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      Pharmacological Effects of Agastache rugosa against Gastritis Using a Network Pharmacology Approach

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          Abstract

          Agastache rugosa is used as a Korean traditional medicine to treat gastric diseases. However, the active ingredients and pharmacological targets of A. rugosa are unknown. In this study, we aimed to reveal the pharmacological effects of A. rugosa on gastritis by combining a mice model and a network pharmacology method. The macrophage and gastritis-induced models were used to evaluate the pharmacological effects of A. rugosa. The results show that A. rugosa relieved mucosal damage induced by HCl/EtOH in vivo. Network analysis identified 99 components in A. rugosa; six components were selected through systematic screening, and five components were linked to 45 gastritis-related genes. The main components were acacetin and luteolin, and the identified core genes were AKT serine/threonine kinase 1 (AKT1), nuclear factor kappa B inhibitor alpha (NFKBIA), and mitogen-activated protein kinase-3 (MAPK3) etc. in this network. The network of components, target genes, protein–protein interactions, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was closely connected with chemokines and with phosphoinositide 3-kinase-Akt (PI3K/AKT), tumor-necrosis-factor alpha (TNFα), mitogen-activated protein kinase, nuclear factor kappa B, and Toll-like receptor (TLR) pathways. In conclusion, A. rugosa exerts gastro-protective effects through a multi-compound and multi-pathway regulatory network and holds potential for treating inflammatory gastric diseases.

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          An automated method for finding molecular complexes in large protein interaction networks

          Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. Results This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Conclusion Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from .
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            Network medicine: a network-based approach to human disease.

            Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
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              Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment

              The research field of systems biology has greatly advanced and, as a result, the concept of network pharmacology has been developed. This advancement, in turn, has shifted the paradigm from a “one-target, one-drug” mode to a “network-target, multiple-component-therapeutics” mode. Network pharmacology is more effective for establishing a “compound-protein/gene-disease” network and revealing the regulation principles of small molecules in a high-throughput manner. This approach makes it very powerful for the analysis of drug combinations, especially Traditional Chinese Medicine (TCM) preparations. In this work, we first summarized the databases and tools currently used for TCM research. Second, we focused on several representative applications of network pharmacology for TCM research, including studies on TCM compatibility, TCM target prediction, and TCM network toxicology research. Third, we compared the general statistics of several current TCM databases and evaluated and compared the search results of these databases based on 10 famous herbs. In summary, network pharmacology is a rational approach for TCM studies, and with the development of TCM research, powerful and comprehensive TCM databases have emerged but need further improvements. Additionally, given that several diseases could be treated by TCMs, with the mediation of gut microbiota, future studies should focus on both the microbiome and TCMs to better understand and treat microbiome-related diseases.
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                Author and article information

                Journal
                Biomolecules
                Biomolecules
                biomolecules
                Biomolecules
                MDPI
                2218-273X
                09 September 2020
                September 2020
                : 10
                : 9
                : 1298
                Affiliations
                Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, 111 Geonjae-ro, Jeollanam-do 58245, Korea; hhnam@ 123456kiom.re.kr (H.-H.N.); centraline@ 123456kiom.re.kr (J.S.K.); junlee@ 123456kiom.re.kr (J.L.); wnsl1118@ 123456kiom.re.kr (Y.H.S.); hs0320@ 123456kiom.re.kr (H.S.K.); smryu@ 123456kiom.re.kr (S.M.R.); serparas@ 123456kiom.re.kr (G.C.); bcmoon@ 123456kiom.re.kr (B.C.M.)
                Author notes
                [* ]Correspondence: lay7709@ 123456kiom.re.kr ; Tel.: +82-61-338-7128; Fax: +82-61-338-7136
                Author information
                https://orcid.org/0000-0003-2180-4860
                https://orcid.org/0000-0002-5756-5470
                https://orcid.org/0000-0003-0530-0793
                https://orcid.org/0000-0002-5374-6274
                https://orcid.org/0000-0003-1670-8911
                Article
                biomolecules-10-01298
                10.3390/biom10091298
                7565599
                32916904
                649d79c3-524d-4815-a0a5-3bf7ad981e98
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 July 2020
                : 07 September 2020
                Categories
                Article

                agastache rugosa,network pharmacology,gastro-protective effects,anti-inflammation,target gene network,bioactive ingredients,signaling pathway

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