Abstract:

Objective

To investigate the metabolic alterations of human placental mesenchymal stem cell (hPMSC) therapy on Crohn's disease model mice based on fecal metabolomics, and to screen biomarkers related to efficacy evaluation.

Methods

A total of 25 SAMP1 / Yit mice with spontaneous chronic ileitis were used as Crohn's disease models and divided into a treatment group (n= 15) and a disease group (n = 10). Additionally, 10 AKR /J mice were used as healthy controls. Mice in the treatment group were injected intraperitoneally with hPMSC, and mice in the disease group and the healthy control group were injected intraperitoneally with an equal volume of phosphate buffered saline at the same time points. On the 0th, 3rd, 7th, and 14th days after treatment, fecal samples of mice in the three groups were obtained, and chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS) was used for metabolomic analysis.

Results

The fecal metabolomic analysis based on CIL LC-MS detected 3 504 mass spectral peak pairs, of which 77.77% could be positively or putatively identified. Partial least squares-discriminant analysis (PLS-DA) was performed according to the mass spectrometry data of the metabolites, and it was found that the metabolic disorders of Crohn's disease mice were restored to a certain extent after treatment. Statistical analysis of the metabolite abundance of mice in each group found that a total of 35 metabolites had significant changes between the disease group and the healthy control group, and between the treatment group and the disease group (P < 0.05, fold change > 1.2). Through stepwise optimization of biomarker combinations, five biomarkers with the best classification performance and biological significance were finally determined by the orthogonal partial least squares-discriminant analysis(OPLS-DA) and machine learning model based on logistic regression algorithm, namely 5-Hydroxyindolepyruvate, p-Synephrine, Isomer 1 of 4-(2-Aminophenyl)-2,4-dioxobutanoic acid, LMetanephrine, and Threonylproline. The combination of these biomarkers achieved an area under the receiver operating characteristic curve of 0.920 with an accuracy of 87.0%.

Conclusion

Five metabolites identified through CIL LC-MS-based fecal metabolomic analysis can serve as a biomarker panel to assess the efficacy of hPMSC in the treatment of Crohn's disease with high accuracy.

Key words: Mesenchymal stem cells, Crohn's disease, Chemical isotope labeling liquid chromatography-mass spectrometry, Metabolomics, Machine learning