(P03-012-24) Combining Proteomics and Metabolomics to Assess Protein Digestibility

Objectives: We presented a clinical feeding study aimed at assessing the digestibility of proteins derived from various food sources in a cohort of 16 human subjects. Our approach involved integrating a food proteomics platform with a rapid-throughput targeted metabolomics platform to elucidate protein digestibility by establishing correlations between the amino acid composition present in foods and the corresponding concentrations of amino acids detected in serum.

Methods: Four types of burger patties were cooked and fed to 16 subjects. Blood samples were drawn from subjects at different time points. For food proteomic, cooked burger patty. For the food proteomic, the cooked burger patty went through protein extraction (urea, SDS), trypsin digestion, and S-trap filter clean up before being injected into a nano-LC system coupled with Orbitrap Fusion Lumos mass spectrometry with Nano-ESI source. Proteomic data processing was done by FragPipe GUI. For metabolomic, the serum sample was extracted with internal standard-added buffer and cleaned with Folch- extraction. Amino acids solution was then injected into UHPLC-QqQ mass spectrometry for separation and analysis.

Results: Bottom-up proteomics characterized protein sources (beef, pork, pea, tofu). Around 706 protein groups were identified and quantified across three replicates, more from pork and tofu. The top 15 abundant protein groups accounted for ~74% relative abundance, indicating dominance by few proteins. We estimated amino acid profiles from proteomics data, revealing tofu's high proline and glutamine, pea's high glutamate and leucine. Plant-based burgers had higher serine, phenylalanine, asparagine, and aspartate; animal-based burgers were higher in others. We explored amino acid concentration changes in serum after protein digestion in 16 subjects. Variability existed among subjects and foods, with subject 14 showing the highest change in essential amino acids after eating beef. We calculated a "digestibility score" correlating food amino acid composition with serum concentrations, a promising tool for personalized nutrition. Di and tripeptide data will enhance specificity in future analyses.

Conclusions: A novel platform combining food proteomic and human metabolomic was applied to a clinical feeding study to assess protein digestibility.

Funding Sources: The Rockefeller Foundation