Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Lewis, Joshua E.; Kemp, Melissa L.

doi:10.1101/2020.08.02.233098

Cited by 11 publications

(12 citation statements)

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“…The metabolome is an excellent integrative system to predict plant environment because it carries imprints of omic inferences and environmental influences (Kosmacz et al ., 2020; Lewis & Kemp, 2021). Ecological metabolomics aims to study the environmental impact on metabolic responses, acclimation and adaptation processes in natural ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Predictive metabolomics of multiple Atacama plant species unveils a core set of generic metabolites for extreme climate resilience

et al. 2022

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Current crop yield of the best ideotypes is stagnating and threatened by climate change. In this scenario, understanding wild plant adaptations in extreme ecosystems offers an opportunity to learn about new mechanisms for resilience. Previous studies have shown species specificity for metabolites involved in plant adaptation to harsh environments.Here, we combined multispecies ecological metabolomics and machine learning-based generalized linear model predictions to link the metabolome to the plant environment in a set of 24 species belonging to 14 families growing along an altitudinal gradient in the Atacama Desert.Thirty-nine common compounds predicted the plant environment with 79% accuracy, thus establishing the plant metabolome as an excellent integrative predictor of environmental fluctuations. These metabolites were independent of the species and validated both statistically and biologically using an independent dataset from a different sampling year. Thereafter, using multiblock predictive regressions, metabolites were linked to climatic and edaphic stressors such as freezing temperature, water deficit and high solar irradiance.These findings indicate that plants from different evolutionary trajectories use a generic metabolic toolkit to face extreme environments. These core metabolites, also present in agronomic species, provide a unique metabolic goldmine for improving crop performances under abiotic pressure.

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Section: Introductionmentioning

confidence: 99%

Predictive metabolomics of multiple Atacama plant species unveils a core set of generic metabolites for extreme climate resilience

et al. 2022

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“…It is reasonable to postulate that cancers with poor responses to radiotherapy may be more likely to have either strong basal antioxidant defenses or a great deal of metabolic plasticity that facilitates rewiring of metabolic fluxes to generate a powerful antioxidant response after radiation. In agreement with this idea, two recent studies using personalized genome‐scale metabolic flux models identified tumor redox metabolism as a major predictor for radiation sensitivity 106,107 . It appears that tumors with poor radiation response reroute metabolism to boost the levels of reducing factors of the cell, such as NADPH and glutathione, thus enhancing the clearance of ROS 106 .…”

Section: Changes To Major Metabolic Pathways Induced By Ionizing Radiationmentioning

confidence: 82%

Metabolic response to radiation therapy in cancer

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Bailleul

Vlashi

et al. 2021

Molecular Carcinogenesis

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Tumor metabolism has emerged as a hallmark of cancer and is involved in carcinogenesis and tumor growth. Reprogramming of tumor metabolism is necessary for cancer cells to sustain high proliferation rates and enhanced demands for nutrients. Recent studies suggest that metabolic plasticity in cancer cells can decrease the efficacy of anticancer therapies by enhancing antioxidant defenses and DNA repair mechanisms. Studying radiation‐induced metabolic changes will lead to a better understanding of radiation response mechanisms as well as the identification of new therapeutic targets, but there are few robust studies characterizing the metabolic changes induced by radiation therapy in cancer. In this review, we will highlight studies that provide information on the metabolic changes induced by radiation and oxidative stress in cancer cells and the associated underlying mechanisms.

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“…Therefore, in this study, CBM‐derived fluxomics is used to integrate with multi‐omics datasets in the ML classifier. [ 144 ]…”

Section: Synergisms Of Cbm and MLmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Cited by 11 publications

References 80 publications

Predictive metabolomics of multiple Atacama plant species unveils a core set of generic metabolites for extreme climate resilience

Predictive metabolomics of multiple Atacama plant species unveils a core set of generic metabolites for extreme climate resilience

Metabolic response to radiation therapy in cancer

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

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