Fine-Scale Characterization of Plant Diterpene Glycosides Using Energy-Resolved Untargeted LC-MS/MS Metabolomics Analysis.

Plant diterpene glycosides are essential for diverse physiological processes. Comprehensive structural characterization proved to be a challenge due to variations in glycosylation patterns, diverse aglycone structures, and the absence of comprehensive reference databases. In this study, a method for fine-scale characterization was proposed based on energy-resolved (ER) untargeted LC-MS/MS metabolomics analysis using steviol glycosides as a demonstration. Energy-dependent fragmentation patterns were unveiled by a series of model compounds. Distinct glycosylation sites were discerned by leveraging varying fragmentation energies for the precursor ions. The sugar moiety linkage at C19OOH (R1) exhibited facile and intact cleavage at low collision energies, while the sugar moiety at C13-OH (R2) demonstrated consecutive cleavage with increasing energy. Aglycone ions exhibited a higher relative intensity at NCE 50, with relative intensities ranging from 95% to 100%. Subsequently, aglycone candidates, R1 sugar composition, and R2 sugar sequence were deduced through ER-MS/MS analysis. The developed method was applied to Stevia rebaudiana leaves. A total of 91 diterpene glycosides were unambiguously identified, including 16 steviol glycosides with novel acetylglycosylation patterns. This method offers a rapid alternative for glycan analysis and the structural differentiation of isomers. The developed method enhances the understanding of diterpene glycosides in plants, providing a reliable tool for the in-depth characterization of complex metabolite profiles.

Leveraging Unidentified Metabolic Features for Key Pathway Discovery: Chemical Classification-driven Network Analysis in Untargeted Metabolomics.

Untargeted metabolomics using liquid chromatography-electrospray ionization-high-resolution tandem mass spectrometry (UPLC-ESI-MS/MS) provides comprehensive insights into the dynamic changes of metabolites in biological systems. However, numerous unidentified metabolic features limit its utilization. In this study, a novel approach, the Chemical Classification-driven Molecular Network (CCMN), was proposed to unveil key metabolic pathways by leveraging hidden information within unidentified metabolic features. The method was demonstrated by using the herbivore-induced metabolic response in corn silk as a case study. Untargeted metabolomics analysis using UPLC-MS/MS was performed on wild corn silk and two genetically modified lines (pre- and postinsect treatment). Global annotation initially identified 256 (ESI-) and 327 (ESI+) metabolites. MS/MS-based classifications predicted 1939 (ESI-) and 1985 (ESI+) metabolic features into the chemical classes. CCMNs were then constructed using metabolic features shared classes, which facilitated the structure- or class annotation for completely unknown metabolic features. Next, 844/713 significantly decreased and 1593/1378 increased metabolites in ESI-/ESI+ modes were defined in response to insect herbivory, respectively. Method validation on a spiked maize sample demonstrated an overall class prediction accuracy rate of 95.7%. Potential key pathways were prescreened by a hypergeometric test using both structure- and class-annotated differential metabolites. Subsequently, CCMN was used to deeply amend and uncover the pathway metabolites deeply. Finally, 8 key pathways were defined, including phenylpropanoid (C6-C3), flavonoid, octadecanoid, diterpenoid, lignan, steroid, amino acid/small peptide, and monoterpenoid. This study highlights the effectiveness of leveraging unidentified metabolic features. CCMN-based key pathway analysis reduced the bias in conventional pathway enrichment analysis. It provides valuable insights into complex biological processes.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

Screening and identification of unknown chemical contaminants in food based on liquid chromatography-high-resolution mass spectrometry and machine learning.

Unknown or unexpected chemical contaminants and/or their transformation products in food that may be harmful to humans need to be discovered for comprehensive safety evaluation. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful tool for detecting chemical contaminants in food samples. However, identifying all of peaks in LC-HRMS is not possible, but if class information is known in advance, further identification will become easier. In this work, a novel MS2 spectra classification-driven screening strategy was constructed based on LC-HRMS and machine learning. First, the classification model was developed based on machine learning algorithm using class information and experimental MS2 data of chemical contaminants and other non-contaminants. By using the developed artificial neural network classification model, in total 32 classes of pesticides, veterinary drugs and mycotoxins were classified with good prediction accuracy and low false-positive rate. Based on the classification model, a screening procedure was developed in which the classes of unknown features in LC-HRMS were first predicted through the classification model, and then their structures were identified under the guidance of class information. Finally, the developed strategy was tentatively applied to the analysis of pork and aquatic products, and 8 chemical contaminants and 11 transformation products belonging to 8 classes were found. This strategy enables screening of unknown chemical contaminants and transformation products in complex food matrices.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach.

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Profiling and annotation of carbonyl compounds in Baijiu Daqu by chlorine isotope labeling-assisted ultrahigh-performance liquid chromatography-high resolution mass spectrometry.

Carbonyl compounds are among the most important flavor substances that affect the taste of Baijiu. However, high coverage analysis of carbonyl compounds is obstructed due to the poor ionization efficiency of these compounds. Here we report a chlorine isotope labeling-assisted ultrahigh-performance liquid chromatography-high resolution mass spectrometry-based method (CIL-UHPLCHRMS) for profiling and annotation of carbonyl compounds in sauce flavored-Baijiu Daqu. 4-Chloro-2-hydrazinylpyridine was demonstrated to be a good labeling reagent that could achieve highly sensitive profiling and high-coverage screening of carbonyl compounds in the absence of heavy isotope labeling reagents. In the analysis of eight carbonyl standards representing different carbonyl categories, l-(-)-fucose, 2-carboxybenzaldehyde, 2-hydroxyacetophenone and heptan-2-one could be ionized only after labeling and MS signals were significantly increased for other 4 standards with an enhancement factor ranging from 181-fold for 3-methoxysalicylaldehyde to 3141-fold for tridecan-2-one. The annotation was achieved based on multidimensional information including MS1, predicted tR, in silico MS/MS and manually annotated fragments. In total, 487 carbonyl compounds were detected in Baijiu Daqu, among which, 314 (64.5%) of them were positively or putatively identified. The outcome of the linearity (with a linear range of 2, 3 orders of magnitude), precision (less than 10%), and limit of detection (varied from 0.07 to 0.10 nM) indicated that the method was adequate for profiling carbonyl compounds in complex biological samples. The established method was successfully applied to study carbonyl compounds in Baijiu Daqu with different colors and different seasons. Taken collectively, the present work provides an effective, simple and economic strategy for comprehensive analysis of carbonyl compounds in complex matrix samples.

Novel Method for Comprehensive Annotation of Plant Glycosides Based on Untargeted LC-HRMS/MS Metabolomics.

Glycosides are a large family of secondary metabolites in plants, which play a critical role in plant growth and development. Due to the complexity and diversity in structures and the limited availability of authentic standards, comprehensive annotation of the glycosides remains a great challenge. In this study, using maize as an example, a deep annotation method of glycosides was proposed based on untargeted liquid chromatography-high-resolution tandem mass spectrometry metabolomics analysis. First, knowledge-based in silico aglycone and glycosyl/acyl-glycosyl libraries were built. A total of 1240 known and potential aglycones from databases and literature were recorded. Next, the MS parameters beneficial to aglycone ion-rich MS/MS were explored using 1782 high-resolution MS/MS spectra of glycosides from the MassBank of North America (MoNA) and confirmed by 52 authentic glycoside standards. Then, screening rules for aglycon ions in MS/MS were recommended. Glycoside candidates were further filtered by MS/MS-based chemical classification and MS/MS similarity of aglycon-glycoside pairs. Finally, the glycosylation sites of flavonoid mono-O-glycosides were recommended by characteristic fragmentation patterns. The developed method was validated using glycosides and nonglycosides from the MoNA library. The annotation accuracy rates were 96.8, 94.9, and 98.0% in negative ion mode (ESI-), positive ion mode (ESI+), and the combined ESI- & ESI+, respectively. The annotation specificity was 99.6% (ESI-), 99.6% (ESI+), and 99.2% (ESI- & ESI+). A total of 274 glycosides (including 34 acyl-glycosides) were tentatively annotated in maize by the developed method. The method enables effective and reliable annotation for plant glycosides.

In-depth profiling of carboxyl compounds in Chinese Baijiu based on chemical derivatization and ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry.

Carboxyl compounds have a significant influence on the flavor of Chinese Baijiu. However, because of the structural diversity and low concentration, the deep profiling of carboxyl compounds in Chinese Baijiu is still challenging. In this work, a systematic method for comprehensive analysis of carboxyl compounds in Chinese Baijiu was established. After derivatized under optimized conditions, 197 p-dimethylaminophenacyl bromide-derived carboxylic compounds were annotated by multidimensional information including accurate mass, predicted tR, in-silico MS/MS, and diagnostic ions for the first time. In addition, 48 of the 197 carboxyl compounds were positively identified, and three of them were newly identified in Chinese Baijiu. Moreover, we found the number and the concentration of carboxyl compounds in sauce-flavor Baijiu were more abundant than in strong-flavor Baijiu. This work provides a novel method for the analysis of carboxyl compounds in Baijiu and other complex samples.

[A novel method for efficient screening and annotation of important pathway-associated metabolites based on the modified metabolome and probe molecules].

Plants produce a wide variety of secondary metabolites in the process of evolution. Secondary metabolites have highly diverse structures due to the modification of the basic skeletons of metabolites. They are required for interaction with the environment and are produced in response to abiotic/biotic stress. Characterization of secondary metabolic pathways is significant to plant molecular breeding and natural product biosynthesis. The liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS) is one of the major techniques for untargeted metabolomics study. The LC-HRMS/MS method could detect tens of thousands of metabolic features and provide abundant structural information. It has been widely used in the discovery and characterization of the secondary metabolome. However, due to the largely diverse structure and limited records in the mass spectral library, the annotation of the secondary metabolome is very difficult. To address the analytical challenges associated with the vast structural diversity and the large numbers of secondary metabolites, particularly those previously unknown structural metabolites, a novel method for the efficient characterization of pathway-associated metabolites was developed. Modification reactions and MS/MS spectral information were collected using the metabolic pathways database and mass spectral library. Screening and annotation of metabolites involved in phenylpropanoid metabolism in maize leaves were used as an example. First, a database of modified groups was established via pathway-associated modifications from open access metabolic pathway database and literature. Here, pathway databases included the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Plant Metabolic Pathways (PlantCyc). A total of 61 modification types were enrolled, including 10 generic and 51 pathway-specific modifications. Modified metabolomes were filtered from untargeted LC-HRMS/MS metabolomics data. Next, MS/MS spectra of the pathway-associated compounds (probe molecules) were collected in the Global Natural Products Social Molecular Networking (GNPS) MS/MS spectral library. The MS/MS of compounds assigned to chemical classes of phenylpropanoids were kept. An MS/MS spectral database of the probe molecules was constructed. It included 2677 spectra of 1542 phenylpropanoid compounds in the positive mode and 814 spectra of 661 phenylpropanoid compounds in the negative mode. Then, an MS/MS molecular network was generated by modified metabolome and probe molecules. The clusters comprising both probe molecules and modified metabolites were kept. To explore more previously unknown structural metabolites, the clusters with one more pathway-specific modified metabolite were retained even though they didn't contain any probe molecule. A total of 392 and 417 phenylpropanoid pathway-related metabolic metabolites were obtained in positive and negative ion modes, respectively. The pathway-associated metabolites were annotated based on the propagation of the molecular network. For the metabolites within the co-cluster, annotations were performed using the probe molecules as the initial seed. The modification group's substructure information was used for network propagation annotation. For the clusters containing only pathway-specific modified metabolites, the annotation is similar to the above process if identified nodes were present within the cluster. Otherwise, de novo annotation was manually executed based on substructure information. Finally, 129 unique metabolites were annotated after integration and removal of redundancy. Ten annotated metabolites were validated using commercially available or synthesized reference compounds. The other annotation results were validated using predicted chemical classes, in silico MS/MS, and predicted retention time. They are mainly involved in the downstream branch of phenylpropanoid pathways, including the flavonoid pathway (8 flavonoids, 19 flavonoid O-glycosides, 32 flavonoid C-glycosides), the hydroxycinnamic acid pathway (31 hydroxycinnamic acids and derivatives), and the lignan pathway (22 neo-lignans/lignan/lignan glycosides). All the annotated structures were searched against the PubChem and SciFinder databases. Among them, 26 metabolites were previously unreported in both the databases. In this study, the pathway-associated metabolites could be quickly discovered and annotated by the integration of probe molecules and modified metabolome. It provides a method for the in-depth study of the phenylpropanoid pathway.

Anti-ageing and Anti-lung Carcinoma Effects of Vulpinic Acid and Usnic Acid Compounds and Biological Investigations with Molecular Modeling Study.

Disorganization and breakdown of extracellular matrix proteins like fibronectin, collagen, and elastin are key characteristics of skin aging due to the increased activation of important proteolytic enzymes like elastases and collagenase enzymes. Also, inhibition of their enzymatic activities by natural molecules might be a promising factor to prevent extrinsic skin aging. All chemicals were obtained from Sigma-Aldrich unless otherwise stated. The assay employed was based on spectrophotometric methods reported in the literature. The collagenase and elastase inhibition assays of some phenolic compounds were performed according to the previous studies. These compounds showed excellent to good inhibitory activities of vulpinic acid against studied these enzymes with IC50 values of 195.36 µM for collagenase and 25.24 µM for elastase. The molecular docking calculations were conducted to investigate the chemical and biological activity of vulpinic acid and usnic acid against collagenase and elastase. The results indicated that these two compounds can interact with the essential residues of the enzymes and affect their activities. The calculations of binding free energies were also performed to obtain more details about the characteristics and free energies of the ligand-enzyme complexes. Additionally, both compounds exhibited the most potent inhibition in the three lung cancer cells, with an IC50 value of 21-68 µM, indicating that vulpinic acid is more potent than Doxorubicin, which exhibited an IC50 value of 21-29 µM.

Nontargeted screening of veterinary drugs and their metabolites in milk based on mass defect filtering using liquid chromatography-high-resolution mass spectrometry.

The development of nontargeted screening strategy for veterinary drugs and their metabolites is very important for food safety. In this study, a nontargeted screening strategy was developed to find the potentially hazardous substances based on mass defect filtering (MDF) using liquid chromatography-high-resolution mass spectrometry. First, the drug metabolites of 112 veterinary drugs from seven classes of antimicrobials were predicted. Second, three MDF models were established, including the traditional rectangular MDF, the enhanced parallelogram MDF, and the polygonal MDF. Finally, the strategy was applied to nontargeted screening of veterinary drugs in 36 milk samples. The polygonal MDF model based on the distribution area of parent drugs and their metabolites showed a better filtering effect. After removing food components and performing MDF, about 10% of the substances remained, and four veterinary drugs and six drug metabolites were discovered and identified, showing the effectiveness of this strategy. The nontargeted screening strategy can rapidly remove interfering substances and find the suspected compounds. It can also be used for nontargeted screening of veterinary drugs and their metabolites in other food matrices.

Nontargeted screening method for veterinary drugs and their metabolites based on fragmentation characteristics from ultrahigh-performance liquid chromatography-high-resolution mass spectrometry.

Nontargeted screening of both veterinary drugs and their metabolites is important for comprehensive safety evaluation of animal-derived foods. In this study, a novel nontargeted screening strategy was developed for veterinary drugs and their metabolites based on fragmentation characteristics from ultrahigh-performance liquid chromatography-high-resolution mass spectrometry. First, an in-house database of mass spectra including 3,710 veterinary drugs and their metabolites was constructed. Second, fragmentation characteristics of parent drugs and their metabolites in mass spectrometry were investigated and summarized. Then, a nontargeted screening procedure was established based on fragmentation characteristics to screen unknown parent drugs and their metabolites. Finally, the strategy was applied to 33 egg samples, and four veterinary drugs and three drug metabolites were determined and identified. These results showed that the developed strategy can realize suspect and nontargeted screening of veterinary drugs and their metabolites, and can also be applied to other animal-derived foods.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies.

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

[Characteristics of agricultural climate resource changes during the growth stages of tobacco during 1961-2017 in Panxi Region, China]. / 1961­2017年攀西烤烟生育期农业气候资源变化特征.

It is of great significance for the sustainable development of regional agriculture to understand the changes of agricultural climate resources during the growth period of local main crops. Based on data from 15 meteorological stations in tobacco planting area of Panxi region, Sichuan, from 1961 to 2017, the average temperature, diurnal temperature range, water deficit, and radiation were used to analyze the temporal and spatial distribution of agricultural climate resources such as light, temperature, and water in the whole growth period and each growth stages respectively. From 1961 to 2017, the average temperature of tobacco during the whole growth period gradually decreased from south to north in Panxi, while temperature showed an increasing trend in most areas. The area with significantly increased temperature accounted for 54.5% of the total planting area. The diurnal temperature range in most area of the eastern Panxi and some of the central Panxi showed an upward trend, and these areas accounted for 76.4% of the whole Panxi region. Water deficit gradually decreased from south to north, with an increasing trend in the whole region. The radiation during the growth stage of tobacco was high in the southwest and low in the northeast, and the climatic tendency was positive values in the southwest. During the study period, average temperature was the highest in the period from vigorous growth to the beginning of maturity, with an overall upward trend. The diurnal temperature rage and water deficit were the highest in transplanting squatting seedling stage. Radiation was the highest at the beginning of maturity, which increased slightly at the beginning of seedling growth.

Interleukin-36 receptor antagonist attenuates atherosclerosis development by inhibiting NLRP3 inflammasome.

Atherosclerosis is characterized, as an inflammatory disorder in the circulatory system, with increasing tendency toward mortality and morbidity. Thus, developing novel therapeutic targeting inflammation is necessary. Here, we investigated the effects of interleukin-36 receptor antagonist (IL-36RN), a newly identified anti-inflammatory factor, on atherosclerosis. The regulation of NLRP3 inflammasome by IL-36RN was determined in vitro in macrophage cells after oxidized low-density lipoprotein (ox-LDL) stimulation. The IL-1ß and caspase-1 p10 secretion were assessed by enzyme-linked immunosorbent assay and western blot analysis. Finally, the IL-36RN/NLRP3 inflammasome pathway was confirmed in apolipoprotein E-deficient mice. IL-36RN suppressed the expression of NLRP3, the secretion of IL-1ß, and caspase-1 p10 in vitro, while IL-36 pathway stimulation activated the NLRP3 inflammasome, which was inhibited by IL-36RN. In the mouse model of atherosclerosis, IL-36RN delivered by the lentivirus vector inhibited the development of atherosclerosis, and the atheroprotective effects of IL-36RN were attenuated by IL-36 pathway stimulation. Furthermore, the regulation of NLRP3 inflammasome by IL-36RN was also confirmed in vivo. We demonstrated here that IL-36RN exerted atheroprotective functions through IL-36RN/NLRP3 inflammasome pathway.

Iridal-type triterpenoids with anti-HBV activity from Iris confusa.

Five new iridal-type triterpenoids, named as spirioiridoconfal A-C (1-3), isobelamcandal (4) and 17-hydroxyl-27-ene-iridal (5), along with one known compound (6) were isolated from the whole plant of Iris confusa. Their structures were determined by extensive spectroscopic analyses. Compounds 3 and 6 showed moderate activity against HBV DNA replication in vitro with the IC50 value at 84.6 ±â€¯24.9 (SI = 2.1) and 58.6 ±â€¯6.1 (SI = 12.7) µM relatively.

New C16-noriridals and formyl-monocycloiridals from the rhizomes of Iris pseudoacorus.

Four new C16-noriridals (1-4) and two reported C16-noriridals (5-6), together with two new formyl-monocycloiridals (7-8) and three known monocycloiridals (9-11) were isolated from the rhizomes of Iris pseudoacorus. Irispseudoacorins A-D (1-4) and iridojaponals A-B (5-6) were C16-noriridals which shared a 6/5/7 tricyclic ring system (1-2, 5-6) or 6/5 tricyclic ring system (3-4). The formyl-monocycloiridals (7-8) were detected in the crude extracts of I. pseudoacorus by LC-MS analysis which suggested they were originated from natural sources rather than artificial products during the isolation. Their structures were determined by UV, IR, extensive NMR spectra and X-ray diffraction analyses. The known monocycloiridals 9-10 displayed pronounced cytotoxic effects against five human tumor cell lines. The IC50 values of 9 were ranging from 12.63 to 24.69µM.

[Shenfu injection induces the apoptosis of prostate cancer PC-3 cells].

OBJECTIVE: To study the effect of Shenfu Injection (SF) on the apoptosis of prostate cancer PC-3 cells and its possible mechanism. METHODS: We divided prostate cancer PC-3 cells into a blank control group and three experimental groups, the latter treated with SF at 50, 100, and 200 microl/ml, respectively, for 24, 48, and 72 hours. Then we determined the proliferation of the cells by MTT assay, measured their apoptosis by Annexin V/PI flow cytometry, and detected the expression of P53 mRNA by RT-qPCR. RESULTS: Compared with the blank control group, the survival rates of the prostate cancer PC-3 cells in the 50, 100, and 200 microl/ml SF groups were (93.76 +/- 2.63)%, (81.21 +/- 1.80)% and (18.01 +/- 3.84)% at 24 hours, (94.67 +/-1.11)%, (78.33 +/- 2.89)% and (10.34 +/- 1.44)% at48 hours, and (91.30 +/- 0.47)%, (36.67 +/- 1.56)% and (1.33 +/- 0.32)% at 72 hours, all significantly increased in a dose- and time-dependent manner (P < 0.05). The expression of p53 mRNA was also markedly increased in all the three experimental groups at 48 hours (P < 0. 05). CONCLUSION: SF can inhibit the proliferation and induce the apoptosis of PC-3 cells, which may due to its upregulation of the p53 mRNA expression.