Construction of Prediction Models for the Transient Receptor Potential Vanilloid Subtype 1 (TRPV1)-Stimulating Activity of Ginger and Processed Ginger Based on LC-HRMS Data and PLS Regression Analyses

Correlation between the metabolic profile of Nelumbo Seed, a component of Seishinrenshiin, and its inhibitory activity on bladder smooth muscle contraction

Seishinrenshiin (SRI), a Kampo formula, is often used for frequent urination. Nelumbo Seed, a crude-drug component of SRI was reported to inhibit bladder smooth muscle contraction using excised rat bladder tissues. However, the active ingredients of Nelumbo Seed have not yet been identified. In this study, we investigated the active ingredients of Nelumbo Seed that inhibit bladder smooth muscle contraction. We obtained liquid chromatography/mass spectrometry profiles of extracts prepared from five types of Nelumbo Seed materials from different production areas and evaluated their inhibitory effects on excised rat bladder smooth muscle contraction. Analysis of these data using orthogonal projections to latent structures revealed neferine (Nef) as the compound with the highest variable influence on projection. Among the Nelumbo Seed materials processed using different methods, the Nef content was the highest in 'Sekirenshi', when embryo and pericarp were not removed at all. We determined the effects of administering Nelumbo Seed extracts with different Nef content on frequent-urination model rats. The Nef content in rat plasma was the highest when the Sekirenshi extract was administered, but Nef was not detected when seed embryos were removed. In this model, administration of the Nelumbo Seed extract improved the maximum bladder contraction pressure and bladder contraction interval, albeit not significantly. In particular, Sekirenshi tended to reduce the maximum bladder contraction pressure compared to other Nelumbo Seed. Our results indicate that although Nef in Nelumbo Seed does not clearly improve frequent urination, it might contribute to the improvement of urination disorder.

Development of a determination method for quality control markers utilizing metabolic profiling and its application on processed Zingiber officinale Roscoe rhizome

This study established an Orthogonal Partial Least Squares (OPLS) model combining 1H-NMR and GC-MS data to identify characteristic metabolites in complex extracts. Both in metabolomics studies, and natural product chemistry, the reliable identification of marker metabolites usually requires laborious isolation and purification steps, which remains a bottleneck in many studies. Both ginger (GR) and processed ginger (PGR) are listed in the Japanese pharmacopeia. The plant of origin, the rhizome of Zingiber officinale Roscoe, is differently processed for these crude drugs. Notably, the quality of crude drugs is affected by genetic and environmental factors, making it difficult to maintain a certain quality standard. Therefore, characteristic markers for the quality control of GR and PGR are required. Metabolomic analysis using 1H-NMR was able to discriminate between GR and PGR, but there were unidentified signals that were difficult to distinguish based on NMR data alone. Therefore, we combined 1H-NMR and GC-MS analytical data to identify them by OPLS. As a result, αr-curcumene was found to be a useful marker for these identifications. This new approach enabled rapid identification of characteristic marker compounds and reduced the labor involved in the isolation process.

Improved quantitative analysis of tenuifolin using hydrolytic continuous-flow system to build prediction models for its content based on near-infrared spectroscopy

This study used two types of analyses and statistical calculations on powdered samples of Polygala root (PR) and Senega root (SR): (1) determination of saponin content by an independently developed quantitative analysis of tenuifolin content using a flow reactor, and (2) near-infrared spectroscopy (NIR) using crude drug powders as direct samples for metabolic profiling. Furthermore, a prediction model for tenuifolin content was developed and validated using multivariate analysis based on the results of (1) and (2). The goal of this study was to develop a rapid analytical method utilizing the saponin content and explore the possibility of quality control through a wide-area survey of crude drugs using NIR spectroscopy. Consequently, various parameters and appropriate wavelengths were examined in the regression analysis, and a model with a reasonable contribution rate and prediction accuracy was successfully developed. In this case, the wavenumber contributing to the model was consistent with that of tenuifolin, confirming that this model was based on saponin content. In this series of analyses, we have succeeded in developing a model that can quickly estimate saponin content without post-processing and have demonstrated a brief way to perform quality control of crude drugs in the clinical field and on the market.

Structure and core taxa of bacterial communities involved in extracellular electron transfer in paddy soils across China

Microbial communities with extracellular electron transfer (EET) activity are capable of driving geochemical changes and cycles, but a comprehensive understanding of the key microbiota responsible for EET in complex soil matrices is still lacking. Herein, the EET activities, in terms of maximum current density (j_max) and accumulated charge output (C_out), of 41 paddy soils across China were evaluated from the exoelectrogenic properties with a conventional bioelectrochemical system (BES). The j_max with a range of 8.85 × 10^-4 to 0.41 A/m² and C_out with a range of 0.27 to 172.21C were obtained from these soil-based BESs. The bacterial community analyses revealed that the most abundant phylum, order, and genus were Firmicutes, Clostridiales, and Clostridum-sensus-stricto 10, respectively. Bacterial network analysis displayed the positive correlations between the majority of electroactive bacteria-containing genera and multiple other genera, indicating their underlying cooperation for the EET. Partial least squares regression (PLSR) model showed remarkable performance in describing the EET activity with 75 most abundant genera as input variables, identified that 32 genera were very important for governing the EET activities. Multiple linear regression (MLR) analyses further prioritized that the genera norank-c-Berkelbacteria and Fonticella were the key contributors, while the genus Paenibacillus was the key competitor against bacterial exoelectrogenesis in paddy soils. Moreover, the spearman analysis showed that the abundance of these keystone taxa was mainly influenced by the carbon content and pH. This approach provides a promising avenue to monitor the microbial activities in paddy soils as well as the links between microbial community composition and ecological function.

Remedia Sternutatoria over the Centuries: TRP Mediation

Sneezing (sternutatio) is a poorly understood polysynaptic physiologic reflex phenomenon. Sneezing has exerted a strange fascination on humans throughout history, and induced sneezing was widely used by physicians for therapeutic purposes, on the assumption that sneezing eliminates noxious factors from the body, mainly from the head. The present contribution examines the various mixtures used for inducing sneezes (remedia sternutatoria) over the centuries. The majority of the constituents of the sneeze-inducing remedies are modulators of transient receptor potential (TRP) channels. The TRP channel superfamily consists of large heterogeneous groups of channels that play numerous physiological roles such as thermosensation, chemosensation, osmosensation and mechanosensation. Sneezing is associated with the activation of the wasabi receptor, (TRPA1), typical ligand is allyl isothiocyanate and the hot chili pepper receptor, (TRPV1), typical agonist is capsaicin, in the vagal sensory nerve terminals, activated by noxious stimulants.

Pre etched Ag nanocluster as SERS substrate for the rapid quantification of AFB1 in peanut oil via DFT coupled multivariate calibration

The current study extends the use of surface-enhanced Raman spectroscopy (SERS) combined with density functional theory (DFT) and multivariate calibration towards the rapid quantification of aflatoxin B1 (AFB1) in peanut oil samples. It reports the design of pre etched Ag nanocluster as an active SERS substrate for quantifying AFB1, after being impregnated on its surface. The SERS spectra of AFB1@pre etched Ag nanocluster was recorded and its respective theoretical spectrum was calculated by density functional theory (DFT) to assign the characteristic peaks. The baseline drift and rotation effects were masked by the first-order derivative preprocessing method followed by multivariate calibration. The BP-AdaBoost model exhibited optimum prediction (Rp = 0.9283 and 0.9332) ability over the concentration range 5-100 and 100-1000 ngmL^-1, respectively. The limit of detection calculated was 5.0 ngmL^-1 and the obtained recoveries were in the range from 90.4% to 113.1% in spiked peanut oil samples. Additionally, precision analysis revealed an RSD ca. 5%, suggesting the applicability of the pre etched Ag nanocluster SERS substrate towards AFB1 detection. Thus, the proposed SERS platform exploiting DFT and BP-AdaBoost model was found reproducible for the quantification of AFB1 in peanut oil.

A comparative UPLC-Q/TOF-MS-based metabolomics approach for distinguishing Zingiber officinale Roscoe of two geographical origins

Ginger, the rhizome of Zingiber officinale Roscoe, is a popular spice used in the food, beverage and confectionary industries. In this study, we report an untargeted UPLC-Q/TOF-MS-based metabolomics approach for comprehensively discriminating between ginger from two geographical locations, Ghana in West Africa and China. Forty batches of fresh ginger from both countries were discriminated using principal component analysis and orthogonal partial least squares discrimination analysis. Sixteen differential metabolites were identified between the gingers from the two geographical locations, six of which were identified as the marker compounds responsible for the discrimination. Our study highlights the essence and predictive power of metabolomics in detecting minute differences in same varieties of plants/plant samples based on the levels and composition of their metabolites.