Abnormal bile acid metabolism is an important feature of gut microbiota and fecal metabolites in patients with slow transit constipation

Destructions in the intestinal ecosystem are implicated with changes in slow transit constipation (STC), which is a kind of intractable constipation characterized by colonic motility disorder. In order to deepen the understanding of the structure of the STC gut microbiota and the relationship between the gut microbiota and fecal metabolites, we first used 16S rRNA amplicon sequencing to evaluate the gut microbiota in 30 STC patients and 30 healthy subjects. The α-diversity of the STC group was changed to a certain degree, and the β-diversity was significantly different, which indicated that the composition of the gut microbiota of STC patients was inconsistent with healthy subjects. Among them, Bacteroides, Parabacteroides, Desulfovibrionaceae, and Ruminiclostridium were significantly upregulated, while Subdoligranulum was significantly downregulated. The metabolomics showed that different metabolites between the STC and the control group were involved in the process of bile acids and lipid metabolism, including taurocholate, taurochenodeoxycholate, taurine, deoxycholic acid, cyclohexylsulfamate, cholic acid, chenodeoxycholate, arachidonic acid, and 4-pyridoxic acid. We found that the colon histomorphology of STC patients was significantly disrupted, and TGR5 and FXR were significantly downregulated. The differences in metabolites were related to changes in the abundance of specific bacteria and patients’ intestinal dysfunction. Analysis of the fecal genomics and metabolomics enabled separation of the STC from controls based on random forest model prediction [STC vs. control (14 gut microbiota and metabolite biomarkers)—Sensitivity: 1, Specificity: 0.877]. This study provided a perspective for the diagnosis and intervention of STC related with abnormal bile acid metabolism

Evaluation of the Strain Bacillus amyloliquefaciens YP6 in Phoxim Degradation via Transcriptomic Data and Product Analysis

Phoxim, a type of organophosphorus pesticide (OP), is widely used in both agriculture and fisheries. The persistence of phoxim has caused serious environmental pollution problems. In this study, Bacillus amyloliquefaciens YP6 (YP6), which is capable of promoting plant growth and degrading broad-spectrum OPs, was used to study phoxim degradation. Different culture media were applied to evaluate the growth and phoxim degradation of YP6. YP6 can grow rapidly and degrade phoxim efficiently in Luria–Bertani broth (LB broth) medium. Furthermore, it can also utilize phoxim as the sole phosphorus source in a mineral salt medium. Response surface methodology was performed to optimize the degradation conditions of phoxim by YP6 in LB broth medium. The optimum biodegradation conditions were 40 °C, pH 7.20, and an inoculum size of 4.17% (v/v). The phoxim metabolites, O,O-diethylthiophosphoric ester, phoxom, and α-cyanobenzylideneaminooxy phosphonic acid, were confirmed by liquid chromatography–mass spectrometry. Meanwhile, transcriptome analysis and qRT-PCR were performed to give insight into the phoxim-stress response at the transcriptome level. The hydrolase-, oxidase-, and NADPH-cytochrome P450 reductase-encoding genes were significantly upregulated for phoxim hydrolysis, sulfoxidation, and o-dealkylation. Furthermore, the phoxim biodegradation pathways by YP6 were proposed, for the first time, based on transcriptomic data and product analysis

Predictive value of TCM clinical index for diabetic peripheral neuropathy among the type 2 diabetes mellitus population: A new observation and insight

Aims: The objectives of this study were to identify clinical predictors of the Traditional Chinese medicine (TCM) clinical index for diabetic peripheral neuropathy (DPN) in type 2 diabetes mellitus (T2DM) patients, develop a clinical prediction model, and construct a nomogram. Methods: We collected the TCM clinical index from 3590 T2DM recruited at the Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine from January 2019 to October 2020. The participants were randomly assigned to either the training group (n = 3297) or the validation group (n = 1426). TCM symptoms and tongue characteristics were used to assess the risk of developing DPN in T2DM patients. Through 5-fold cross-validation in the training group, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection. In addition, using multifactor logistic regression analysis, a predictive model and nomogram were developed. Results: A total of eight independent predictors were found to be associated with the DPN in multivariate logistic regression analyses: advanced age of grading (odds ratio/OR 1.575), smoke (OR 2.815), insomnia (OR 0.557), sweating (OR 0.535), loose teeth (OR 1.713), dry skin (OR 1.831), purple tongue (OR 2.278). And dark red tongue (OR 0.139). The model was constructed using these eight predictor's medium discriminative capabilities. The area under the curve (AUC) of the training set is 0.727, and the AUC of the validation set is 0.744 on the ROC curve. The calibration plot revealed that the model's goodness-of-fit is satisfactory. Conclusions: We established a TCM prediction model for DPN in patients with T2DM based on the TCM clinical index