ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models.

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we've enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package (https://pypi.org/project/ECMpy/).

Enlarged perivascular spaces predict malignant cerebral edema after acute large hemispheric infarction.

INTRODUCTION: Enlarged perivascular spaces (EPVS) are considered early manifestations of impaired clearance mechanisms in the brain; however, it is unclear whether EPVS they are associated with the development of malignant cerebral edema (MCE) after large hemispheric infarction (LHI). Therefore, we investigated the predictive value of EPVS in predicting MCE in LHI. METHODS: Patients suffering from acute LHI were consecutively enrolled. EPVS were rated after the stroke with validated rating scales from magnetic resonance imagess. Patients were divided into two groups according to the occurrence of MCE. Logistic regression was used to analyze the relationship between EPVS and MCE in the basal ganglia (BG) and centrum semiovale (CS) regions. Receiver operating characteristic (ROC) curves assessed the ability of EPVS individually and with other factors in predicting MCE. RESULTS: We included a total of 255 patients, of whom 98 were MCE patients (58 [59.2%] males, aged 70 [range=61.75-78] years) and found that atrial fibrillation, National Institutes of Health Stroke Scale score, infarct volume, neutrophil-lymphocyte ratio, and moderate-to-severe CS-EPVS were positively associated with MCE. After adjusting for confounds, moderate-to-severe CS-EPVS remained independent risk factor of MCE (odds ratio=16.212, p＜0.001). According to the ROC analysis, MCE was highly suspected when CS-EPVS > 14 (sensitivity=0.82, specificity=0.48), and the guiding value were higher when CS-EPVS combined with other MCE predictors (area under the curve=0.90, sensitivity=0.74, specificity=0.90). CONCLUSION: CS-EPVS were important risk factor for MEC in patients with acute LHI and can help identify patients at risk for MCE.

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum.

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.