Gut microbiota-derived indole 3-propionic acid protects against radiation toxicity via retaining acyl-CoA-binding protein.

BACKGROUND: We have proved fecal microbiota transplantation (FMT) is an efficacious remedy to mitigate acute radiation syndrome (ARS); however, the mechanisms remain incompletely characterized. Here, we aimed to tease apart the gut microbiota-produced metabolites, underpin the therapeutic effects of FMT to radiation injuries, and elucidate the underlying molecular mechanisms. RESULTS: FMT elevated the level of microbial-derived indole 3-propionic acid (IPA) in fecal pellets from irradiated mice. IPA replenishment via oral route attenuated hematopoietic system and gastrointestinal (GI) tract injuries intertwined with radiation exposure without precipitating tumor growth in male and female mice. Specifically, IPA-treated mice represented a lower system inflammatory level, recuperative hematogenic organs, catabatic myelosuppression, improved GI function, and epithelial integrity following irradiation. 16S rRNA gene sequencing and subsequent analyses showed that irradiated mice harbored a disordered enteric bacterial pattern, which was preserved after IPA administration. Notably, iTRAQ analysis presented that IPA replenishment retained radiation-reprogrammed protein expression profile in the small intestine. Importantly, shRNA interference and hydrodynamic-based gene delivery assays further validated that pregnane X receptor (PXR)/acyl-CoA-binding protein (ACBP) signaling played pivotal roles in IPA-favored radioprotection in vitro and in vivo. CONCLUSIONS: These evidences highlight that IPA is a key intestinal microbiota metabolite corroborating the therapeutic effects of FMT to radiation toxicity. Owing to the potential pitfalls of FMT, IPA might be employed as a safe and effective succedaneum to fight against accidental or iatrogenic ionizing ARS in clinical settings. Our findings also provide a novel insight into microbiome-based remedies toward radioactive diseases. Video abstract.

[Quantitative analysis of multi-component gas mixture based on KPCA and SVR].

In the present paper, the authors present a new quantitative analysis method of mid-infrared spectrum. The method combines the kernel principal component analysis (KPCA) technique with support vector regress machine (SVR) to createa quantitative analysis model of multi-component gas mixtures. Firstly, the spectra of multi-component gas mixtures samples were mapped nonlinearly into a high-dimensional feature space through the use of Gaussian kernels. And then, PCA technique was employed to compute efficiently the principal components in the high-dimensional feature spaces. After determining the optimal numbers of principal components, the extracted features (principal components) were used as the inputs of SVR to create the quantitative analysis model of seven-component gas mixtures. The prediction RMSE (phi x 10(-6))of seven-component gases of prediction set samples by use of KPCA-SVR model were respectively 124.37, 72.44, 136.51, 87.29, 153.01, 57.12, and 81.72, ten times less than that by use of SVR model. The elapsed time of modeling and prediction by using KPCA-SVR were respectively 46.59 (s) and 4.94 (s), which was consumedly less than 752.52 (s) and 26.21 (s) by using only SVR These results show that KPCA has an excellent ability of nonlinear feature extraction. It can make the most of the information of entire spectra range and effectively reduce noise and the dimension of the spectra. The KPCA combined with SVR can improve the model's analysis precision and cut the elapsed time of modeling and analysis. From our research and experiments, we conclude that KPCA-SVR is an effective new method for infrared spectroscopic quantitative analysis.

[Method of infrared spectrum on-line pattern recognition of mixed gas distribution based on SVM].

In order to solve the difficulties that the spectrum training data samples of the massive mixed gas cannot be actually obtained, the analysis precision is low and it is not real time online analysis in the analysis of mixed gas component concentration, the support vector machine, a new information processing method, was used in the mixed gas infrared spectrum analysis, and the concept of mixed gas distribution pattern was proposed in the present paper. Based on the thought that the mixed gas distribution pattern recognition is carried out first, and then the analysis work of mixed gas component concentration is done, sixty kinds of mixed gas distribution pattern were determined after investigation and study, and 6000 mixed gas spectrum data samples were used for model training and testing. Sequential minimal optimization algorithm was applied to realize the decrement and the increase of online learning, and finally the model of infrared spectrum online pattern recognition of mixed gas distribution based on SVM was established. The model structure is composed of 2 levels, pattern recognition level and result output level. The pattern recognition level completes the task of mixed gas distribution pattern recognition; while the result output level is composed of 60 SVM calibration models, and it completes the task of mixed gas concentration analysis. Experimental results show that the correct recognition rate of mixture gas distribution pattern is not lower than 98.8%, and that the method can be used for online recognition of mixed gas distribution pattern under the conditions of small samples of a mixed gas, and can add new mixed gas online, and it has the practical application value.