RESUMO
Parkinson's disease (PD) is a common neurodegenerative disease with limited treatment and no cure, hence, broadening PD drug spectrum is of great significance. At present, engineered microorganisms are attracting increasing attention. In this study, we constructed an engineered strain of Clostridium butyricum-GLP-1, a C. butyricum (a probiotic) that consistently expresses glucagon-like peptide-1 (GLP-1, a peptide-based hormone with neurological advantage) in anticipation of its use in PD treatment. We further investigated the neuroprotective mechanism of C. butyricum-GLP-1 on PD mice models induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The results indicated that C. butyricum-GLP-1 could improve motor dysfunction and ameliorate neuropathological changes by increasing TH expression and reducing the expression of α-syn. Moreover, we confirmed that C. butyricum-GLP-1 improved microbiome imbalance of PD mice by decreasing the relative abundance of Bifidobacterium at the genus level, improved gut integrity, and upregulated the levels of GPR41/43. Surprisingly, we found it could exert its neuroprotective effects via promoting PINK1/Parkin mediated mitophagy and attenuating oxidative stress. Together, our work showed that C. butyricum-GLP-1 improves PD by promoting mitophagy, which provides an alternative therapeutic modality for PD.
RESUMO
In this paper, 300 samples of desert soil collected in the Ebinur Lake Wetland Nature Reserve of Xinjiang were used as the research subject, and the visible/near-infrared spectra data about the soil obtained with the ASD Field Spec 3 HR spectrometer and the data about total phosphorus in the soil obtained through chemical analysis were used as the data sources; following Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment, the combination of ant colony optimization interval partial least squares (ACO-iPLS) and genetic algorithm interval partial least squares (GA-iPLS) were employed to extract the characteristic wavelengths of the total phosphorus content in desert soil, before the partial least squares regression model for predicting the total-phosphorus content in soil was constructed; and this model was compared with the full-spectrum partial least squares model, ACO-iPLS and GA-iPLS. According to the results: through filte- ring with ACO-iPLS, the total-phosphorus characteristic wavebands in the desert soil were 500-700, 1 101-1 300, 1 501-1 700, and 1 901-2 100 nm; through further variable selection with GA-iPLS, 13 effective wavelengths with the minimum colinearity were selected, which were respectively: 1621, 546, 1259, 573, 1572, 1527, 564, 1 186, 1 988, 1541, 2024, 1 118, and 1 191 nm. According to the comparison of modeling methods, the most accurate model was the one based on the characteristic variables selected with the combination of ACO-iPLS and GA-iPLS, followed by the ones with genetic algorithm, ant colony optimization algorithm and the full spectrum method. For the total phosphorus content in soil model established with the combination of ACO-iPLS and GA-iPLS, the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were respectively 0.122 and 0.108 mg x g(-1), and the related coefficient for cross validation (R(c)) and the related coefficient for prediction (R(p)) were 0.535 7 and 0.555 9, respectively. Therefore, it can be seen that the model constructed through Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment and by using the combination of AGO-iPLS and GA-iPLS has simple structure, high prediction accuracy and good robustness, and can be used for estimating the total phosphorus content in desert soil.
RESUMO
In the present paper, based on the multi-resolution attribute of EEMD (ensemble empirical mode decomposition) method, we presented a new de-noising method for analyzing spectrum, and applied it to process the reflecting spectrum data of 33 soil profiles in the typical oasis located in the middle reaches of the Tarim River. To explore the de-noising effect of EEMD threshold method for reflecting spectrum in soil profiles; we compared EEMD threshold method with wavelet transform method. The results showed that compared with traditional wavelet transform method, the signal to noise ratio (SNR) was improved from 14. 8366 to 34. 2757 dB, and the root mean square error (RMSE) was reduced to 7. 2406 X 10(-6) from 6. 7861 X 10(-5) and the correlation coefficient (r) increased from 0. 9825 to 0. 9998. Therefore, three de-noising effect indicators of EEMD threshold method are better than those of wavelet transform method. This proved that the EEMD threshold method can effectively eliminate the noise of soil-profile spectrum and also preserve the detailed information of the original spectra well. Thus, the analysis precision of the spectrum will be improved. In addition, by contrast with the wavelet threshold method, the EEMD threshold method is adaptive and is fairly reliable. As a new method for spectral pretreatment, the EEMD threshold method will have a good application prospect in spectra de-noising.
