The harmful intestinal microbial community accumulates during DKD exacerbation and microbiome-metabolome combined validation in a mouse model.

Objective: Diabetic kidney disease (DKD) is one of the most prevalent complications of diabetes mellitus (DM) and is associated with gut microbial dysbiosis. We aim to build a diagnostic model to aid clinical practice and uncover a crucial harmful microbial community that contributes to DKD pathogenesis and exacerbation. Design: A total of 528 fecal samples from 180 DKD patients and 348 non-DKD populations (138 DM and 210 healthy volunteers) from the First Affiliated Hospital of Zhengzhou University were recruited and randomly divided into a discovery phase and a validation phase. The gut microbial composition was compared using 16S rRNA sequencing. Then, the 180 DKD patients were stratified into four groups based on clinical stages and underwent gut microbiota analysis. We established DKD mouse models and a healthy fecal microbiota transplantation (FMT) model to validate the effects of gut microbiota on DKD and select the potential harmful microbial community. Untargeted metabolome-microbiome combined analysis of mouse models helps decipher the pathogenetic mechanism from a metabolic perspective. Results: The diversity of the gut microbiome was significantly decreased in DKD patients when compared with that of the non-DKD population and was increased in the patients with more advanced DKD stages. The DKD severity in mice was relieved after healthy gut microbiota reconstruction. The common harmful microbial community was accumulated in the subjects with more severe DKD phenotypes (i.e., DKD and DKD5 patients and DKD mice). The harmful microbial community was positively associated with the serum injurious metabolites (e.g., cholic acid and hippuric acid). Conclusion: The fecal microbial community was altered markedly in DKD. Combining the fecal analysis of both human and animal models selected the accumulated harmful pathogens. Partially recovering healthy gut microbiota can relieve DKD phenotypes via influencing pathogens' effect on DKD mice's metabolism.

Metabolomics-Based Biosignatures of Prostate Cancer in Patients Following Radiotherapy.

Metabolomics offers new promise for research on prostate cancer (PCa) and its personalized treatment. Metabolomic profiling of radiation-treated PCa patients is particularly important to reveal their new metabolomic status, and evaluate the radiation effects. In addition, bioinformatics-integrated metabolomics-based approaches for disease profiling and assessment of therapy could help develop precision biomarkers in a context of PCa. We report mass spectrometry-based untargeted (global) serum metabolomics findings from patients with PCa (n = 55) before and after treatment with stereotactic body radiation therapy (SBRT), and intensity-modulated radiation therapy (IMRT) with SBRT, and using parsimony phylogenetic analysis. Importantly, the radiation-treated serum metabolome of patients represented a unique robust cluster on a cladogram that was distinct from the pre-RT metabolome. The altered radiation responsive serum metabolome was defined by predominant aberrations in the metabolic pathways of nitrogen, pyrimidine, purine, porphyrin, alanine, aspartate, glutamate, and glycerophospholipid. Our findings collectively suggest that global metabolomics integrated with parsimony phylogenetics offer a unique and robust systems biology analytical platform for powerful unbiased determination of radiotherapy (RT)-associated biosignatures in patients with PCa. These new observations call for future translational research for evaluation of metabolomic biomarkers in PCa prognosis specifically, and response to radiation treatment broadly. Radiation metabolomics is an emerging specialty of systems sciences and clinical medicine that warrants further research and educational initiatives.