Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling

Abstract

Genome-scale metabolic models (GEMs) are mathematical representations of metabolism that allow for in silico simulation of metabolic phenotypes and capabilities. A prerequisite for these predictions is an accurate representation of the biomolecular composition of the cell necessary for replication and growth, implemented in GEMs as the so-called biomass objective function (BOF). The BOF contains the metabolic precursors required for synthesis of the cellular macro- and micromolecular constituents (e.g. protein, RNA, DNA), and its composition is highly dependent on the particular organism, strain, and growth condition. Despite its critical role, the BOF is rarely constructed using specific measurements of the modeled organism, drawing the validity of this approach into question. Thus, there is a need to establish robust and reliable protocols for experimental condition-specific biomass determination. Here, we address this challenge by presenting a general pipeline for biomass quantification, evaluating its performance on Escherichia coli K-12 MG1655 sampled during balanced exponential growth under controlled conditions in a batch-fermentor set-up. We significantly improve both the coverage and molecular resolution compared to previously published workflows, quantifying 91.6% of the biomass. Our measurements display great correspondence with previously reported measurements, and we were also able to detect subtle characteristics specific to the particular E. coli strain. Using the modified E. coli GEM iML1515a, we compare the feasible flux ranges of our experimentally determined BOF with the original BOF, finding that the changes in BOF coefficients considerably affect the attainable fluxes at the genome-scale.

Central carbon metabolite profiling reveals vector-associated differences in the recombinant protein production host Escherichia coli BL21

“…PCN was approximately 20 for the 2-series strains (A2, A2-mCh, Z2 and Z2-mCh) (Durland et al, 1990), which represents a small fraction of the total DNA content of the cells (around 3.6% and 3.9%, for empty plasmid and mCherry strains, respectively). The genomic DNA content itself constitutes only 1%-3% of the E. coli dry weight cell mass (Neidhardt et al, 1990;Beck et al, 2018;Simensen et al, 2022). Thus, the additional 20 plasmid copies should not cause a major precursor and energetic burden for E. coli before induction of heterologous gene expression, as supported by both the metabolite profiling and the cultivation data of this study (Figure 2; Table 3; Figure 5).…”

“…In comparison, the mCherry production was small, around 140 mg L -1 . This represents 4% of the precursor and energetic resources needed for protein synthesis (assuming 50% protein in E. coli (Simensen et al, 2022)), and 2% of the total resources for cell growth. Compared with its emptyplasmid control (A2), the glucose consumption for A2-mCh was lower between T1 and T2 (Figure 2), and the lower glycolytic flux is manifested by the metabolite accumulation at T2.…”

Omics data analysis reveals the system-level constraint on cellular amino acid composition

“…Furthermore, we compared the calculated values with two latest sets of experimental AACell [ 33 , 34 ], and we found a very high degree of similarity between the calculated and experimental values (PCC all exceeding 0.91). The standard procedure for experimental determination of AACell involves measuring the total cellular protein content via acid hydrolysis.…”

Dynamic metagenome‐scale metabolic modeling of a yogurt bacterial community

“…For instance, acidification by ST was previously found to be stimulated by formic acid, casitone, pyruvic acid, folic acid, and polysorbate 20 (Sieuwerts et al, 2010). Monoculture of dominant ST and LB strains separated from the starter culture will be needed to approximate the growth‐associated ATP requirement from the carbon source utilized (Teusink et al, 2006), and gas chromatography/mass spectrometry or HPLC can be used to quantify major components in cellular biomass, that is, protein, DNA, RNA, lipids, and glycogen (Long & Antoniewicz, 2014; Simensen et al, 2022). To resolve the other two limitations, the proposed next step is to further refine the established GSMMs by manually adding the biosynthetic pathways of flavor and probiotic compounds of interests and implement dynamic regulatory FBA (Liu & Bockmayr, 2020) with meta‐transcriptome profiling.…”