Biology subjects

Wendering, P.

Publications and source records attributed to Wendering, P..

4 recordsLinked to original sources

bioRxiv2022

Estimates of in vivo turnover numbers by simultaneously considering data from multiple conditions improve metabolic predictions

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 demonstrate that in vivo estimation of turnover number by simultaneous consideration of heterogeneous physiological data leads to improved prediction of condition-specific growth rates. Moreover, the obtained estimates are more precise than the available in vivo 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.

bioinformatics↗

bioRxiv2021

Genome-scale modeling specifies the metabolic capabilities of Rhizophagus irregularis

Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants. Here we used all published genomics, transcriptomics, and metabolomics resources to gain insights in the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to: (1) accurately predict increased growth of R. irregularis on myristate with minimal medium; (2) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation ({rho}S = 0.74) to measured hyphal dry weight; and (3) simulated growth rate increases with tighter association of this AMF with the host plant across three fungal structures. Based on the validated model and system-level analyses that integrate data from transcriptomics studies, we predicted that differences in flux distributions between intraradical mycelium and arbuscles are linked to changes in amino acid and cofactor biosynthesis. Therefore, our results demonstrated that the enzyme-constrained metabolic model can be employed to pinpoint mechanisms driving developmental and physiological responses of R. irregularis to different environmental cues. In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance. ImportanceMounting evidence points at the benefits of the symbiotic interactions between the arbuscular mycorrhiza fungus Rhizophagus irregularis and crops; yet, the molecular mechanisms underlying the physiological responses of this fungus to different host plants and environments remain largely unknown. We present a manually curated, enzyme-constrained genome-scale metabolic model of R. irregularis that can accurately predict experimentally observed phenotypes. We show that this high-quality model provides an entry point into better understanding the metabolic and physiological responses of this fungus to changing environments due to the availability of different nutrients. The model can be used to design metabolic engineering strategies to tailor R. irregularis metabolism towards improving the performance of host plants.

systems biology↗

bioRxiv2021

COMMIT: Consideration of metabolite leakage and community composition improves microbial community models

Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic models improves the quality of the draft models, measured by the genomic evidence for considered enzymatic reactions. We then devise an approach for gap filling, termed COMMIT, that considers exchangeable metabolites based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual models. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.

bioinformatics↗

bioRxiv2021

A structural property for reduction of biochemical networks

Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.

bioinformatics↗