Automated evaluation of consistency within the PubChem Compound database

Effects of phytase/ethanol treatment on aroma characteristics of rapeseed protein isolates

This study investigates enhancing the flavor of rapeseed protein isolate (RPI), a protein-rich substance with an unfavorable taste, through phytase/ethanol treatment. Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOF-MS) analysis identified 268 volatile compounds in RPI. The study found that this treatment significantly altered the content of these compounds, reducing sourness and enhancing sweetness and fruitiness. The analysis also showed that the treatment notably increased the relative odor activity values (ROAVs) of key aroma compounds, improving RPI's flavor. Sensory evaluation confirmed the positive impact of the treatment, indicating its potential to make RPI a more acceptable ingredient in the food industry.

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.

“Drying effect” of fructus aurantii components and the mechanism of action based on network pharmacology and in vitro pharmacodynamic validation

Background: Fructus aurantii (FA) is the dried, unripe fruit of the plant Citrus aurantium L. and its cultivated varieties. We investigated the drying effect of FA components and how this drying affect is achieved.

Methods: We employed systems pharmacology to predict the components and targets of FA that produce its drying effect. These predictions were verified by computer simulation and animal experiments. In the latter, we measured the bodyweight, water consumption, urine output, fecal water content, rate of salivary secretion, and cross-sectional area of the long axis of the submandibular gland of mice. Immunohistochemistry was used to measure expression of aquaporin (AQP)5 in the submandibular gland, AQP2 in the kidney, and AQP3 in the colon. ELISA kits were used to measure the horizontal variation of cyclic adenosine monsophosphate (cAMP), cyclic guanosine monophosphate (cGMP) and interferon-γ.

Results: Sixty-seven potentially active components of FA were screened out. FA could produce a drying effect after regulating 214 targets through 66 active components. A total of 870 gene ontology (GO) terms and 153 signaling pathways were identified. The hypoxia inducible factor-1 signaling pathway, phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway, calcium signaling pathway, and Ras signaling pathway may have important roles in the drying effect of FA. Four components of FA were identified: sinensetin, tangeretin, 5-demethylnobiletin and chrysin. These four components could increase the serum level of interferon-γ and ratio of cyclic adenosine monophosphate:cyclic guanosine monophosphate in mice, and affect their water consumption, urine output, fecal water content and rate of salivary secretion.

Conclusion: Four components of FA (tangeretin, sinensetin, chrysin, 5-Demethylmobiletin) were closely related to the Janus kinase-signal transducer and activator of transcription-3 (JAK-STAT3), PI3K-AKT, and the other signaling pathways. They can regulate the protein expression of JAK2, STAT3, PI3K, lymphocyte cell-specific protein-tyrosine kinase, vascular endothelial growth factor A, and protein kinase B1, affect water metabolism in the body and, finally, result in a drying effect.

Pomegranate Peel in the Amelioration of High-Altitude Disease: A Network Pharmacology and Molecular Docking Study of Underlying Mechanisms

High-altitude disease (HAD) describes the failure to adapt to the lack of oxygen found at high altitudes and therapeutic antioxidant effects have been attributed to pomegranate peel (PP) extract. Network pharmacology, molecular docking, and experimental validation were used to study mechanisms responsible for the alleviation of HAD by PP. The aim was to establish a reference for future research and aid technological development, particularly in clinical settings. Network pharmacology analysis showed that PP affected many targets in HAD via the active ingredients, luteolin 7-O-glycoside, punicalagin, and ellagic acid. HNRNPA1, HSPA1B, HSPA1A, CUL4B, CLTC, PPP1CA, PARP1, RACK1, NEDD8, and MAP3K1 were all targets, responsible for effects on ribosomes, apoptosis, cell cycle, mRNA surveillance pathway, and the MAPK signaling pathway. PP had an antiapoptosis effect on H9c2 cells damaged by hypoxia, as shown by annexinV-FITC/PI double staining. Practical Applications. HAD comprises a group of diseases caused by failure to adapt to a low-oxygen environment. PP extract has previously been shown to have antioxidant effects. PP attenuated damage to H9c2 cells and reduced the apoptosis rate. The current results lay the foundation for further experimental investigations.

Mechanism of Tao Hong Decoction in the treatment of atherosclerosis based on network pharmacology and experimental validation

Background

Atherosclerosis (AS) has long been recognized as a cardiovascular disease and stroke risk factor. A well-known traditional Chinese medicine prescription, Tao Hong decoction (THD), has been proven effective in treating AS, but its mechanism of action is still unclear.

Objective

To assess the effects, explore THD's primary mechanism for treating AS, and provide a basis for rational interpretation of its prescription compatibility.

Methods

Based on network pharmacology, we evaluated the mechanism of THD on AS by data analysis, target prediction, the construction of PPI networks, and GO and KEGG analysis. AutoDockTools software to conduct Molecular docking. Then UPLC-Q-TOF-MS was used to identify significant constituents of THD. Furthermore, an AS mice model was constructed and intervened with THD. Immunofluorescence, RT-qPCR, and Western blot were used to verify the critical targets in animal experiments.

Results

The network pharmacology results indicate that eight core targets and seven core active ingredients play an essential role in this process. The GO and KEGG analysis results suggested that the mechanism is mainly involved in Fluid shear stress and atherosclerosis and Lipid and atherosclerosis. The molecular docking results indicate a generally strong affinity. The animal experiment showed that THD reduced plaque area, increased plaque stability, and decreased the levels of inflammatory cytokines (NF-κB, IL-1α, TNF-α, IL-6, IL-18, IL-1β) in high-fat diet -induced ApoE-/-mice. Decreased levels of PTGS2, HIF-1α, VEGFA, VEGFC, FLT-4, and the phosphorylation of PI3K, AKT, and p38 were detected in the THD-treated group.

Conclusion

THD plays a vital role in treating AS with multiple targets and pathways. Angiogenesis regulation, oxidative stress regulation, and immunity regulation consist of the crucial regulation cores in the mechanism. This study identified essential genes and pathways associated with the prognosis and pathogenesis of AS from new insights, demonstrating a feasible method for researching THD's chemical basis and pharmacology.

Biological Magnetic Resonance Data Bank

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics. Advances including GHz-class NMR instruments, national and trans-national NMR cyberinfrastructure, hybrid structural biology methods and machine learning are driving increases in the amount, type, and applications of NMR data in the biosciences. BMRB is a Core Archive and member of the World-wide Protein Data Bank (wwPDB).

Network Pharmacology and Molecular Docking to Explore the Mechanism of Kangxian Decoction for Epilepsy

Purpose

Kangxian decoction (KXD) has been used in clinical practice to treat epilepsy. The purpose of this study was to explore the active components of KXD and clarify its antiepileptic mechanism through network pharmacology and molecular docking.

Methods

The components of KXD were collected from the Encyclopedia of Traditional Chinese Medicine (ETCM) database and the literature was searched. Then, active ingredients were screened by SwissADME and potential targets were predicted by the SwissTargetPrediction database. Epilepsy-related differentially expressed genes were downloaded from the Gene Expression Omnibus database. A component-target-pathway network was constructed with Cytoscape. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein‒protein interaction network analysis revealed the potential mechanism and critical targets. Receiver operating characteristic (ROC) curves and box plots in microarray data validated the good diagnostic value and significant differential expression of these critical genes. Molecular docking verified the association between active ingredients and essential target proteins.

Results

In our study, we screened the important compounds of KXD for epilepsy, including quercetin, baicalin, kaempferol, yohimbine, geissoschizine methyl ether, baicalein, etc. KXD may exert its therapeutic effect on epilepsy through the following targets: PTGS2, MMP9, CXCL8, ERBB2, and ARG1, acting on the following pathways: neuroactive ligand-receptor interactions, arachidonic acid metabolism, IL-17, TNF, NF-kappa B, and MAPK signaling pathways. The molecular docking results showed that the active ingredients in KXD exhibited good binding ability to the key targets.

Conclusion

In this study, we explored the possibility that KXD for epilepsy may act on multiple targets through multiple active ingredients, involving neurotransmitters and neuroinflammatory pathways, providing a theoretical basis for subsequent clinical and experimental studies that will help develop effective new drugs to treat epilepsy.

Exploring the Therapeutic Effects of Black Ginseng on Non-alcoholic Fatty Liver Disease by Using Network Pharmacology and Molecular Docking

This study aimed to investigate the therapeutic effect of BG on non-alcoholic fatty liver disease (NAFLD) using network pharmacology combined with the molecular docking strategy. The saponin composition of BG was analyzed by liquid chromatography-mass spectrometry (LC-MS) instrument. Then the network pharmacology was applied to explore the potential targets and related mechanisms of BG in the treatment of NAFLD. After screening out key targets, molecular docking was used to predict the binding modes between ginsenoside and target. Finally, a methionine and choline deficiency (MCD) diet-induced NAFLD mice model was established to further confirm the therapeutic effect of BG on NAFLD. Twenty-four ginsenosides were annotated based on the MS and tandem MS information. Ten proteins were screened out as key targets closely related to BG treatment of NAFLD. The molecular docking showed that most of the ginsenosides had good binding affinities with ALT1. The validation experiment revealed that BG administration could reduce serum ALT, and AST levels and improve the MCD diet-induced histological changes in liver tissue. Moreover, BG could upregulate the phosphorylation level of AKT in the liver of NAFLD mice, thereby exerting the therapeutic effect on NAFLD. Further studies on the active ginsenosides as well as their synergistic action on NAFLD will be required to reveal the underlying mechanisms in-depth. This study demonstrates that network pharmacological prediction in conjunction with molecular docking is a viable technique for screening the active chemicals and related targets of BG that can be applied to other herbal medicines.

Metabolovigilance: Associating Drug Metabolites with Adverse Drug Reactions

The Metabolovigilance database (https://pharmacogenomics.clas.ucdenver.edu/pharmacogenomics/side-effect/) is a single repository of information on over 15,920 pharmaceuticals and the compounds expected to result from metabolism of these drugs. Metabolovigilance functions as both a web server, providing data directly to users and as a web application, applying user inputs to create logic statements that curate the data presented or downloaded. Using this tool, it is easy to collect information on drugs, their side effects, and the metabolites associated with specific side effects. Information on these compounds can be sorted based on physical properties of the drugs and their metabolites. All of this information can be viewed, sorted, and downloaded for use in other applications. This open-access tool will facilitate molecular studies on the causes of adverse drug reactions and is well suited to integrate with genomic data furthering the goals of personalized medicine.

Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

Probabilistic identification of saccharide moieties in biomolecules and their protein complexes

The chemical composition of saccharide complexes underlies their biomedical activities as biomarkers for cardiometabolic disease, various types of cancer, and other conditions. However, because these molecules may undergo major structural modifications, distinguishing between compounds of saccharide and non-saccharide origin becomes a challenging computational problem that hinders the aggregation of information about their bioactive moieties. We have developed an algorithm and software package called "Cheminformatics Tool for Probabilistic Identification of Carbohydrates" (CTPIC) that analyzes the covalent structure of a compound to yield a probabilistic measure for distinguishing saccharides and saccharide-derivatives from non-saccharides. CTPIC analysis of the RCSB Ligand Expo (database of small molecules found to bind proteins in the Protein Data Bank) led to a substantial increase in the number of ligands characterized as saccharides. CTPIC analysis of Protein Data Bank identified 7.7% of the proteins as saccharide-binding. CTPIC is freely available as a webservice at (http://ctpic.nmrfam.wisc.edu).

Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.