Genome-level transcription data of Yersinia pestis analyzed with a New metabolic constraint-based approach

Abstract

BackgroundConstraint-based computational approaches, such as flux balance analysis (FBA), have proven successful in modeling genome-level metabolic behavior for conditions where a set of simple cellular objectives can be clearly articulated. Recently, the necessity to expand the current range of constraint-based methods to incorporate high-throughput experimental data has been acknowledged by the proposal of several methods. However, these methods have rarely been used to address cellular metabolic responses to some relevant perturbations such as antimicrobial or temperature-induced stress. Here, we present a new method for combining gene-expression data with FBA (GX-FBA) that allows modeling of genome-level metabolic response to a broad range of environmental perturbations within a constraint-based framework. The method uses mRNA expression data to guide hierarchical regulation of cellular metabolism subject to the interconnectivity of the metabolic network.ResultsWe applied GX-FBA to a genome-scale model of metabolism in the gram negative bacterium Yersinia pestis and analyzed its metabolic response to (i) variations in temperature known to induce virulence, and (ii) antibiotic stress. Without imposition of any a priori behavioral constraints, our results show strong agreement with reported phenotypes. Our analyses also lead to novel insights into how Y. pestis uses metabolic adjustments to counter different forms of stress.ConclusionsComparisons of GX-FBA predicted metabolic states with fluxomic measurements and different reported post-stress phenotypes suggest that mass conservation constraints and network connectivity can be an effective representative of metabolic flux regulation in constraint-based models. We believe that our approach will be of aid in the in silico evaluation of cellular goals under different conditions and can be used for a variety of analyses such as identification of potential drug targets and their action.

Genome‐scale metabolic reconstruction and constraint‐based modelling of the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125

“…In at least two study‐cases, FBA and expression data have been merged to explore the effect of temperature downshift at the system level (Navid and Almaas, ; Tong et al ., ). In particular, Tong and colleagues () performed robustness analysis on the core metabolic model of Thermoanaerobacter tengcongensis to study the dynamic changes of the metabolic network, following the perturbation of the culture temperature and collecting the bacterial growth rates and differential proteomes.…”

Applications of genome-scale metabolic network model in metabolic engineering

“…OptStrain [67], OptReg [68], OptForce [72], k-OptForce [16], OptORF [44], CosMos [20] Omics data integration Transcriptome GIMME [5], iMAT [82], GIM 3 E [76], E-Flux [18], PROM [13], MADE [38], tFBA [90], RELATCH [45], TEAM [19], AdaM [89], GX-FBA [60], mCADRE [92], FCGs [43], EXAMO [75], TIGER [37] Proteome GIMMEp [6] Pathway prediction BNICE [29], Cho et al [14], RetroPath [11], PathPred [59], DESHARKY [74], BioPath [94], XTMS [12], GEM-Path [56] phenotype and gene essentiality [24]. Even further, taking advantage of a large set of genome sequences available for various E. coli strains, the GEMs for 55 E. coli strains were used to investigate the variations in gene, reaction and metabolite contents, and the capabilities to adapt to different nutritional environments among the strains [40].…”

Efflux pumps and antimicrobial resistance: Paradoxical components in systems genomics

“…Metabolic simulation of another high-quality model iPAO1 of Pseudomonas aeruginosa showed mechanism of polymyxin resistance in which polymixin exerted the remarkable change in the physiochemical properties of the outer membrane (Zhu et al, 2018). Similar models to study antibiotic treatment and response were developed in multi-drug resistant bacteria, including Acinetobacter baumannii (Presta et al, 2017), Mycobacterium tuberculosis (Colijn et al, 2009), and Yersinia pestis (Navid and Almaas, 2012).…”