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An infant in vitro digestion model was utilized to investigate protein digestion characteristics in human and diverse mammalian milk (i.e., cow, goat, sheep, mare, and camel milk) using electrophoresis and chromatography. Digestive differences among milks were mainly manifested in the infant gastric phase, as evidenced by varying degrees of protein digestion. Notably, proteins (i.e., lactoferrin, serum albumin, and immunoglobulin G-heavy chain) remained partially intact in human milk, whereas these proteins in animal milk were exclusively degraded after gastrointestinal digestion. The peptide spectra of human, mare, and camel milk were highly similar, with a predominant formation of low-intensity small peptides, whereas the other three milk showed the opposite phenomenon. Heatmap cluster analysis indicated that camel milk was the most comparable to human milk before digestion, yet sheep milk was the most similar to human milk regarding protein digestion behaviors following infant gastric digestion.

Health-promoting effects of kefir may be partially caused by bioactive peptides. To evaluate their formation or degradation during gastrointestinal digestion, we monitored changes of the peptide profile in a model of (1) oral, (2) gastric, and (3) small intestinal digestion of kefir. Matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy analyses revealed clearly different profiles between digests 2/3 and kefir/digest 1. Subsequent ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry identified 92 peptides in total (25, 25, 43, and 30, partly overlapping in kefir and digests 1, 2, and 3, respectively), including 16 peptides with ascribed bioactivity. Relative quantification in scheduled multiple reaction monitoring mode showed that many bioactive peptides were released by simulated digestion. Most prominently, the concentration of angiotensin-converting enzyme inhibitor β-casein 203-209 increased approximately 10 000-fold after combined oral, gastric, and intestinal digestion. Thus, physiological digestive processes may promote bioactive peptide formation from proteins and oligopeptides in kefir. Furthermore, bioactive peptides present in certain compartments of the gastrointestinal tract may exert local physiological effects.

In recent years, mass spectrometry imaging (MSI) has been shown to be a promising technique in oncology. The effective application of MSI, however, is hampered by the complexity of the generated data. Bioinformatic approaches that reduce the complexity of these data are needed for the effective use in a (bio)medical setting. This holds especially for the analysis of tissue microarrays (TMA), which consist of hundreds of small tissue cores. Here we present an approach that combines MSI on tissue microarrays with principal component linear discriminant analysis (PCA-LDA) to predict treatment response. The feasibility of such an approach was evaluated on a set of patient-derived xenograft models of triple-negative breast cancer (TNBC). PCA-LDA was used to classify TNBC tumor tissues based on the proteomic information obtained with matrix-assisted laser desorption ionization (MALDI) MSI from the TMA surface. Classifiers based on two different tissue microarrays from the same tumor models showed overall classification accuracies between 59 and 77%, as determined by cross-validation. Reproducibility tests revealed that the two models were similar. A clear effect of intratumor heterogeneity of the classification scores was observed. These results demonstrate that the analysis of MALDI-MSI data by PCA-LDA is a valuable approach for the classification of treatment response and tumor heterogeneity in breast cancer.