Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Abstract

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI is the first method that integrates thermodynamics together with relative gene-expression and metabolomic data as constraints for FBA. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was also able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any 2 two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology. Author SummaryThe recent advances in omics technologies have provided us with an unprecedented abundance of data spanning genomes, global gene expression, and metabolomes. Though these advancements in highthroughput data collection offer an excellent opportunity for a more thorough understanding of metabolic capacities of a wide range of species, they have caused a considerable gap between "data generation" and "data integration." reconstructed model to predict the observed physiology, e.g., growth phase through omics data integration. In this study, we present a new method named REMI (Relative Expression and Metabolomic Integrations) that enables the co-integration of gene expression, metabolomics and thermodynamics data as constraints in genome-scale models. This not only allows the better understanding of how different phenotypes originate from a given genotype but also aid...

The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models

“…An additional gauge constraint will be needed for the relative data. Previous transcriptomic integration methods, such as REMI 23 , iMAT 24 , GIMME 25 , or MINEA 26 , can also be adequately reformulated for ETFL. Metabolomics can still be integrated using TFA 2,3 .…”

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

“…Alternatively, there are theoretical approaches based on sensitivity analysis to identify metabolites of interest to be considered during the experimental design (67). As a matter of fact, relative metabolite abundance data has been successfully combined with thermodynamics to improve flux prediction between differential physiological states (54). The impact of the inherent dynamics (cell cycle and cell ageing) has been pointed out as a source of metabolic heterogeneity in clonal microbial populations (68).…”

“…It is important to note that lower and upper boundaries for uptake rates for other macronutrients (such as O 2 ) were applied as originally constrained in the metabolic networks. To compare the in silico fluxes from FBA and TFA with in vivo 13 C-MFA values (or estimated and experimental metabolite concentration values), a goodness-of-fit analysis based on the Pearson correlation coefficient ( r ) was performed, as shown in (54). In particular, MATLAB’s in-built corrcoef function was used.…”

GEM-based metabolic profiling for Human Bone Osteosarcoma under different glucose and glutamine availability

“…The RNA sequencing data were then processed (see Materials & Methods) and used for creation of the specific GEM models. Since GEMs include known functions of protein-encoding genes, they can be used as platforms for analyzing mRNA expression data to elucidate how changes in gene expression impacts cell metabolism and phenotypes [22][23][24][25][26][27][28] . Here, we additionally integrated the nutrient availability data by constraining the bounds of the exchange reactions.…”