Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Abstract

23Understanding the adaptive responses of individual bacterial strains is crucial for microbiome 24 engineering approaches that introduce new functionalities into complex microbiomes, such as 25 xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic 26 reprogramming of the cell, which can be captured by multi-omics, but this data remains 27 formidably challenging to interpret and predict. Here we present a new approach that combines 28 genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are 29 common tools for studying dynamic population behavior. As a realistic demonstration, we 30 developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate 31 bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil 32 microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during 33 growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic 34 processes from the integrated model closely matched experimental observations. We conclude 35 that integrative and network-based analysis can help build predictive models that accurately 36 capture bacterial adaptation responses. Further development and testing of such models may 37 considerably improve the successful establishment of bacterial inoculants in more complex 38 systems. 39 40 41 42 43 44 45 46 47 48 49 50 3

Path to improving the life cycle and quality of genome-scale models of metabolism

“…Recent reconstruction efforts make use of this added information to identify which compartments to model and the reactome content of each compartment (Seaver et al, 2014). Transcriptomics datasets have served to confirm the activity (or lack thereof) of a pathway and offer insights into the validity of network gaps (Hadadi et al, 2019).…”

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

“…[ 31,32 ] Recent advances in the integrating omics data with GEMs have shown promise in the development of high predictive models in several aspects of human health and biotechnology. [ 33–35 ] Recently, using omics data, a mathematical model framework for investigating the effect of extracellular ammonium concentration on the N‐linked sialylation process of monoclonal antibodies in CHO cells was presented by Savizi et al. [ 36 ]…”

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

“…Here, we investigated another venue and hypothesized that iJN1411 is missing a critical reaction in the ATP-related metabolism. Therefore, to make model predictions consistent with the experimental observations, we used the gap-filling procedure proposed by Chiappino-Pepe et al in 2017 [60] and later used by Hadadi et al in 2019 [67]. The gap-filling procedure is metabolic-task-driven [68,69], where a metabolic task such as the production of a biomass precursor is defined and mixed-integer linear programming (MILP) is used to identify a minimal number of gap-filling reactions required to perform the task.…”