MACC: a visual interactive knowledgebase of metabolite-associated cell communications
Abstract Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become availabl...
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Veröffentlicht in: | Nucleic acids research 2024-01, Vol.52 (D1), p.D633-D639 |
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creator | Gao, Jian Mo, Saifeng Wang, Jun Zhang, Mou Shi, Yao Zhu, Chuhan Shang, Yuxuan Tang, Xinyue Zhang, Shiyue Wu, Xinwen Xu, Xinyan Wang, Yiheng Li, Zihao Zheng, Genhui Chen, Zikun Wang, Qiming Tang, Kailin Cao, Zhiwei |
description | Abstract
Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/
Graphical Abstract
Graphical Abstract |
doi_str_mv | 10.1093/nar/gkad914 |
format | Article |
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Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/
Graphical Abstract
Graphical Abstract</description><identifier>ISSN: 0305-1048</identifier><identifier>EISSN: 1362-4962</identifier><identifier>DOI: 10.1093/nar/gkad914</identifier><identifier>PMID: 37897362</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Databases, Factual ; Humans ; Knowledge Bases</subject><ispartof>Nucleic acids research, 2024-01, Vol.52 (D1), p.D633-D639</ispartof><rights>The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. 2024</rights><rights>The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c357t-25df8649070c9da07825a015237b653b087ce5ad50e18044604202b9038699343</citedby><cites>FETCH-LOGICAL-c357t-25df8649070c9da07825a015237b653b087ce5ad50e18044604202b9038699343</cites><orcidid>0000-0003-1165-1928 ; 0000-0002-0508-6056 ; 0000-0001-5380-2435 ; 0000-0002-0446-7471</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,1598,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37897362$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gao, Jian</creatorcontrib><creatorcontrib>Mo, Saifeng</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Zhang, Mou</creatorcontrib><creatorcontrib>Shi, Yao</creatorcontrib><creatorcontrib>Zhu, Chuhan</creatorcontrib><creatorcontrib>Shang, Yuxuan</creatorcontrib><creatorcontrib>Tang, Xinyue</creatorcontrib><creatorcontrib>Zhang, Shiyue</creatorcontrib><creatorcontrib>Wu, Xinwen</creatorcontrib><creatorcontrib>Xu, Xinyan</creatorcontrib><creatorcontrib>Wang, Yiheng</creatorcontrib><creatorcontrib>Li, Zihao</creatorcontrib><creatorcontrib>Zheng, Genhui</creatorcontrib><creatorcontrib>Chen, Zikun</creatorcontrib><creatorcontrib>Wang, Qiming</creatorcontrib><creatorcontrib>Tang, Kailin</creatorcontrib><creatorcontrib>Cao, Zhiwei</creatorcontrib><title>MACC: a visual interactive knowledgebase of metabolite-associated cell communications</title><title>Nucleic acids research</title><addtitle>Nucleic Acids Res</addtitle><description>Abstract
Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/
Graphical Abstract
Graphical Abstract</description><subject>Databases, Factual</subject><subject>Humans</subject><subject>Knowledge Bases</subject><issn>0305-1048</issn><issn>1362-4962</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNp90M1LwzAYx_EgipvTk3fJSQSpe_LWJt5G8Q0mXty5pGk24tpmNunE_96OTY-ecvnwe8IXoUsCdwQUm7a6m67WulKEH6ExYSlNuErpMRoDA5EQ4HKEzkL4ACCcCH6KRiyTKhvgGC1eZ3l-jzXeutDrGrs22k6b6LYWr1v_VdtqZUsdLPZL3NioS1-7aBMdgjdOR1thY-saG980feuMjs634RydLHUd7MXhnaDF48N7_pzM355e8tk8MUxkMaGiWsqUK8jAqEpDJqnQQARlWZkKVoLMjBW6EmCJBM5T4BRoqYDJVCnG2QTd7Hc3nf_sbYhF48LuP7q1vg8FlXI4BAToQG_31HQ-hM4ui03nGt19FwSKXcdi6FgcOg766jDcl42t_uxvuAFc74HvN_8u_QD7lnrx</recordid><startdate>20240105</startdate><enddate>20240105</enddate><creator>Gao, Jian</creator><creator>Mo, Saifeng</creator><creator>Wang, Jun</creator><creator>Zhang, Mou</creator><creator>Shi, Yao</creator><creator>Zhu, Chuhan</creator><creator>Shang, Yuxuan</creator><creator>Tang, Xinyue</creator><creator>Zhang, Shiyue</creator><creator>Wu, Xinwen</creator><creator>Xu, Xinyan</creator><creator>Wang, Yiheng</creator><creator>Li, Zihao</creator><creator>Zheng, Genhui</creator><creator>Chen, Zikun</creator><creator>Wang, Qiming</creator><creator>Tang, Kailin</creator><creator>Cao, Zhiwei</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1165-1928</orcidid><orcidid>https://orcid.org/0000-0002-0508-6056</orcidid><orcidid>https://orcid.org/0000-0001-5380-2435</orcidid><orcidid>https://orcid.org/0000-0002-0446-7471</orcidid></search><sort><creationdate>20240105</creationdate><title>MACC: a visual interactive knowledgebase of metabolite-associated cell communications</title><author>Gao, Jian ; Mo, Saifeng ; Wang, Jun ; Zhang, Mou ; Shi, Yao ; Zhu, Chuhan ; Shang, Yuxuan ; Tang, Xinyue ; Zhang, Shiyue ; Wu, Xinwen ; Xu, Xinyan ; Wang, Yiheng ; Li, Zihao ; Zheng, Genhui ; Chen, Zikun ; Wang, Qiming ; Tang, Kailin ; Cao, Zhiwei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-25df8649070c9da07825a015237b653b087ce5ad50e18044604202b9038699343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Databases, Factual</topic><topic>Humans</topic><topic>Knowledge Bases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gao, Jian</creatorcontrib><creatorcontrib>Mo, Saifeng</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Zhang, Mou</creatorcontrib><creatorcontrib>Shi, Yao</creatorcontrib><creatorcontrib>Zhu, Chuhan</creatorcontrib><creatorcontrib>Shang, Yuxuan</creatorcontrib><creatorcontrib>Tang, Xinyue</creatorcontrib><creatorcontrib>Zhang, Shiyue</creatorcontrib><creatorcontrib>Wu, Xinwen</creatorcontrib><creatorcontrib>Xu, Xinyan</creatorcontrib><creatorcontrib>Wang, Yiheng</creatorcontrib><creatorcontrib>Li, Zihao</creatorcontrib><creatorcontrib>Zheng, Genhui</creatorcontrib><creatorcontrib>Chen, Zikun</creatorcontrib><creatorcontrib>Wang, Qiming</creatorcontrib><creatorcontrib>Tang, Kailin</creatorcontrib><creatorcontrib>Cao, Zhiwei</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Nucleic acids research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gao, Jian</au><au>Mo, Saifeng</au><au>Wang, Jun</au><au>Zhang, Mou</au><au>Shi, Yao</au><au>Zhu, Chuhan</au><au>Shang, Yuxuan</au><au>Tang, Xinyue</au><au>Zhang, Shiyue</au><au>Wu, Xinwen</au><au>Xu, Xinyan</au><au>Wang, Yiheng</au><au>Li, Zihao</au><au>Zheng, Genhui</au><au>Chen, Zikun</au><au>Wang, Qiming</au><au>Tang, Kailin</au><au>Cao, Zhiwei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MACC: a visual interactive knowledgebase of metabolite-associated cell communications</atitle><jtitle>Nucleic acids research</jtitle><addtitle>Nucleic Acids Res</addtitle><date>2024-01-05</date><risdate>2024</risdate><volume>52</volume><issue>D1</issue><spage>D633</spage><epage>D639</epage><pages>D633-D639</pages><issn>0305-1048</issn><eissn>1362-4962</eissn><abstract>Abstract
Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/
Graphical Abstract
Graphical Abstract</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>37897362</pmid><doi>10.1093/nar/gkad914</doi><orcidid>https://orcid.org/0000-0003-1165-1928</orcidid><orcidid>https://orcid.org/0000-0002-0508-6056</orcidid><orcidid>https://orcid.org/0000-0001-5380-2435</orcidid><orcidid>https://orcid.org/0000-0002-0446-7471</orcidid><oa>free_for_read</oa></addata></record> |
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title | MACC: a visual interactive knowledgebase of metabolite-associated cell communications |
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