Pasteurella multocida Expresses Two Lipopolysaccharide Glycoforms Simultaneously, but Only a Single Form Is Required for Virulence: Identification of Two Acceptor-Specific Heptosyl I Transferases

Lipopolysaccharide (LPS) is a critical virulence determinant in Pasteurella multocida and a major antigen responsible for host protective immunity. In other mucosal pathogens, variation in LPS or lipooligosaccharide structure typically occurs in the outer core oligosaccharide regions due to phase variation. P. multocida elaborates a conserved oligosaccharide extension attached to two different, simultaneously expressed inner core structures, one containing a single phosphorylated 3-deoxy-D-manno-octulosonic acid (Kdo) residue and the other containing two Kdo residues. We demonstrate that two heptosyltransferases, HptA and HptB, add the first heptose molecule to the Kdo1 residue and that each exclusively recognizes different acceptor molecules. HptA is specific for the glycoform containing a single, phosphorylated Kdo residue (glycoform A), while HptB is specific for the glycoform containing two Kdo residues (glycoform B). In addition, KdkA was identified as a Kdo kinase, required for phosphorylation of the first Kdo molecule. Importantly, virulence data obtained from infected chickens showed that while wild-type P. multocida expresses both LPS glycoforms in vivo, bacterial mutants that produced only glycoform B were fully virulent, demonstrating for the first time that expression of a single LPS form is sufficient for P. multocida survival in vivo. We conclude that the ability of P. multocida to elaborate alternative inner core LPS structures is due to the simultaneous expression of two different heptosyltransferases that add the first heptose residue to the nascent LPS molecule and to the expression of both a bifunctional Kdo transferase and a Kdo kinase, which results in the initial assembly of two inner core structures.

Non-destructive prediction of 'Hass' avocado dry matter via FT-NIR spectroscopy.

BACKGROUND: The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near-infrared spectroscopy (NIRS) was investigated for the first time as a non-invasive technique for estimating %DM of whole intact 'Hass' avocado fruit. Partial least squares regression models were developed from the diffuse reflectance spectra to predict %DM, taking into account effects of intra-seasonal variation and orchard conditions. RESULTS: It was found that combining three harvests (early, mid and late) from a single farm in the major production district of central Queensland yielded a predictive model for %DM with a coefficient of determination for the validation set of 0.76 and a root mean square error of prediction of 1.53% for DM in the range 19.4-34.2%. CONCLUSION: The results of the study indicate the potential of FT-NIRS in diffuse reflectance mode to non-invasively predict %DM of whole 'Hass' avocado fruit. When the FT-NIRS system was assessed on whole avocados, the results compared favourably against data from other NIRS systems identified in the literature that have been used in research applications on avocados.

Simultaneous prediction of acidity parameters (pH and titratable acidity) in Kefir using near infrared reflectance spectroscopy.

Acidity in terms of pH and titratable acids influences the texture and flavour of fermented dairy products, such as Kefir. However, the methods for determining pH and titratable acidity (TA) are time consuming. Near infrared (NIR) spectroscopy is a non-destructive method, which simultaneously predicts multiple traits from a single scan and can be used to predict pH and TA. The best pH NIR calibration model was obtained with no spectral pre-treatment applied, whereas smoothing was found to be the best pre-treatment to develop the TA calibration model. Using cross-validation, the prediction results were found acceptable for both pH and TA. With external validation, similar results were found for pH and TA, and both models were found to be acceptable for screening purposes.