PhenoMapping: A protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models

Chiappino-Pepe, Anush; Hatzimanikatis, Vassily

doi:10.1016/j.xpro.2020.100280

Cited by 4 publications

(2 citation statements)

References 56 publications

(128 reference statements)

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“…GEMs can be improved through integration of transcriptomics or proteomics datasets with generic models, generating context-specific GEMs [19][20][21] . Based on the curated models, a variety of computational analyses and simulations can be implemented to predict individual reaction rates, identify perturbed metabolites and genes associated with diseases, and determine the essential substrates that contribute to cellular phenotypes 22 . In Drosophila, several GEMs have been generated to analyze metabolic perturbations in silico.…”

Section: Introductionmentioning

confidence: 99%

Identification of high sugar diet-induced dysregulated metabolic pathways in muscle using tissue-specific metabolic models in Drosophila

Moon,

Hu,

Dzieciatkowska

et al. 2024

Preprint

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Individual tissues perform highly specialized metabolic functions to maintain whole-body homeostasis. Although Drosophila serves as a powerful model for studying human metabolic diseases, a lack of tissue-specific metabolic models makes it challenging to quantitatively assess the metabolic processes of individual tissues and disease models in this organism. To address this issue, we reconstructed 32 tissue-specific genome-scale metabolic models (GEMs) using pseudo-bulk single cell transcriptomics data, revealing distinct metabolic network structures across tissues. Leveraging enzyme kinetics and flux analyses, we predicted tissue-dependent metabolic pathway activities, recapitulating known tissue functions and identifying tissue-specific metabolic signatures, as supported by metabolite profiling. Moreover, to demonstrate the utility of tissue-specific GEMs in a disease context, we examined the effect of a high sugar diet (HSD) on muscle metabolism. Together with 13C-glucose isotopic tracer studies, we identified glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as a rate-limiting enzyme in glycolysis in response to HSD. Decreased GAPDH activity was linked to increased NADH/NAD+ ratio and oxidation of GAPDH. Furthermore, we introduced a pathway flux index to predict and validate additionally perturbed pathways, including fructose and butanoate metabolism. Altogether, our results represent a significant advance in generating quantitative tissue-specific GEMs and flux analyses in Drosophila, highlighting their use for identifying dysregulated metabolic pathways in a human disease model.

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Section: Introductionmentioning

confidence: 99%

Identification of high sugar diet-induced dysregulated metabolic pathways in muscle using tissue-specific metabolic models in Drosophila

Moon,

Hu,

Dzieciatkowska

et al. 2024

Preprint

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show abstract

“…Using this parameter as a guidance, we demonstrate selective growth inhibition in cancer cell lines, such as MDA-MB-231 and HCT-116, by perturbing cytosolic or mitochondrial NADPH metabolism, respectively. Moreover, using a reconstructed genome-scale metabolic model (GEM) for gene-knockout simulation and flux analysis 25,26 , we systematically evaluate the SLC25 mitochondrial transporter gene family, identify and experimentally validate that SLC25A1 plays a key role in maintaining both cytosolic and mitochondrial NADPH pools in shuttling NADPH. Given the essential role of compartmentalized NADPH metabolism in cell growth and survival, targeting NADPH metabolism has been proposed as a potential anti-cancer therapy 27,28 .…”

Section: Introductionmentioning

confidence: 99%

NADPH composite index analysis quantifies the relationship between compartmentalized NADPH dynamics and growth rates in cancer cells

Moon,

Chiappino-Pepe,

Dong

et al. 2024

Preprint

Self Cite

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NADPH, a highly compartmentalized electron donor in mammalian cells, plays essential roles in cell metabolism. However, little is known about how cytosolic and mitochondrial NADPH dynamics relate to cancer cell growth rates in response to varying nutrient conditions. To address this issue, we present NADPH composite index analysis, which quantifies the relationship between compartmentalized NADPH dynamics and growth rates using genetically encoded NADPH sensors, automated image analysis pipeline, and correlation analysis. Through this analysis, we demonstrated that compartmentalized NADPH dynamics patterns were cancer cell-type dependent. Specifically, cytosolic and mitochondrial NADPH dynamics of MDA-MB-231 decreased in response to serine deprivation, while those of HCT-116 increased in response to serine or glutamine deprivation. Furthermore, by introducing a fractional contribution parameter, we correlated cytosolic and mitochondrial NADPH dynamics to growth rates. Using this parameter, we identified cancer cell lines whose growth rates were selectively inhibited by targeting cytosolic or mitochondrial NADPH metabolism. Mechanistically, we identified citrate transporter as a key mitochondrial transporter that maintains compartmentalized NADPH dynamics and growth rates. Altogether, our results present a significant advance in quantifying the relationship between compartmentalized NADPH dynamics and cancer cell growth rates, highlighting a potential of targeting compartmentalized NADPH metabolism for selective cancer cell growth inhibitions.

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Machine learning for the advancement of genome-scale metabolic modeling

Kundu,

Beura,

Mondal

et al. 2024

Biotechnology Advances

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PhenoMapping: A protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models

Cited by 4 publications

References 56 publications

Identification of high sugar diet-induced dysregulated metabolic pathways in muscle using tissue-specific metabolic models in Drosophila

Identification of high sugar diet-induced dysregulated metabolic pathways in muscle using tissue-specific metabolic models in Drosophila

NADPH composite index analysis quantifies the relationship between compartmentalized NADPH dynamics and growth rates in cancer cells

Machine learning for the advancement of genome-scale metabolic modeling

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