Chemical Entities Of Biological Interest Research Articles

Chemical entity normalization for successful translational development of Alzheimer’s disease and dementia therapeutics

BackgroundIdentifying chemical mentions within the Alzheimer’s and dementia literature can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities of Biological Interest (ChEBI) ontology, which is rich in hierarchical and other relationship types, for entity normalization can provide an advantage for future downstream applications. We provide a reproducible hybrid approach that combines an ontology-enhanced PubMedBERT model for disambiguation with a dictionary-based method for candidate selection.ResultsThere were 56,553 chemical mentions in the titles of 44,812 unique PubMed article abstracts. Based on our gold standard, our method of disambiguation improved entity normalization by 25.3 percentage points compared to using only the dictionary-based approach with fuzzy-string matching for disambiguation. For the CRAFT corpus, our method outperformed baselines (maximum 78.4%) with a 91.17% accuracy. For our Alzheimer’s and dementia cohort, we were able to add 47.1% more potential mappings between MeSH and ChEBI when compared to BioPortal.ConclusionUse of natural language models like PubMedBERT and resources such as ChEBI and PubChem provide a beneficial way to link entity mentions to ontology terms, while further supporting downstream tasks like filtering ChEBI mentions based on roles and assertions to find beneficial therapies for Alzheimer’s and dementia.

Anti-Shigella and antioxidant-based screening of some Cameroonian medicinal plants, UHPLC-LIT-MS/MS fingerprints, and prediction of pharmacokinetic and drug-likeness properties of identified chemicals.

Ethnopharmacological relevanceShigella infection is a public health problem responsible for approximately 700,000 deaths annually. The management of this disease is impaired by the emergence of multidrug-resistant Shigella species, highlighting the urgent need to search for alternative treatment options. In this regard, investigating medicinal plants traditionally used for the treatment of dysentery, diarrheal infections, and/or associated symptoms in endemic regions might provide an opportunity to identify phytochemicals that could be further used as a basis for the development of future anti-shigella drug candidates. Aim of the studyThis study was designed to investigate the anti-shigella and antioxidant-based ethnopharmacological potency of some Cameroonian medicinal plants with an emphasis on pharmacokinetic properties of the identified chemical pharmacophore. Materials and methodsBriefly, plant species were selected and collected based on their ethnopharmacological uses and information reported in the literature. Crude aqueous, ethanolic, methanolic, and hydroethanolic (30:70, v/v) extracts from these plants were prepared and then screened for their anti-Shigella activity against four Shigella strains and cytotoxicity against Vero and Raw cell lines using microdilution and resazurin-based methods, respectively. The antioxidant activities of potent extracts were evaluated using DPPH, ABTS, NO, and FRAP scavenging assays. The chemical profile of potent extracts was performed using the UHPLC-LIT-MS/MS and the pharmacokinetic properties, druglikeness, and likely molecular targets of the chemical scaffolds identified were predicted using SwissADME and SwissTargetPredictor. ResultsThirty-nine (39) plants belonging to 26 plant families were harvested. Out of the 228 extracts tested, 18 extracts originating from 6 plants (15.38 %) were active (MICs 250–1000 μg/mL) and nontoxic toward Vero (CC50 129.25–684.55 μg/mL) and Raw cell lines (CC50 336.20 to >1000 μg/mL). Six potent extracts from the two plants exhibited moderate to potent DPPH (SC50 8.870–54.410 μg/mL), ABTS (SC50 12.020–27.36 μg/mL), and NO (SC50 0.02–195.85 μg/mL) scavenging activities. Later, these extracts showed interesting ferric iron-reducing power (1.28–12.14 μg equivalent NH2OH/g of extract). The shortest onset of action time (4 and 6 h) observed following inhibition kinetics studies was observed with extracts BFSHE, PMSE, and PMSM. The UHPLC-LIT-MS/MS and some databases (Mass Spectral Library (NIST 14), Human Metabolome Database (HMD), MassBank, SuperNatural 3.0, The Food Database (FooDB), and Chemical Entities of Biological Interest (ChEBI)) allowed the annotation of 18 and 17 metabolites in the extracts from stem bark of P. macrophylla and B. ferruginea respectively. Pharmacokinetic prediction of these chemicals showed that compound 6 (4,6a-bis(Hydroxymethyl)-9a-methyl-3-oxo-1a,1b,3,5,6,6a,7a,9a-octahydrobis (oxireno)[2′,3':5,6; 2″,3'':9,10]cyclodeca[1,2-b]furan-5-yl methacrylate), compound 8 (Corynoxeine), and compounds 35 (Stachybotrydial acetate) demonstrated acceptable druglike and pharmacokinetic properties and might act through inhibition of kinase, transferase, protease, oxidoreductase, and family AG protein-linked receptors. ConclusionThe findings from this investigation demonstrated that Cameroonian medicinal plants are suitable reservoirs of anti-Shigella and antioxidant agents with good drug candidate properties.

Development of an embedded molecular structure-based model for prediction of micropollutant treatability in a drinking water treatment plant by machine learning from three years monitoring data

In this study, an autoencoder-based molecular structure embedding model was developed to predict treatability of micropollutant in a drinking water treatment plant (DWTP) by machine learning using 69 micropollutants monitoring data at 18 DWTPs for three years. The molecular structure, which contains physicochemical characteristics, was embedded as a fixed-length vector that is advantageous for data-driven analysis and machine learning. First, the molecular structure of the micropollutants was converted to a sequence of tokens using the simplified molecular-input line-entry system (SMILES) pair encoding tokenizer, a frequency-based tokenization method. It was then compressed into fixed-length vectors using an autoencoder trained on various molecular structures within the Chemical Entities of Biological Interest. To validate the proposed models, a binary classification of micropollutant treatability was performed using the embedded molecular structure of micropollutants with various external features, such as concentration, season, and the presence of specific drinking water treatment processes by machine learning. The accuracy of the developed model for the 69 micropollutants in this study was 0.86, and the molecular structure was determined to be the most important feature. Furthermore, an accuracy of 0.71 was obtained in external validation for pharmaceuticals and personal care products that were not used for training. This shows that the proposed embedding vector can be generalized to unseen molecules during the training process, which means that it reflects the characteristics of the molecular structures.

Annotation of biologically relevant ligands in UniProtKB using ChEBI.

To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands. We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides. Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/). Supplementary data are available at Bioinformatics online.

Towards the prediction of non-peptidic epitopes.

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69–0.96 depending on the molecule cluster.

Rhea, the reaction knowledgebase in 2022.

Rhea (https://www.rhea-db.org) is an expert-curated knowledgebase of biochemical reactions based on the chemical ontology ChEBI (Chemical Entities of Biological Interest) (https://www.ebi.ac.uk/chebi). In this paper, we describe a number of key developments in Rhea since our last report in the database issue of Nucleic Acids Research in 2019. These include improved reaction coverage in Rhea, the adoption of Rhea as the reference vocabulary for enzyme annotation in the UniProt knowledgebase UniProtKB (https://www.uniprot.org), the development of a new Rhea website, and the designation of Rhea as an ELIXIR Core Data Resource. We hope that these and other developments will enhance the utility of Rhea as a reference resource to study and engineer enzymes and the metabolic systems in which they function.

Interaction of SARS-CoV-2 spike protein with angiotensin converting enzyme inhibitors and selected compounds from the chemical entities of biological interest

BackgroundRecent COVID-19 outbreak has prompted the search of novel therapeutic agents to treat the disease. The initial step of the infection involves the binding of the virus through the viral spike protein with the host angiotensin converting enzyme 2 (ACE2). In this study, the interaction of some ACE or ACE2 inhibitors and their analogues as well as selected compounds with the viral spike protein as a strategy to hinder viral-ACE2 interaction were investigated. SARS-CoV-2 spike protein as well as the ligands were retrieved from protein databank and ChEBI database respectively. The molecules were prepared before initiating the virtual screening using PyRx software. Discovery studio was used to further visualize the binding interactions between the compounds and the protein.ResultsThe ACE inhibitors and their analogues fosinopril (1-), fosinopril and moexipril have the best binding affinity to the protein with binding energies < − 7.0 kcal/mol while non-flavonoid stilben-4-ol binds with free binding energy of − 7.1 kcal/mol. Others compounds which belong to either the flavonoids, terpenes and alkaloid classes also have binding energies < − 7.0 kcal/mol. Such high binding energies were enhanced via hydrogen bond (h-bond) interactions in addition to other interactions observed between the compounds and the amino acid residues of the protein.ConclusionsThe ACE inhibitors and their analogues as well as the selected compounds could serve as inhibitors of the spike protein as well as lead in drug discovery processes to target the SARS-CoV-2 virus.

Mycotoxin producers in the VKM mycelial fungi collection

The article provides information about All-Russian Collection of Microorganisms’ (VKM) fungi – potential producers of mycotoxins. A comparative analysis of the VKM database of the diversity of mycelial fungi with ChEBI (Chemical Entities of Biological Interest) and ChEMBL (Chemical Database of European Molecular Biology Laboratory) databases containing information on various chemical compounds was performed. It confirms that the fungi of VKM collection represent significant objects for a comprehensive study of their metabolome. Currently described mycotoxins for representatives of 44 genera of mycelial fungi of VKM are presented. More research in this area will allow a more active development of mycotoxin studies in representatives of hitherto poorly studied fungal taxa using the VKM collection fund.

Mixology: a tool for calculating required masses and volumes for laboratory solutions

Laboratory work often requires making up solutions with defined concentrations of various components. Mixology is a tool we have created to simplify calculation of the masses and volumes required to obtain particular concentrations. It operates with many kinds of volumetric, mass and concentration units, including conversion between molarity- and mass-based concentrations using molecular masses retrieved from the Chemical Entities of Biological Interest (ChEBI) database. Mixology can be accessed at https://mixology.science.

Mixology: a tool for calculating required masses and volumes for laboratory solutions.

Laboratory work often requires making up solutions with defined concentrations of various components. Mixology is a tool we have created to simplify calculation of the masses and volumes required to obtain particular concentrations. It operates with many kinds of volumetric, mass and concentration units, including conversion between molarity- and mass-based concentrations using molecular masses retrieved from the Chemical Entities of Biological Interest (ChEBI) database. Mixology can be accessed at https://mixology.science.

Computer-Aided Drug Discovery Identifies Alkaloid Inhibitors of Parkinson's Disease Associated Protein, Prolyl Oligopeptidase.

Parkinson's disease is a common neurodegenerative disorder marked by the accumulation of the protein alpha synuclein. Studies have indicated the role of prolyl oligopeptidase (POP), a serine protease, in alpha synuclein accumulation. Therefore, POP emerges as an attractive medicinal target. Traditionally, most of the early medicines have been plant-based owing to their ready availability and negligible side effects. Alkaloids owing to their neurotransmitter modulatory, anti-amyloid, anti-oxidant, and anti-inflammatory activities have shown potential in neurodegenerative disease. In this work, we computationally evaluated alkaloid class of phytochemicals for their therapeutic efficacy against POP. Alkaloids were retrieved from the publically available database, Chemical Entities of Biological Interest (ChEBI), and screened for their drug likeness (Lipinski's rule of 5) and absorption, distribution, metabolism, and excretion, and toxicity (ADMET) in Discovery Studio by ensuring parameters suitable for a central nervous system disease such as blood-brain barrier (BBB) level set to ≤2, absorption level set to 0 and solubility level permitted set to 2, 3, or 4. Next, molecular docking was performed to learn about the affinity of the filtered alkaloids with the POP. Subsequently, molecular dynamic simulations were conducted to assess the reliability and stability of the alkaloid-protein complex. Our study identified metergoline, pipercallosine, celacinnine, lobeline, cystodytin G, lycoperine A, hookerianamide J, and martefragin A as putative lead compounds against POP. Among these, metergoline, pipercallosine, hookerianamide J, and lobeline showed the most promising results. These compounds demonstrated better or equivalent molecular docking scores in comparison to three POP inhibitors that had reached clinical trials, i.e., Z-321, S-17092, and JTP-4819. MD simulations indicated that these compounds remained intact at the active site while adhering to the binding mode and interaction patterns as that of the reported inhibitors. The research conducted here, therefore, provides evidence for conducting in vitro POP inhibitory studies of these newly identified plant-based POP inhibitors.

Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB.

The UniProt Knowledgebase UniProtKB is a comprehensive, high-quality, and freely accessible resource of protein sequences and functional annotation that covers genomes and proteomes from tens of thousands of taxa, including a broad range of plants and microorganisms producing natural products of medical, nutritional, and agronomical interest. Here we describe work that enhances the utility of UniProtKB as a support for both the study of natural products and for their discovery. The foundation of this work is an improved representation of natural product metabolism in UniProtKB using Rhea, an expert-curated knowledgebase of biochemical reactions, that is built on the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Knowledge of natural products and precursors is captured in ChEBI, enzyme-catalyzed reactions in Rhea, and enzymes in UniProtKB/Swiss-Prot, thereby linking chemical structure data directly to protein knowledge. We provide a practical demonstration of how users can search UniProtKB for protein knowledge relevant to natural products through interactive or programmatic queries using metabolite names and synonyms, chemical identifiers, chemical classes, and chemical structures and show how to federate UniProtKB with other data and knowledge resources and tools using semantic web technologies such as RDF and SPARQL. All UniProtKB data are freely available for download in a broad range of formats for users to further mine or exploit as an annotation source, to enrich other natural product datasets and databases.

A substrate-based ontology for human solute carriers.

Solute carriers (SLCs) are the largest family of transmembrane transporters in the human genome with more than 400 members. Despite the fact that SLCs mediate critical biological functions and several are important pharmacological targets, a large proportion of them is poorly characterized and present no assigned substrate. A major limitation to systems‐level de‐orphanization campaigns is the absence of a structured, language‐controlled chemical annotation. Here we describe a thorough manual annotation of SLCs based on literature. The annotation of substrates, transport mechanism, coupled ions, and subcellular localization for 446 human SLCs confirmed that ~30% of these were still functionally orphan and lacked known substrates. Application of a substrate‐based ontology to transcriptomic datasets identified SLC‐specific responses to external perturbations, while a machine‐learning approach based on the annotation allowed us to identify potential substrates for several orphan SLCs. The annotation is available at https://opendata.cemm.at/gsflab/slcontology/. Given the increasing availability of large biological datasets and the growing interest in transporters, we expect that the effort presented here will be critical to provide novel insights into the functions of SLCs.

Coronavirus Disease 2019 (COVID-19): Do Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers Have a Biphasic Effect?

Coronavirus Disease 2019 (COVID-19): Do Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers Have a Biphasic Effect?

Biomedical ontologies and their development, management, and applications in and beyond China

Abstract Since the boom of biomedical big data studies, various big data processing technologies have been developed rapidly. As an important form of knowledge representation, ontology has become an important means for the utilization and integration of biomedical big data. The emergence of new technologies for ontology development has resulted in the generation of many biomedical ontologies by many ontology development communities. The Open Biological and Biomedical Ontology Foundry, an academic organization for bio-ontology developers, has provided a set of principles to guide community-based open ontology construction. The Open Biological and Biomedical Ontology Foundry have also built many widely used ontologies, such as Gene Ontology, Human Phenotype Ontology, and Chemical Entities of Biological Interest. Other various ontology repositories have also been created and used to support ontology reuse. Many efficient tools for ontology applications, such as data annotation and terms mapping, have also been developed. High quality ontologies are also being used to develop new methods and tools for biomedical data analysis. The applications of Gene Ontology and Human Phenotype Ontology for data analysis and integration in recent years are reviewed here. To promote the development and applications of biomedical ontologies in China, a research community, OntoChina, was founded recently. OntoChina aims to support the development of reference ontologies, especially bilingual and Chinese translated ontologies. OntoChina also encourages ontology developers to follow the Open Biological and Biomedical Ontology Foundry principles.

Analysis of Low Molecular Weight Substances and Related Processes Influencing Cellular Cholesterol Efflux.

Cholesterol efflux is the key process protecting the vascular system from the development of atherosclerotic lesions. Various extracellular and intracellular events affect the ability of the cell to efflux excess cholesterol. To explore the possible pathways and processes that promote or inhibit cholesterol efflux, we applied a combined cheminformatic and bioinformatic approach. We performed a comprehensive analysis of published data on the various substances influencing cholesterol efflux and found 153 low molecular weight substances that are included in the Chemical Entities of Biological Interest (ChEBI) database. Pathway enrichment was performed for substances identified within the Reactome database, and 45 substances were selected in 93 significant pathways. The most common pathways included the energy-dependent processes related to active cholesterol transport from the cell, lipoprotein metabolism and lipid transport, and signaling pathways. The activators and inhibitors of cholesterol efflux were non-uniformly distributed among the different pathways: the substances influencing ‘biological oxidations’ activate cholesterol efflux and the substances influencing ‘Signaling by GPCR and PTK6’ inhibit efflux. This analysis may be used in the search and design of efflux effectors for therapies targeting structural and functional high-density lipoprotein deficiency.

Enzyme annotation in UniProtKB using Rhea.

MotivationTo provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ChEBI (Chemical Entities of Biological Interest) ontology.ResultsWe replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that Rhea and ChEBI provide.Availability and implementationUniProtKB at https://www.uniprot.org; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/; Rhea at https://www.rhea-db.org.

BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies

BackgroundRecent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.ResultsWe propose a new model to detect and classify relations in text, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using open biomedical ontologies, specifically Chemical Entities of Biological Interest (ChEBI), Human Phenotype, and Gene Ontology. We assessed the performance of BO-LSTM with drug-drug interactions mentioned in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved the F1-score of both the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. We adapted an existing DDI extraction model with our ontology-based method, obtaining a higher F1 score than the original model. Furthermore, we developed and made available a corpus of 228 abstracts annotated with relations between genes and phenotypes, and demonstrated how BO-LSTM can be applied to other types of relations.ConclusionsOur findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.

A Structure- and Ligand-Based Virtual Screening of a Database of "Small" Marine Natural Products for the Identification of "Blue" Sigma-2 Receptor Ligands.

Sigma receptors are a fascinating receptor protein class whose ligands are actually under clinical evaluation for the modulation of opioid analgesia and their use as positron emission tomography radiotracers. In particular, peculiar biological and therapeutic functions are associated with the sigma-2 (σ2) receptor. The σ2 receptor ligands determine tumor cell death through apoptotic and non-apoptotic pathways, and the overexpression of σ2 receptors in several tumor cell lines has been well documented, with significantly higher levels in proliferating tumor cells compared to quiescent ones. This acknowledged feature has found practical application in the development of cancer cell tracers and for ligand-targeting therapy. In this context, the development of new ligands that target the σ2 receptors is beneficial for those diseases in which this protein is involved. In this paper, we conducted a search of new potential σ2 receptor ligands among a database of 1517 “small” marine natural products constructed by the union of the Seaweed Metabolite and the Chemical Entities of Biological Interest (ChEBI) Databases. The structures were passed through two filters that were constituted by our developed two-dimensional (2D) and three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) statistical models, and successively docked upon a σ2 receptor homology model that we built according to the FASTA sequence of the σ2/TMEM97 (SGMR2_HUMAN) receptor.

Multitargeted Molecular Docking Study of Natural-Derived Alkaloids on Breast Cancer Pathway Components.

Targeting of multiple sites is a pharmacologically, pharmacokinetic and dynamically more acceptable approach for complex diseases such as BC. It is recommended that the women who are at high risk of developing BC might be given foods enhanced by indole alkaloids from vegetables like cabbage and broccoli. Administration of indole-3-carbinol is associated with decreased incidence of hormone-responsive BC (HRBC) which is implicated due to the induction of cytochrome P450 and glutathione-S-transferase which metabolizes chemical mutagens and by altering estrogen metabolism. To determine the molecular mechanism behind the anticancer activity of natural indole alkaloids present in various food and nutraceuticals products by utilizing Induced-fit docking (IFD) approach. Indole alkaloids were obtained from the database maintained by ChEBI (The database and ontology of Chemical Entities of Biological Interest) with ChEBI id 38958. The 3-dimentional and X-ray structure coordinates of Estrogen receptor- α (ER-α), Estrogen receptor- β (ER-β), and aromatase were obtained from protein data bank with PDB id codes 3ERT, 3OLS, and 3S7S (www.rcsb.org). The Induced fit molecular docking and ADME properties were calculated using Maestro 9.6. IFD analysis showed that bromocriptine exhibits maximum binding affinity towards ER-α and fellutanine B towards ER-β and aromatase. Present research provided in-depth analysis of molecular mechanism and helped in the future design of new pharmacophores based on natural indole alkaloids targeting BC.