian GENREs. ...... 48 Functional differences captured by rat and human metabolic networks ........................................................ 49 Figure 3.3 – Reconciled GPR rules between iRno and iHsa allow for varying degrees of redundancy...... 50 Figure 3.4 – Functional differences known to distinguish rat and human metabolism ............................... 52 Improvements within bile acid metabolism ................................................................................................... 53 Identifying species-specific reactions ........................................................................................................... 54 Updating annotations to external databases ................................................................................................ 54 Curating GPR rules to include complex relationships ................................................................................... 56 Formulating metabolic tasks......................................................................................................................... 56 Table 3.2 – Summary of added metabolic tasks……………………………………………………….………….57 Formulating species-specific tasks ............................................................................................................... 58 Figure 3.5 – Simulating genetic engineering strategies with iRno and iHsa .............................................. 58 Genetic engineering strategies with metabolic networks .............................................................................. 59 Acknowledgements ...................................................................................................................................... 59 Chapter 4: Quantitative growth rate predictions ......................................................................................... 60 Synopsis ...................................................................................................................................................... 60 Defining metabolic objectives for mammalian cell types ............................................................................... 60 Formulating biomass from heterogeneous datasets ....................................................................................