Network analysis of the proteome and peptidome sheds light on human milk as a biological system

Abstract

Proteins and peptides found in human milk have bioactive potential to benefit the newborn and support healthy development. Research has been carried out on the health benefits of proteins and peptides, but many questions still need to be answered about the nature of these components, how they are formed, and how they end up in the milk. This study explored and elucidated the complexity of the human milk proteome and peptidome. Proteins and peptides were analyzed with non-targeted nanoLC-Orbitrap-MS/MS in a selection of 297 milk samples from the CHILD Cohort Study. Protein and peptide abundances were determined, and a network was inferred using Gaussian graphical modeling (GGM), allowing an investigation of direct associations. This study showed that signatures of (1) specific mechanisms of transport of different groups of proteins, (2) proteolytic degradation by proteases and aminopeptidases, and (3) coagulation and complement activation are present in human milk. These results show the value of an integrated approach in evaluating large-scale omics data sets and provide valuable information for studies that aim to associate protein or peptide profiles from biofluids such as milk with specific physiological characteristics.

Figure 1

Network representation of associations between proteins and peptides. The network is constructed by calculation of Gaussian graphical models (GGMs) and subsequent clustering with the Leiden community detection algorithm. Purple nodes represent proteins, and orange nodes represent peptides. The thickness of the edges is proportional to the partial correlation coefficients from the GGMs. Clusters with a significant overrepresentation of Gene Ontology (GO) annotation have a label in bold and are indicated with *. A high-resolution version of this figure, which includes labeling of the individual nodes, is provided as Supplementary Fig. S1.

Figure 2

Network representation (circular layout) of a selection of clusters. The network is constructed by calculation of Gaussian graphical models (GGMs) and subsequent clustering with the Leiden community detection algorithm. Purple nodes represent proteins with their respective gene names, and orange nodes represent peptides with the gene name of the precursor protein and the respective sequence range. The thickness of the edges is proportional to the partial correlation coefficients from the GGMs. Selected clusters show associations between proteins and peptides and the selection of clusters is made from Fig. 1 with corresponding cluster labels.