Although many studies have been conducted on the components present in human breast milk (HM), research on the differences of chemical metabolites between HM, bovine milk (BM) and formula milk (FM) is limited. This study was to explore the chemical diversity of HM, BM and FM by metabolomic approaches. GC-TOFMS and UPLC-QTOFMS were applied to investigate the metabolic compositions in 30 HM samples, 20 FM samples and 20 BM samples. Metabolite profiling identified that most of the non-esterified fatty acids, which reflected the hydrolysis of triglycerides, were much more abundant in HM than those in FM and BM, except for palmitic acid and stearic acid. The levels of tricarboxylic acid (TCA) intermediates were much higher in FM and BM than those in HM. Each type of milk also showed its unique composition of free amino acids and free carbohydrates. In conclusion, higher levels of non-esterified saturated fatty acids with aliphatic tails <16 carbons, monounsaturated fatty acids and polyunsaturated fatty acids and lower levels of TCA intermediates are characteristic of HM, as compared with FM and BM. The content of non-esterified fatty acids may reflect the hydrolysis of triglycerides in different milk types.

Background: Metabolomics data analyses rely on the use of bioinformatics tools. Many integrated multi-functional tools have been developed for untargeted metabolomics data processing and have been widely used. More alternative platforms are expected for both basic and advanced users. Results: Integrated mass spectrometry-based untargeted metabolomics data mining (IP4M) software was designed and developed. The IP4M, has 62 functions categorized into 8 modules, covering all the steps of metabolomics data mining, including raw data preprocessing (alignment, peak de-convolution, peak picking, and isotope filtering), peak annotation, peak table preprocessing, basic statistical description, classification and biomarker detection, correlation analysis, cluster and sub-cluster analysis, regression analysis, ROC analysis, pathway and enrichment analysis, and sample size and power analysis. Additionally, a KEGG-derived metabolic reaction database was embedded and a series of ratio variables (product/substrate) can be generated with enlarged information on enzyme activity. A new method, GRaMM, for correlation analysis between metabolome and microbiome data was also provided. IP4M provides both a number of parameters for customized and refined analysis (for expert users), as well as 4 simplified workflows with few key parameters (for beginners who are unfamiliar with computational metabolomics). The performance of IP4M was evaluated and compared with existing computational platforms using 2 data sets derived from standards mixture and 2 data sets derived from serum samples, from GC–MS and LC–MS respectively. Conclusion: IP4M is powerful, modularized, customizable and easy-to-use. It is a good choice for metabolomics data processing and analysis. Free versions for Windows, MAC OS, and Linux systems are provided.