{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T00:43:25Z","timestamp":1794185005377,"version":"4.8.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec><jats:title>Aim<\/jats:title><jats:p>Chinese medicine Danhong injection (DHI) is an effective pharmaceutical preparation for treating cerebral infarction. Our previous study shows that DHI can remarkably reduce the ischemic stroke-induced infarct volume in a dose-dependent manner, but the pharmacological mechanism of the DHI dose-dependent relationship is not clear. Therefore, the dose-dependent efficacy of DHI on cerebral ischemia and the underlying mechanisms were further investigated in this study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A middle cerebral artery occlusion (MCAO) model was established and the rats were randomly divided into six groups: sham, vehicle, DHI dose-1, DHI dose-2, DHI dose-3, and DHI dose-4. Forty-one metabolites in serum were selected as candidate biomarkers of efficacy phenotypes by the Agilent 1290 rapid-resolution liquid chromatography system coupled with the Agilent 6550 Q-TOF MS system. Then, the metabolic networks in each group were constructed using the Weighted Correlation Network analysis (WGCNA). Moreover, the Yang and Yin transformation of six patterns (which are defined by up- and downregulation of metabolites) and synchronous modules divided from a synchronous network were used to dynamically analyze the mechanism of the drug\u2019s effectiveness.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The neuroprotective effect of DHI has shown a dose-dependent manner, and the high-dose group (DH3 and DH4) effect is better. The entropy of the metabolic network and the Yin\/Yang index both showed a consistent dose\u2013response relationship. Seven dose-sensitive metabolites maintained constant inverse upregulation or downregulation in the four dose groups. Three synchronous modules for the DH1\u2013DH4 full-course network were identified. Glycine, N-acetyl-L-glutamate, and tetrahydrofolate as a new emerging module appeared in DH2\/DH3 and enriched in glutamine and glutamate metabolism-related pathways.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study takes the DHI metabolic network as an example to provide a new method for the discovery of multiple targets related to pharmacological effects. Our results show that the three conservative allosteric module nodes, taurine, L-tyrosine, and L-leucine, may be one of the basic mechanisms of DHI in the treatment of cerebral infarction, and the other three new emerging module nodes glyoxylate, L-glutamate, and L-valine may participate in the glutamine and glutamate metabolism pathway to improve the efficacy of DHI.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2021.645185","type":"journal-article","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T16:14:48Z","timestamp":1629476088000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Dose-Dependent Variation of Synchronous Metabolites and Modules in a Yin\/Yang Transformation Model of Appointed Ischemia Metabolic Networks"],"prefix":"10.3389","volume":"15","author":[{"given":"Yifei","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niwen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixing","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-4-2","article-title":"An automated method for finding molecular complexes in large protein interaction networks.","volume":"4","author":"Bader","year":"2003","journal-title":"BMC Bioinform."},{"key":"B2","author":"Beeckmans","year":"2009","journal-title":"Encyclopedia of Microbiology."},{"key":"B3","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s10928-017-9567-4","article-title":"Boolean network modeling in systems pharmacology.","volume":"45","author":"Bloomingdale","year":"2018","journal-title":"J. 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