Amable Hortaçsu

Flux balance analysis of a genome-scale yeast model constrained by exometabolomic data allows metabolic system identification of genetically different strains

A systems approach to biology requires a principled approach to pathway identification. In this study, the two nuclear petite yeast mutants K1Δpet191a and K1Δpet191ab and their parental industrial strain K1 were cultured in glucose‐containing microaerobic chemostats. Exometabolomic profiles were used to infer the differences in the fermentation characteristics and respiration capacity of the strains. The ability of the metabolite measurement information to describe genetically different strains was investigated using a genome‐scale yeast model. Flux balance analysis (FBA) of the model reveals that the objective function of minimal oxygen consumption enables the identification of the effect of genotypic differences when combined with the knowledge of the extracellular state of metabolism. The predicted decrease in oxygen consumption flux of K1Δpet191a and K1Δpet191ab strains with respect to the parental strain is about 80% and 100%, respectively, which coincides with the respiratory deficiencies of the strains. The expected increase in ethanol production rates in response to the decrease in the respiratory capacity was also predicted to be very close to the experimental values. This study shows the predictive power of the integrated analysis of genome‐scale models with exometabolomic profiles, since accurate predictions could be made without any information about the respiration capacity of the strains. The FBA approach thereby enables identification of responsive pathways and so permits the elucidation of the genetic characteristics of strains in terms of expressed metabolite profiles.

The effect of glucose concentration on the growth rate and some intracellular components of a recombinant E. coli culture

Abstract The effect of the concentration of a limiting substrate on growth and the concentrations of several intracellular components of recombinant E. coli JM 109 containing the plasmid pUC13 were investigated. The limiting substrate was glucose and the intracellular components measured were plasmid content, RNA content, and β-lactamase activity. The specific growth rate increased with increase in substrate concentration up to 16 g/litre. A further increase in glucose concentration inhibited growth. The RNA content increased with increasing specific growth rate while the plasmid content and β-lactamase activity decreased. An unstructured model for the effect of substrate concentration on the specific growth rate was found to fit the experimental data. The equation simulates substrate inhibition during growth at high glucose concentrations.

Dynamic analysis of ß lactamase ligand interaction

β-lactam antibiotics are the most commonly used antibiotics which cause bacterial cell lysis by inhibiting the enzyme responsible for the cell wall synthesis. Production of β-lactamase enzyme, which catalyzes the hydrolysis of β-lactam ring of β-lactam antibiotics is the most common mechanism of bacterial resistance. β-Lactamase Inhibitory Protein (BLIP), is an effective inhibitor of class A β-lactamases such as TEM-1 and SHV-1. TEM-1 and SHV-1 are the most commonly found β-lactamases and they are responsible for the resistance to β-lactam antibiotics of various pathogenic bacteria. In an effort to elucidate the mechanism of β-lactamase inhibiton by BLIP and to make predictions of binding affinity between these molecules, Molecular Dynamics (MD) simulations were performed on TEM-1 and SHV-1 bound to BLIP and BLIP based peptides. Asp49 residue which is known to play a critical role on binding on BLIP was mutated to Alanine to determine the contribution of this residue to binding. Binding free energy of the TEM-1 and SHV-1 bound BLIP, mutant BLIP (D49A) complexes were estimated by the molecular mechanics Poisson Boltzmann Surface Area method (MM-PBSA). Free energy of binding calculations show that the mutation on D49 causes a decrease in binding affinity for both TEM-1 and SHV-1 β-lactamase.