Sodium butyrate ameliorates diabetic retinopathy in mice via the regulation of gut microbiota and related short-chain fatty acids.

BACKGROUND: Diabetic retinopathy (DR) development is associated with disturbances in the gut microbiota and related metabolites. Butyric acid is one of the short-chain fatty acids (SCFAs), which has been found to possess a potential antidiabetic effect. However, whether butyrate has a role in DR remains elusive. This study aimed to investigate the effect and mechanism of sodium butyrate supplementation on DR. METHODS: C57BL/6J mice were divided into three groups: Control group, diabetic group, and diabetic with butyrate supplementation group. Type 1 diabetic mouse model was induced by streptozotocin. Sodium butyrate was administered by gavage to the experimental group daily for 12 weeks. Optic coherence tomography, hematoxylin-eosin, and immunostaining of whole-mount retina were used to value the changes in retinal structure. Electroretinography was performed to assess the retinal visual function. The tight junction proteins in intestinal tissue were evaluated using immunohistochemistry. 16S rRNA sequencing and LC-MS/MS were performed to determine the alteration and correlation of the gut microbiota and systemic SCFAs. RESULTS: Butyrate decreased blood glucose, food, and water consumption. Meanwhile, it alleviated retinal thinning and activated microglial cells but improved electroretinography visual function. Additionally, butyrate effectively enhanced the expression of ZO-1 and Occludin proteins in the small intestine. Crucially, only butyric acid, 4-methylvaleric acid, and caproic acid were significantly decreased in the plasma of diabetic mice and improved after butyrate supplementation. The deeper correlation analysis revealed nine genera strongly positively or negatively correlated with the above three SCFAs. Of note, all three positively correlated genera, including norank_f_Muribaculaceae, Ileibacterium, and Dubosiella, were significantly decreased in the diabetic mice with or without butyrate treatment. Interestingly, among the six negatively correlated genera, Escherichia-Shigella and Enterococcus were increased, while Lactobacillus, Bifidobacterium, Lachnospiraceae_NK4A136_group, and unclassified_f_Lachnospiraceae were decreased after butyrate supplementation. CONCLUSION: Together, these findings demonstrate the microbiota regulating and diabetic therapeutic effects of butyrate, which can be used as a potential food supplement alternative to DR medicine.

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis.

A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.

Compound Opening Arrow Mixture exerts anti-tumor effects in a mouse model of breast cancer.

Compound Opening Arrow Mixture (COAM) has demonstrated therapeutic effects in patients with breast cancer. We explored the underlying molecular mechanisms of COAM using a mouse model of breast cancer. Luciferase-labeled 4T1-Luc2 cells were inoculated into the breast pad of BALB/c-nu mice, which were divided into model group (saline), COAM (6 g/ml high-dose, 3 g/ml medium-dose, and 1.5 g/ml low-dose) groups, and low-molecular-weight heparin (LMWH, 1500 U/Kg) group. The number and distribution of 4T1-luc2 tumors were measured by an in vivo imaging system. Tumor cell apoptosis was measured through TUNEL and quantitating the expression of Caspase-3 mRNA and protein. Compared with the model group, in vivo tumor growth was lower in the LMWH- and COAM-treated groups. Tumor apoptosis was time-dependent and dose-dependent, as shown by a higher TUNEL apoptotic index and higher Caspase-3 mRNA and Caspase-3/cleaved-Caspase-3 proteins levels on the 14th day than the 7th day. The COAM high-dose group had the highest apoptotic index and the most activation of Caspase-3. Collectively, COAM significantly inhibits the growth of 4T1-luc2 breast cancer in mice and induces tumor apoptosis by activating Caspase-3, which provides a preliminary explanation of therapeutic effects of COAM.

A Visual Circuit Related to Habenula Underlies the Antidepressive Effects of Light Therapy.

Light plays a pivotal role in the regulation of affective behaviors. However, the precise circuits that mediate the impact of light on depressive-like behaviors are not well understood. Here, we show that light influences depressive-like behaviors through a disynaptic circuit linking the retina and the lateral habenula (LHb). Specifically, M4-type melanopsin-expressing retinal ganglion cells (RGCs) innervate GABA neurons in the thalamic ventral lateral geniculate nucleus and intergeniculate leaflet (vLGN/IGL), which in turn inhibit CaMKIIα neurons in the LHb. Specific activation of vLGN/IGL-projecting RGCs, activation of LHb-projecting vLGN/IGL neurons, or inhibition of postsynaptic LHb neurons is sufficient to decrease the depressive-like behaviors evoked by long-term exposure to aversive stimuli or chronic social defeat stress. Furthermore, we demonstrate that the antidepressive effects of light therapy require activation of the retina-vLGN/IGL-LHb pathway. These results reveal a dedicated retina-vLGN/IGL-LHb circuit that regulates depressive-like behaviors and provide a potential mechanistic explanation for light treatment of depression.

[NIR Assignment of Magnolol by 2D-COS Technology and Model Application Huoxiangzhengqi Oral Liduid].

Near infrared (NIR) spectroscopy assignment of Magnolol was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. According to the synchronous spectra of deuterated chloroform solvent and Magnolol, 1365~1455, 1600~1720, 2000~2181 and 2275~2465 nm were the characteristic absorption of Magnolol. Connected with the structure of Magnolol, 1440 nm was the stretching vibration of phenolic group O-H, 1679 nm was the stretching vibration of aryl and methyl which connected with aryl, 2117, 2304, 2339 and 2370 nm were the combination of the stretching vibration, bending vibration and deformation vibration for aryl C-H, 2445 nm were the bending vibration of methyl which linked with aryl group, these bands attribut to the characteristics of Magnolol. Huoxiangzhengqi Oral Liduid was adopted to study the Magnolol, the characteristic band by spectral assignment and the band by interval Partial Least Squares (iPLS) and Synergy interval Partial Least Squares (SiPLS) were used to establish Partial Least Squares (PLS) quantitative model, the coefficient of determination Rcal(2) and Rpre(2) were greater than 0.99, the Root Mean of Square Error of Calibration (RM-SEC), Root Mean of Square Error of Cross Validation (RMSECV) and Root Mean of Square Error of Prediction (RMSEP) were very small. It indicated that the characteristic band by spectral assignment has the same results with the Chemometrics in PLS model. It provided a reference for NIR spectral assignment of chemical compositions in Chinese Materia Medica, and the band filters of NIR were interpreted.

Targeted metabolomic study indicating glycyrrhizin's protection against acetaminophen-induced liver damage through reversing fatty acid metabolism.

The present study aimed to give a short report on a possible mechanism of glycyrrhizin to acetaminophen-induced liver toxicity. Seven-day intraperitoneal administration of glycyrrhizin (400 mg/kg/day) to 2- to 3-month-old male C57BL/6N mice (mean weight 27 g) significantly prevents acetaminophen-induced liver damage, as indicated by the activity of alanine transaminase and aspartate aminotransferase. Metabolomics analysis and principal component analysis (PCA) using ultra-fast liquid chromatography coupled to triple time-of-flight mass spectrometer were performed. PCA separated well the control, glycyrrhizin-treated, acetaminophen-treated, and glycyrrhizin+acetaminophen-treated groups. Long-chain acylcarnitines were listed as the top ions that contribute to this good separation, which include oleoylcarnitine, palmitoylcarnitine, palmitoleoylcarnitine, and myristoylcarnitine. The treatment of glycyrrhizin significantly reversed the increased levels of long-chain acylcarnitines induced by acetaminophen administration. In conclusion, this metabolomic study indicates a significant glycyrrhizin protection effect against acetaminophen-induced liver damage through reversing fatty acid metabolism.

[Multivariate detection limits of baicalin in qingkailing injection based on four NIR spectrometer types].

Multivariate detection limits (MDLs) of different types of near-infrared instruments were investigated to guide the selection of device type for TCM NIR analysis. In this paper, near-infrared spectroscopy of Qingkailing injection was performed in transmission mode on four near-infrared spectrometers named a, b, c and d, respectively. High performance liquid chromatography (HPLC) was used as the reference method to determine the content of baicalin in Qingkailing injection. Partial least squares (PLS) and interval partial least squares (iPLS) quantitative models of baicalin in Qingkailing injection were established and MDLs of quantitative models based on different types of instruments were calculated. The determination coefficient of prediction (R2(pre))and the root mean square errors of prediction (SEP) of PLS models of four different near-infrared spectrometers are 0.9762 and 230.4 microg x mL(-1) (a), 0.9561 and 246.4 microg x mL(-1) (b), 0.9662 and 264.4 microg x mL*-1) (c), 0.9985 and 71.5 microg x mL(-1) (d). And the model of instrument d shows a better prediction performance than the other three types. There are no remarkable superiorities in predictability in iPLS models of instruments a and b after variable selection, since the R2(pre) and SEP values for instruments a and b are 0.9771 and 218.4 microg x mL(-1), and 0.9754 and 219.4 microg x mL(-1), respectively. Models c and d show no results of variable selection. MDLs (delta(0.05, 0.05) of different instruments are all less than 250 microg x mL(-1), and the MDLs of instruments c and d reach to 58 and 2.9 microg x mL(-1) respectively. The results reveal that the predictability of models and corresponding MDLs are different for different detection equipments. This paper innovatively used the theory of MDL to investigate the detection performance of different types of NIR instruments. The results demonstrated the feasibility of the approach. And it is expected that in actual applications, choosing the right type of instrument should be based on the characteristics of the study carrier to ensure quantitative accuracy.

NIR spectroscopy as a process analytical technology (PAT) tool for monitoring and understanding of a hydrolysis process.

The use of near infrared spectroscopy was investigated as a process analytical technology to monitor the amino acids concentration profile during hydrolysis process of Cornu Bubali. A protocol was followed, including outlier selection using relationship plot of residuals versus the leverage level, calibration models using interval partial least squares and synergy interval partial least squares (SiPLS). A strategy of four robust root mean square error of predictions (RMSEP) values have been developed to assess calibration models by means of the desirability index. Furthermore, multivariate quantification limits (MQL) values of the optimum model were determined using two types of error. The SiPLS(3) models for L-proline, L-tyrosine, L-valine, L-phenylalanine and L-lysine provided excellent accuracies with RMSEP values of 0.0915 mg/mL, 0.1605 mg/mL, 0.0515 mg/mL, 0.0586 mg/mL and 0.0613 mg/mL, respectively. The MQL ranged from 90 ppm to 810 ppm, which confirmed that these models can be suitable for most applications.

A novel model selection strategy using total error concept.

Our previous work had proved that accuracy profile theory could be employed as a means of validating one PLS model in Chinese material medica system. In this paper, accuracy profile theory is proposed as a powerful decision tool to demonstrate the prediction performance of multi-model at each concentration level rather than all concentration levels. Partial least square (PLS), interval partial least square (iPLS), backward interval partial least square (BiPLS) and moving window partial least square (MWPLS) were selected to construct visible and near-infrared (vis/NIR) spectroscopy models. Chemometric indicators, i.e., determination coefficient (R(2)), root-mean-square error of prediction (RMSEP) and ratio of performance to inter-quartile (RPIQ), were used to select the optimum model. However, the results clarified that these commonly used indicators could not clearly demonstrate different PLS models' ability because these indicators depend on all concentration levels to assess the multi-model. Therefore, "total error concept" (accuracy profile theory) was introduced to assess the ability of multi-model at each concentration level. Analytical methodology parameters, i.e., linearity, relative bias, uncertainty, repeatability, intermediate precision, lower limit of quantification (LLOQ) and risk, were calculated by accuracy profile theory. Final results showed that model selection strategy which was based on model assessment at every concentration level was more sensitive than the one based on all concentration levels. The analytical procedures involved in this work ensure that model selection strategy using total error concept is coherent.