Salmonella typhimuriumandEscherichia colidissimilarity: Closely related bacteria with distinct metabolic profiles

Abstract

Live attenuated strains of Salmonella typhimurium have been extensively investigated as vaccines for a number of infectious diseases. However, there is still little information available concerning aspects of their metabolism. S. typhimurium and Escherichia coli show a high degree of similarity in terms of their genome contents and metabolic networks. However, this work presents experimental evidence showing that significant differences exist in their abilities to direct carbon fluxes to biomass and energy production. It is important to study the metabolism of Salmonella to elucidate the formation of acetate and other metabolites involved in optimizing the production of biomass, essential for the development of recombinant vaccines. The metabolism of Salmonella under aerobic conditions was assessed using continuous cultures performed at dilution rates ranging from 0.1 to 0.67 h(-1), with glucose as main substrate. Acetate assimilation and glucose metabolism under anaerobic conditions were also investigated using batch cultures. Chemostat cultivations showed deviation of carbon towards acetate formation, starting at dilution rates above 0.1 h(-1). This differed from previous findings for E. coli, where acetate accumulation was only detected at dilution rates exceeding 0.4 h(-1), and was due to the lower rate of acetate assimilation by S. typhimurium under aerobic conditions. Under anaerobic conditions, both microorganisms mainly produced ethanol, acetate, and formate. A genome-scale metabolic model, reconstructed for Salmonella based on an E. coli model, provided a poor description of the mixed fermentation pattern observed during Salmonella cultures, reinforcing the different patterns of carbon utilization exhibited by these closely related bacteria.

Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis

“…Acetate was produced in both studied dilution rates. An increase of acetate production rate was observed with increasing growth rates, showing the overflow effect, also reported in our previous work on S. typhimurium (Sargo et al, 2015). Although the overflow metabolism is an intricate phenomenon that is regulated at different cellular levels, it is recognized that the growth rate acts as a modulator of the overflow metabolism (for review see Bernal et al, 2016).…”

“…1). The biomass yield obtained was similar in both dilution rates, and slightly higher than the one obtained for S. typhimurium, under the same conditions, by Sargo et al (2015). Acetate was the unique by-product detected and was secreted in both dilution rates, with a production flux of 0.87 and 2.63 mmol gDW −1 h −1 , for D = 0.26 h −1 and D = 0.52 h −1 , respectively.…”

“…In the present study, 13 C-MFA was applied to study S. typhimurium LT2 metabolism in aerobic glucose-limited chemostat cultivations under two different dilution rates (0.26 h −1 and 0.52 h −1 ). The dilution rates were chosen based on previous results, which showed noticeable differences on the metabolic behavior of S. typhimurium growing at the mentioned dilution rates (Sargo et al, 2015). In our studies, 20% [U-13 C] glucose mixed with 80% naturally labeled glucose was used to explore S. typhimurium central carbon metabolism.…”

Novel Two-Step Hierarchical Screening of Mutant Pools Reveals Mutants under Selection in Chicks

“…6C). Formate is a major product of mixed-acid fermentation and accounts for approximately a third of the carbon generated from glucose (32,75). Formate is present in the ceca of 18-week-old chickens (76).…”

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

“…Moreover, the concomitant proliferation of genome-scale metabolic models (GSMMs), often of organisms poorly characterized in physiological terms, adds a new layer of uncertainty to in silico predictions that also needs to be considered when designing novel and improved strains. In fact, most pathogens have few physiological data available, due to difficulties in performing controlled cultivations or even understanding nutritional requirements (25)(26)(27)(28), but organisms commonly used in industry, such as the yeast Kluyveromyces lactis, also have very few data available that could be used for model construction and validation (29).…”