Genome-scale modeling specifies the metabolic capabilities of <i>Rhizophagus irregularis</i>

Wendering, Philipp; Nikoloski, Zoran

doi:10.1101/2021.10.07.463607

Cited by 1 publication

(1 citation statement)

References 115 publications

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“…The estimates of turnover numbers are integrated into metabolic models by different constraint-based approaches that have been grouped into coarse-grained (e.g. MOMENT (5), sMOMENT (20), eMOMENT (21), and GECKO (7)) and fine-grained (e.g. resource balance analysis (1, 2) and ME-models (3)).…”

Section: Introductionmentioning

confidence: 99%

Data integration across conditions improves turnover number estimates and metabolic predictions

Wendering

Arend

Nikoloski

2022

Preprint

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Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by ranking of estimates obtained by integrating reaction rate and enzyme abundance measurements from individual experiments; yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly in the ultimate test scenario when protein abundances are integrated in the model. We then demonstrate that in vitro turnover numbers can be corrected via a constraint-based approach that simultaneously leverages heterogeneous physiological data from multiple experiments. We find that the resulting estimates of in vivo turnover numbers lead to improved prediction of condition-specific growth rates, particularly when protein abundances are used as constraints, and are more precise than the available in vitro turnover numbers. Therefore, our approach provides the means to decrease the bias of in vivo turnover numbers and paves the way towards cataloguing in vivo kcatomes of other organisms.Significance StatementIntegration of turnover numbers in protein-constrained metabolic models has provided insights in enzyme allocation and can improve prediction of metabolic phenotypes. While in vivo turnover numbers are estimated by ranking the outcomes from the integration of condition-specific proteomics data and reaction rates, simultaneous consideration of physiological read-outs over multiple conditions has not been considered, yet. Here we designed and tested a constraint-based approach that leverages heterogeneous data to improve estimates of in vivo turnover numbers in model unicellular organisms. We showed that the resulting turnover numbers are more precise than in vitro estimates from public databases and lead to improved prediction of condition-specific growth. The approach is readily applicable to non-model organisms and paves the way to documenting their kcatomes.

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