Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors

Lewis, Joshua E.; Forshaw, Tom E.; Boothman, David A.; Furdui, Cristina M.; Kemp, Melissa L.

doi:10.1016/j.cels.2020.12.001

Cited by 41 publications

(40 citation statements)

References 103 publications

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“…The results of our study will encourage future endeavors to use our approach in any setting of an inborn monogenic disease. Moving forward, the datasets derived from our study can be further exploited, for example, by applying network contextualization tools 34 , integrating multi-omics and flux modeling 35 and reconstructing genome-scale metabolic networks 36 , continuing to refine the pipeline of a multi-modal study of IEMs.…”

Section: Discussionmentioning

confidence: 99%

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Forny

Bonilla

Lamparter³

et al. 2023

Nat Metab

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Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

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Section: Discussionmentioning

confidence: 99%

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Forny

Bonilla

Lamparter³

et al. 2023

Nat Metab

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“…We achieve this by integrating genetic effects on transcript levels into organ-specific GSMMs and simulating how they propagate and interact into genome-scale flux maps of major human organs. To validate our method, we built organ-specific models for the liver, heart, skeletal muscle, brain, and adipose tissue for over 520,000 individuals from the INTERVAL 35 , 36 and UKB 37 cohorts, surpassing by more than two orders of magnitude the number of personalised GSMMs built in previous works 30 – 33 . Association analyses were performed between genetically-personalised flux values and directly measured blood metabolites in both INTERVAL and UKB, identifying many significant and replicable associations.…”

Section: Discussionmentioning

confidence: 99%

Genetically personalised organ-specific metabolic models in health and disease

Foguet

Ritchie

et al. 2022

Nat Commun

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Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

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“…This approach can be integrated with metabolite concentrations and kinetic constants, yielding more realistic models [ 191 , 192 , 193 ]. Further combining this with multi-omics, kinetic, and thermodynamic information, personalized genome-scale models have been constructed, allowing to investigate the metabolic differences subtending different tumor phenotypes, such as resistance or sensitivity to radiation therapy, and to identify personalized therapeutic strategies for individual radiation-resistant patients [ 194 ].…”

Section: Patient-specific Network Modelingmentioning

confidence: 99%

Can Systems Biology Advance Clinical Precision Oncology?

Rocca

Kholodenko

2021

Cancers

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Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

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Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors

Cited by 41 publications

References 103 publications

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Genetically personalised organ-specific metabolic models in health and disease

Can Systems Biology Advance Clinical Precision Oncology?

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