ETHNOPHARMACOLOGY: Xiaoyaosan, a famous Chinese prescription, composed of Poria (Poria cocos (Schw.) Wolf), Radix Paeoniae Alba (Paeonia lactiflora Pall.), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.), Radix Bupleuri (Bupleurum chinense DC.), Radix Angelicae Sinensis (Angelica sinensis (Oliv.) Diels), Rhizoma Atractylodis Macrocephalae (Atractylodes macrocephala Koidz.), Herba Menthae (Mentha haplocalyx Briq.), and Rhizoma Zingiberis Recens (Zingiber officinale Rosc.), has been widely used in the clinic for treating mental disorders. Behavior and biochemical analyses indicate xiaoyaosan has obvious anti-depression activity. However, there is no report on the effects of xiaoyaosan using a metabolomics approach. AIM OF THE STUDY: A urinary metabolomics method was applied to evaluate the efficacy of xiaoyaosan on rat model of chronic unpredictable mild stress. MATERIAL AND METHODS: Rats were divided into 6 groups and drugs were administered during the 21-day model building period. Urine was measured using GC-MS, processed with XCMS and Microsoft Excel and analyzed by SIMCA-P and SPASS software. Variable importance in projection statistics and loading plot were used to find biomarker ions. RESULTS: Clear separation between model and each drug group was achieved. High dose group of xiaoyaosan was much closer to control group than middle dose group and amitriptyline group. The time-dependent recovery tendency in high dose group was obtained. CONCLUSIONS: In term of anti-depression effect, high dose xiaoyaosan was the most effective and amitriptyline equaled middle dose xiaoyaosan as shown by metabolomics strategy and behavior tests. Some common and characteristic metabolites on the anti-depression of xiaoyaosan and amitriptyline were obtained. The work showed metabolomics is a valuable tool in studying the efficacy and potential biomarkers of therapeutic effect of complex prescriptions. Copyright (c) 2010 Elsevier Ireland Ltd. All rights reserved.
ETHNOPHARMACOLOGY: Xiaoyaosan, a famous Chinese prescription, composed of Poria (Poria cocos (Schw.) Wolf), Radix Paeoniae Alba (Paeonia lactiflora Pall.), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.), Radix Bupleuri (Bupleurum chinense DC.), Radix Angelicae Sinensis (Angelica sinensis (Oliv.) Diels), Rhizoma Atractylodis Macrocephalae (Atractylodes macrocephala Koidz.), Herba Menthae (Mentha haplocalyx Briq.), and Rhizoma Zingiberis Recens (Zingiber officinale Rosc.), has been widely used in the clinic for treating mental disorders. Behavior and biochemical analyses indicate xiaoyaosan has obvious anti-depression activity. However, there is no report on the effects of xiaoyaosan using a metabolomics approach. AIM OF THE STUDY: A urinary metabolomics method was applied to evaluate the efficacy of xiaoyaosan on rat model of chronic unpredictable mild stress. MATERIAL AND METHODS:Rats were divided into 6 groups and drugs were administered during the 21-day model building period. Urine was measured using GC-MS, processed with XCMS and Microsoft Excel and analyzed by SIMCA-P and SPASS software. Variable importance in projection statistics and loading plot were used to find biomarker ions. RESULTS: Clear separation between model and each drug group was achieved. High dose group of xiaoyaosan was much closer to control group than middle dose group and amitriptyline group. The time-dependent recovery tendency in high dose group was obtained. CONCLUSIONS: In term of anti-depression effect, high dose xiaoyaosan was the most effective and amitriptyline equaled middle dose xiaoyaosan as shown by metabolomics strategy and behavior tests. Some common and characteristic metabolites on the anti-depression of xiaoyaosan and amitriptyline were obtained. The work showed metabolomics is a valuable tool in studying the efficacy and potential biomarkers of therapeutic effect of complex prescriptions. Copyright (c) 2010 Elsevier Ireland Ltd. All rights reserved.
Authors: Sean O'Callaghan; David P De Souza; Andrew Isaac; Qiao Wang; Luke Hodkinson; Moshe Olshansky; Tim Erwin; Bill Appelbe; Dedreia L Tull; Ute Roessner; Antony Bacic; Malcolm J McConville; Vladimir A Likić Journal: BMC Bioinformatics Date: 2012-05-30 Impact factor: 3.169
Authors: C Webhofer; P Gormanns; V Tolstikov; W Zieglgänsberger; I Sillaber; F Holsboer; C W Turck Journal: Transl Psychiatry Date: 2011-12-13 Impact factor: 6.222