Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

Abstract

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

“…The library of metabolic tasks a cell can sustain is encrypted in its genome and the capacity to modulate the activity of these tasks enable the cell's adaptation to changing environment. This concept of "metabolic tasks" has been previously used to evaluate the quality and capabilities of genome-scale metabolic models [9][10][11][14][15][16][17][18] . However, these studies used various frameworks to define the cell's capacity to sustain a metabolic task.…”

“…The curation was done by first taking the union of previously published lists of metabolic tasks 9,10 . We removed duplicated tasks and lumped tasks that rely on the description of similar metabolic functions.…”

“…Here, we propose an alternative approach for the interpretation of omics data (e.g., differentially expressed genes) that captures the simplicity of enrichment analyses, while providing mechanistic insights into how differential expression impacts specific cellular functions, based on pre-computed model simulations. To this end, genome-scale metabolic networks were decomposed into many smaller metabolic tasks 9,10 . We curated and standardized these tasks, resulting in a collection of hundreds of tasks covering 7 major metabolic activities of a cell (energy generation, nucleotide, carbohydrate, amino acid, lipid, vitamin & cofactor and glycan metabolism).…”

Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics

“…The simulations can uncover the reasons for counterintuitive results, such as drugs that block K v 11.1 but are nonetheless safe Lancaster and Sobie, 2016;Li et al, 2017), or drugs that only cause hepatotoxicity in some species (Kyriakides et al, 2016;Smith et al, 2016;Blais et al, 2017;Thiel et al, 2017;Woodhead et al, 2017). The simulations can also suggest the prioritization of experiments that are most likely to provide additional insight.…”

“…Using the mechanistic DILIsym R model, a testable hypothesis was generated in which the amount of reactive metabolite produced from each isomer was identified as an important contributor to the observed species differences, and this prediction was confirmed experimentally in a later study (Kyriakides et al, 2016). In addition to the consortium that has developed the DILIsym R package, other groups have gained important insight into DILI through mathematical modeling (Smith et al, 2016;Blais et al, 2017;Thiel et al, 2017).…”