Development and Interlaboratory Evaluation of an LC-MS/MS Method for the Quantification of Lysozyme in Wine Across Independent Instrument Platforms.

BACKGROUND: Various processing aids and fining agents are used in winemaking to help improve sensory characteristics. Some of these materials may contain or be derived from allergenic foods, such as eggs. In order to ensure food safety and that products meet regulatory compliance, it is essential to have robust and effective analytical methods to verify the removal of allergenic proteins following their use. Current methods include ELISA and MS methods, which can target either whole foods or individual proteins, and provide either quantitative data or qualitative confirmation of proteins. MS methods offer the potential to test for multiple proteins within a single assay to improve cost and efficiency, whereas ELISA methods typically analyze for a single protein per assay. OBJECTIVE: This study focuses on the development of a LC-tandem MS (MS/MS) quantitative method for lysozyme in white wine and compares performance across two laboratories utilizing two different instrument platforms. METHODS: Lysozyme target peptides were selected by conducting bottom-up discovery proteomics. Candidate targets were evaluated using parallel reaction monitoring (PRM) or selected reaction monitoring (SRM) LC-MS/MS, depending on the instrument in each laboratory. Quantification of lysozyme was conducted using internal, stable isotope-labeled synthetic peptide standards. RESULTS: Three of eight candidate target peptides showed performance suitable for the final quantitative method. White wine spiked with 0.1 and 0.5 ppm lysozyme demonstrated quantitative recovery of 70-120%. While the PRM method delivered better repeatability, the SRM method gave higher quantitative recovery values. CONCLUSION: A targeted LC-MS/MS method for quantification of lysozyme in white wine has been developed and deployed on two different MS instrument platforms in two laboratories. HIGHLIGHTS: Both SRM and PRM targeted LC-MS/MS methodologies can be used for quantification of lysozyme in white wine. This study is among the first to evaluate an MS method for food allergen quantification in multiple laboratories.

Learning delayed influences of biological systems.

Boolean networks are widely used model to represent gene interactions and global dynamical behavior of gene regulatory networks. To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model. In this paper, we present a logical method to learn such models from sequences of gene expression data. This method analyzes each sequence one by one to iteratively construct a Boolean network that captures the dynamics of these observations. To illustrate the merits of this approach, we apply it to learning real data from bioinformatic literature. Using data from the yeast cell cycle, we give experimental results and show the scalability of the method. We show empirically that using this method we can handle millions of observations and successfully capture delayed influences of Boolean networks.