Untargeted metabolomics reveals that declined PE and PC in obesity may be associated with prostate hyperplasia.

BACKGROUND: Recent studies have shown that obesity may contribute to the pathogenesis of benign prostatic hyperplasia (BPH). However, the mechanism of this pathogenesis is not fully understood. METHODS: A prospective case-control study was conducted with 30 obese and 30 nonobese patients with BPH. Prostate tissues were collected and analyzed using ultra performance liquid chromatography ion mobility coupled with quadrupole time-of-flight mass spectrometry (UPLC-IMS-Q-TOF). RESULTS: A total of 17 differential metabolites (3 upregulated and 14 downregulated) were identified between the obese and nonobese patients with BPH. Topological pathway analysis indicated that glycerophospholipid (GP) metabolism was the most important metabolic pathway involved in BPH pathogenesis. Seven metabolites were enriched in the GP metabolic pathway. lysoPC (P16:0/0:0), PE (20:0/20:0), PE (24:1(15Z)/18:0), PC (24:1(15Z)/14:0), PC (15:0/24:0), PE (24:0/18:0), and PC (16:0/18:3(9Z,12Z,15Z)) were all significantly downregulated in the obesity group, and the area under the curve (AUC) of LysoPC (P-16:0/0/0:0) was 0.9922. The inclusion of the seven differential metabolites in a joint prediction model had an AUC of 0.9956. Thus, both LysoPC (P-16:0/0/0:0) alone and the joint prediction model demonstrated good predictive ability for obesity-induced BPH mechanisms. CONCLUSIONS: In conclusion, obese patients with BPH had a unique metabolic profile, and alterations in PE and PC in these patients be associated with the development and progression of BPH.

LC-MS-based untargeted metabolomics reveals the mechanism underlying prostate damage in a type 2 diabetes mouse model.

Type 2 diabetes mellitus (T2DM) can cause prostate damage and affect male reproductive function, but the underlying mechanisms are not completely understood. In this study, we used liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics to identify endogenous metabolites in the prostate of a T2DM mouse model. The selected endogenous metabolites were then subjected to bioinformatics analysis and metabolic pathway studies to understand their role in the development of T2DM-induced prostate damage. We used male homozygous BTBR ob/ob mice (n = 12) and BTBR WT mice (n = 11) in this study. We monitored changes in blood glucose, body weight, prostate weight, and prostate index, as well as performed hematoxylin and eosin (H&E) staining and observed that the prostate of the BTBR ob/ob was damaged. We then used ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) for metabolomics analysis. The stability of the model was validated using principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Using variable importance in projection (VIP) > 1, false discovery rate (FDR) < 0.05, and coefficient of variation (CV) < 30 as criteria, a total of 149 differential metabolites (62 upregulated and 87 downregulated) were identified between the prostates of the two groups of mice. Topological pathway analysis showed that these differential metabolites were mainly involved in sphingolipid (SP) and glycerophospholipid (GP) metabolism. In conclusion, our study not only emphasizes the damage caused by T2DM to the prostate but also provides new insights into the potential mechanisms of T2DM-induced male reproductive dysfunction.

Bioinformatics analysis reveals lipid metabolism may play an important role in the SiO2-stimulated rat model.

Silicosis is a progressive and irreversible common occupational disease caused by long-term inhalation of a large amount of free silica dust. Its pathogenesis is complex, and the existing prevention and treatment methods can not effectively improve silicosis injury. To uncover potential differential genes in silicosis, SiO2-stimulated rats and their control original transcriptomic data sets GSE49144, GSE32147 and GSE30178 were downloaded for further bioinformatics analysis. We used R packages to extract and standardize transcriptome profiles, then screened differential genes, and enriched GO and KEGG pathways through clusterProfiler packages. In addition, we investigated the role of lipid metabolism in the progression of silicosis by qRT-PCR validation and transfection with si-CD36. A total of 426 differential genes were identified in this study. Based on GO and KEGG enrichment analysis, it was found that lipid and atherosclerosis were significantly enriched. qRT-PCR was used to detect the relative expression level of differential genes in this signaling pathway of silicosis rat models. mRNA levels of Abcg1, Il1b, Sod2, Cyba, Cd14, Cxcl2, Ccl3, Cxcl1, Ccl2 and CD36 increased, mRNA levels of Ccl5, Cybb and Il18 decreased. In addition, at the cellular level, SiO2-stimulated lead to lipid metabolism disorder in NR8383, and silencing CD36 inhibited SiO2-induced lipid metabolism disorder. These results indicate that lipid metabolism plays an important role in the progression of silicosis, and the genes and pathways reported in this study may provide new ideas for the pathogenesis of silicosis.