Potential of multiparametric characterization of foodstuffs by nuclear magnetic resonance to better predict microbial behavior.

Numerous predictive microbiology models have been proposed to describe bacterial population behaviors in foodstuffs. These models depict the growth kinetics of particular bacterial strains based on key physico-chemical parameters of food matrices and their storage temperature. In this context, there is a prominent issue to accurately characterize these parameters, notably pH, water activity (aw ), and NaCl and organic acid concentrations. Usually, all these product features are determined using one destructive analysis per parameter at macroscale (>5 g). Such approach prevents an overall view of these characteristics on a single sample. Besides, it does not take into account the intra-product microlocal variability of these parameters within foods. Nuclear magnetic resonance (NMR) is a versatile non-invasive spectroscopic technique. Experiments can be recorded successively on a same collected sample without damaging it. In this work, we designed a dedicated NMR approach to characterize the microenvironment of foods using 10-mg samples. The multiparametric mesoscopic-scale approach was validated on four food matrices: a smear soft cheese, cooked peeled shrimps, cold-smoked salmon, and smoked ham. Its implementation in situ on salmon fillets enabled to observe the intra-product heterogeneity and to highlight the impact of process on the spatial distribution of pH, NaCl, and organic acids. This analytical development and its successful application can help address the shortcomings of monoparametric methods traditionally used for predictive microbiology purposes.

Identification of markers of thermal processing ("roasting") in aqueous extracts of Coffea arabica L. seeds through NMR fingerprinting and chemometrics.

Roasting of Coffea arabica L. seeds gives rise to chemical reactions that produce more than 800 compounds, some being responsible for the desired organoleptic properties for which the beverage called "coffee" is known. In the industry, the "roasting profile," that is, the times and temperatures applied, is key to influence the composition of roasted coffee beans and the flavour of the beverage made from them. The impact of roasting on the chemical composition of coffee has been the subject of numerous studies, including by nuclear magnetic resonance (NMR) spectroscopy. However, the roasting equipment and profiles applied in these studies are often far from real industrial conditions. In this work, the effects of two critical technological parameters of the roasting process, namely, the "development time" (the period of time after the "first crack," a characteristic noise due to seed disruption) and the final roasting temperature on coffee extracts, were investigated. Seeds were roasted at pilot scale according to 13 industrial roasting profiles and extracted in D2 O. The extracts were analysed by 1 H NMR experiments. The NMR spectra were compared using (a) quantitative analysis of main signals by successive orders of magnitude and (b) chemometric tools (principal component analysis, partial least squares and sparse-orthogonal partial least squares analysis). This allowed to identify compounds, which may serve as markers of roasting and showed that changes in chemical composition can be detected even for slight change in final temperature (~1°C) or in total roasting time (~25 s).

Accurate protein-peptide titration experiments by nuclear magnetic resonance using low-volume samples.

NMR spectroscopy allows measurements of very accurate values of equilibrium dissociation constants using chemical shift perturbation methods, provided that the concentrations of the binding partners are known with high precision and accuracy. The accuracy and precision of these experiments are improved if performed using individual capillary tubes, a method enabling full automation of the measurement. We provide here a protocol to set up and perform these experiments as well as a robust method to measure peptide concentrations using tryptophan as an internal standard.

Disorder-to-order transition of MAGI-1 PDZ1 C-terminal extension upon peptide binding: thermodynamic and dynamic insights.

PDZ domains are highly abundant protein-protein interaction modules commonly found in multidomain scaffold proteins. The PDZ1 domain of MAGI-1, a protein present at cellular tight junctions that contains six PDZ domains, is targeted by the E6 oncoprotein of the high-risk human papilloma virus. Thermodynamic and dynamic studies using complementary isothermal titration calorimetry and nuclear magnetic resonance (NMR) (15)N heteronuclear relaxation measurements were conducted at different temperatures to decipher the molecular mechanism of this interaction. Binding of E6 peptides to the MAGI-1 PDZ1 domain is accompanied by an unusually large and negative change in heat capacity (ΔC(p)) that is attributed to a disorder-to-order transition of the C-terminal extension of the PDZ1 domain upon E6 binding. Analysis of temperature-dependent thermodynamic parameters and (15)N NMR relaxation data of a PDZ1 mutant in which this disorder-to-order transition was abolished allows the unusual thermodynamic signature of E6 binding to be correlated to local folding of the PDZ1 C-terminal extension. Comparison of the exchange contributions observed for wild-type and mutant proteins explains how variation in the solvent-exposed area may compensate for the loss of conformational entropy and further designates a distinct set of a few residues that mediate this local folding phenomena.