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

Abstract

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.npj Systems Biology and Applications (2020) 6:1 ; https://doi.

Adaptation of Carbon Source Utilization Patterns of Geobacter metallireducens During Sessile Growth

“…Even in column sections with non-detectable concentrations of benzoate, a number of proteins for translation were upregulated, indicating activity of G. metallireducens. This goes in line with a recent study where the toluene degrader Pseudomonas veronii increased amino acids synthesis when cultivated in sand, compared to slow growth rates in liquid medium (Hadadi et al, 2020).…”

Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

“…The SWIM algorithm was applied to a specific group of diseases of interest to build disease-specific GENs (Supplementary Data 1 ) and extract a list of switch genes for each disease through an accurate topological analysis (Supplementary Table 2 ). The analyzed human diseases were: ten tumor types (i.e., BLCA, BRCA, CHOL, COAD, HNSC, KIRP, LUAD, LUSC, PRAD, and UCEC) available from TCGA, whose corresponding lists of switch genes were retrieved from our previous study 11 ; one pulmonary disease (COPD), whose corresponding list of switch genes was retrieved from our previous study 14 ; ischemic cardiomyopathy (IC), whose list of switch genes was obtained by applying SWIM correlation network analysis to RNA-sequencing data from ischemic human failing versus non-failing control hearts; and non-ischemic cardiomyopathy (NIC), whose list of switch genes was obtained by applying SWIM correlation network analysis to RNA-sequencing data from non-ischemic human failing versus non-failing control hearts; AD, whose list of switch genes was obtained by applying SWIM correlation network analysis to microarray expression data related to AD patients versus controls. …”

“…ten tumor types (i.e., BLCA, BRCA, CHOL, COAD, HNSC, KIRP, LUAD, LUSC, PRAD, and UCEC) available from TCGA, whose corresponding lists of switch genes were retrieved from our previous study 11 ;…”

Analyzing Neck Circumference as an Indicator of CPAP Treatment Response in Obstructive Sleep Apnea with Network Medicine

“…In this paper, we follow the network medicine approach [ 15 , 26 ], which can uncover complex phenotype relationships in respiratory medicine [ 21 , 23 , 27 , 28 ]. Using the data from , we construct a complex network , with V the node (or vertex) set and E the link (or edge) set.…”