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Discovery of Selenocysteine as a Potential Nanomedicine Promotes Cartilage Regeneration With Enhanced Immune Response by Text Mining and Biomedical Databases.

ABSTRACT: Background:Unlike bone tissue, little progress has been made regarding cartilage regeneration, and many challenges remain. Furthermore, the key roles of cartilage lesion caused by traumas, focal lesion, or articular overstress remain unclear. Traumatic injuries to the meniscus as well as its degeneration are important risk factors for long-term joint dysfunction, degenerative joint lesions, and knee osteoarthritis (OA) a chronic joint disease characterized by degeneration of articular cartilage and hyperosteogeny. Nearly 50% of the individuals with meniscus injuries develop OA over time. Due to the limited inherent self-repair capacity of cartilage lesion, the Biomaterial drug-nanomedicine is considered to be a promising alternative. Therefore, it is important to elucidate the gene potential regeneration mechanisms and discover novel precise medication, which are identified through this study to investigate their function and role in pathogenesis. Methods:We downloaded the mRNA microarray statistics GSE117999, involving paired cartilage lesion tissue samples from 12 OA patients and 12 patients from a control group. First, we analyzed these statistics to recognize the differentially expressed genes (DEGs). We then exposed the gene ontology (GO) annotation and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses for these DEGs. Protein-protein interaction (PPI) networks were then constructed, from which we attained eight significant genes after a functional interaction analysis. Finally, we identified a potential nanomedicine attained from this assay set, using a wide range of inhibitor information archived in the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results:Sixty-six DEGs were identified with our standards for meaning (adjusted P-value < 0.01, |log2 - FC| ?1.2). Furthermore, we identified eight hub genes and one potential nanomedicine - Selenocysteine based on these integrative data. Conclusion:We identified eight hub genes that could work as prospective biomarkers for the diagnostic and biomaterial drug treatment of cartilage lesion, involving the novel genes CAMP, DEFA3, TOLLIP, HLA-DQA2, SLC38A6, SLC3A1, FAM20A, and ANO8. Meanwhile, these genes were mainly associated with immune response, immune mediator induction, and cell chemotaxis. Significant support is provided for obtaining a series of novel gene targets, and we identify potential mechanisms for cartilage regeneration and final nanomedicine immunotherapy in regenerative medicine.

SUBMITTER: Ye J

PROVIDER: S-EPMC7394085 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Discovery of Selenocysteine as a Potential Nanomedicine Promotes Cartilage Regeneration With Enhanced Immune Response by Text Mining and Biomedical Databases.

Ye Jing J Xu Bingbing B Fan Baoshi B Zhang Jiying J Yuan Fuzhen F Chen Yourong Y Sun Zewen Z Yan Xin X Song Yifan Y Song Shitang S Yang Meng M Yu Jia-Kuo JK

Frontiers in pharmacology 20200724

<h4>Background</h4>Unlike bone tissue, little progress has been made regarding cartilage regeneration, and many challenges remain. Furthermore, the key roles of cartilage lesion caused by traumas, focal lesion, or articular overstress remain unclear. Traumatic injuries to the meniscus as well as its degeneration are important risk factors for long-term joint dysfunction, degenerative joint lesions, and knee osteoarthritis (OA) a chronic joint disease characterized by degeneration of articular ca ...[more]

PMID: 32792959

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Project description:BackgroundGenes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.ResultsIn this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.ConclusionGEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

Project description:Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.

Dataset Information

Discovery of Selenocysteine as a Potential Nanomedicine Promotes Cartilage Regeneration With Enhanced Immune Response by Text Mining and Biomedical Databases.

Publications

Discovery of Selenocysteine as a Potential Nanomedicine Promotes Cartilage Regeneration With Enhanced Immune Response by Text Mining and Biomedical Databases.

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