- Email: [email protected]
Systems Biology Strategy in Chinese Materia Medica Research
Zhang WD et al. Chinese Herbal Medicines, 2016, 8(2): 97-98
97
Available online at SciVarse ScienceDirect
Chinese Herbal Medicines (CHM) ISSN 1674-6384 Journal homepage: www.tiprpress.com
E-mail: [email protected]
Editorial
Systems Biology Strategy in Chinese Materia Medica Research Wei-dong Zhang, Editorial Board Member of CHM Chang-Xiao Liu, Editor-in-Chief of CHM DOI: 10.1016/S1674-6384(16)60016-3 Current pharmaceutical research has realized that the conventional principles in drug discovery might have some oversights. These included that many drugs were previously believed to bind exclusively to a single target, actually interact with multiple targets, and only in such way it could take effect (Dar et al, 2012). Combinatory therapy using multiple drugs aiming at better efficacy and less toxicity has been increasingly studied and developed (Furlow, 2016). Systems biology, therefore, naturally came in sight of researchers attempting to investigate such complex interactions. Systems biology integrates methods from a broad range of biomedical research and is set to elucidate the life systems not only from a bottom-up re-assemble, but also from top-down segregation. It usually employs high throughput techniques for computational and experimental investigations that result in quantitative datasets (Selimkhanov et al, 2014). Systems biology aims at a more comprehensive understanding of the subject, which offers researchers the opportunity to construct a holistic view over the subject. Such methodology aligned well within the central demand of current Chinese materia medica (CMM) research, which focuses on the explanation of why the therapeutic effect of CMM is much greater than its single constituent. Consequently, methods and techniques from systems biology research are now actively introduced to CMM studies, contributing major breakthroughs in the area. In this issue, we present several fine examples of applying systems biology methods to CMM research. These include both experimental and computational developments in the area, where we believe such approach will answer the long-waited questions and push the long-hindered progress. In the review of Integrated Systems Biology and Chemical Biology Approach to Exploring Mechanisms of Traditional Chinese Medicines (CHM, 2016, 8(2): 99-106), Bai et al described approaches to TCM research incorporating methodologies from systems biology to chemical biology, and
set new perspectives for integrative screening of CMM’s key constituents, and characterization of novel therapeutic targets and mechanisms (Liang et al, 2014). Such integrated approach would accelerate the understanding of the core mechanisms underlying the efficacy of CMM prescriptions, promising new development of innovative and highly effective new drugs, and ultimately improve strategies for developing complex disease therapies (Xu, 2011). A more detailed practice was reported by Chai et al, Screening and Validation of Active Ingredients in Sini Decoction by Combination Method of Pharmacophore Modeling and Molecular Docking (CHM, 2016, 8(2): 126-132), in which a bioinformatical database of candidate active constituents of Sini Decoction, an efficacy-proven CMM formulae, was constructed and examined by a TNF-α pharmacophore model computationally. The resulting candidate binding molecule, higenamine, was further subjected to a cellular model in DOX-induced H9c2 and validated experimentally. Bioinformatical methods applied in searching drug targets and druggable molecules can be of particular use in CMM research, where datasets with large number of sequence, structure and interactions of potential drug targets are compiled with potential active compounds from CMM in mathematical models to simulate the drug impact at a large scale in silico. Gong et al used in silico molecular docking to study the binding mode of bentysrepinine (an anti-HBV active compound) and its derivatives with DNA polymerase has been driven by hydrophobic interaction. Two compounds, T2 and T4, exhibited the improved binding affinity to HBV DNA polymerase protein (CHM, 2016, 8(2): 139-142). They suggested that the variant docking poses of T2 and T4 might imply novel recognition of inhibition effect on T2 and T4, in comparison with bentysrepinine. The advantage of bioinformatic methods developed to explain the complex mechanism of CMM is illustrated in Network Pharmacology-based Approaches Capture Essence
98
Zhang WD et al. Chinese Herbal Medicines, 2016, 8(2): 97-98
of Chinese Herbal Medicines (CHM, 2016, 8(2): 107-116). The authors (Li et al) reviewed the proposition, development, and application of “TCM network pharmacology”, which integrates TCM theory with molecular networks and utilizes “network target” as a key concept that focuses on the systematic effects of drug targets on the biological network. Such pipeline has been proved with remarkable capability to decipher the mechanisms of the therapeutic effects of drugs, or TCM herbal formulae, and to understand their possible toxicity and unknown pharmacological activities. The connectivity map (CMAP) database is established initially to connect biology, chemistry and clinical conditions, which helps to discover the connection of disease-gene-drug. CMAP database in study of TCMs can prevent unnecessary animal experiments, reduce experimental cost, short development cycle, and improve research efficiency. Lv et al suggested that CMAP as a new weapon will have more profound application and implication in the field of TCMs research (CHM, 2016, 8(2): 117-120). Similarly, high throughput technologies developed together with systems biology research are now actively applied in CMM studies. Perhaps the biggest impact comes from the introduction of metabolomics techniques into the field. Such technique allows researchers to capture and characterize CMM’s active compounds and their metabolism in vivo, establishing their key constituents. By quantitative screening of disease markers and functioning molecules, disease and corresponding CMM are associated (Zheng et al, 2013). Investigation on Endogenous Metabolites in Pancreas of Diabetic Rats after Treatment of Genipin through 1 H-NMR-based Metabolomic Profiles (CHM, 2016, 8(2): 133-138) presented an NMR-based investigation of the effect of genipin in treatment of diabetes. From the high throughput screening and multi-variate statistics analysis of major metabolic species, researchers established the metabolic response of genipin in pancreas of rat models. The holistic paradigm shared by systems biology and CMM research certainly bridges the two community to a natural collaboration. Techniques and methods from systems biology has offered inspiring opportunities to transform CMM from an experience-based medicine into an evidence-based system. The knowledge in return, serves as established models for systems medicine, offering researchers clinically positive examples to exploit the underlying principles of systems pharmacology. For years, Chinese acknowledge the wisdom in harnessing natural medicines for their therapeutic efficacy, which is now increasingly accepted in global pharmaceutical practice (Andersson et al, 2015). Inevitably, our method has attracted a worldwide attention (Cyranoski, 2001; Normile, 2003; Stone, 2008), under which the entire community is endeavoring to develop a more accessible TCM system. TCM is very complicated, e.g. it involves multiple elements working together, it is individual-specific, its prescribing theory is incompatible with the current reductionism methods
therefore hardly elicit able. The good news is, those odds never stop us from trying (Wang et al, 2008; Zhang et al, 2010). We do not simply hold on to history books, rather in contrary, we seek every chance in applying new methods to understanding more of TCM (Buriani et al, 2012; Zhao et al, 2012; Liu, 2015), hoping that one day, we can truly transform our ancestor’s knowledge and present it to the world. References Andersson J, Lendahl U, Forssberg H, 2015. 2015 Nobel Prize in better health for millions of people thanks to drugs against parasites. Lakartidningen 112. Buriani A, Garcia-Bermejo ML, Bosisio E, Xu Q, Li H, Dong X, Simmonds M S, Carrara M, Tejedor N, Lucio-Cazana J, Hylands PJ, 2012. Omic techniques in systems biology approaches to traditional Chinese medicine research: Present and future. J Ethnopharmacol 140(3): 535-544. Cyranoski D, 2001. Hong Kong seeks secrets of Chinese medicine. Nature 412(6842): 7. Dar AC, Das TK, Shokat KM, Cagan RL, 2012. Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 486(7401): 80-84. Furlow B, 2016. Combination therapy as safe as fluticasone alone in asthma. Lancet Respir Med DOI: http://dx.doi.org/10.1016/ S2213-2600(16)30015-7. Liang X, Li H, Li S, 2014. A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol Biosyst 10(5): 1014-1022. Liu J, Lee J, Hernandez MAS, Mazitschek R, Ozcan U, 2015. Treatment of obesity with celastrol. Cell 161(5): 999-1011. Normile D, 2003. Asian medicine. The new face of traditional Chinese medicine. Science 299(5604): 188-190. Selimkhanov J, Taylor B, Yao J, Pilko A, Albeck J, Hoffmann A, Tsimring L, Wollman R, 2014. Systems biology. Accurate information transmission through dynamic biochemical signaling networks. Science 346(6215): 1370-1373. Stone R, 2008. Biochemistry. Lifting the veil on traditional Chinese medicine. Science 319(5864): 709-710. Wang L, Zhou GB, Liu P, Song JH, Liang Y, Yan XJ, Xu F, Wang BS, Mao JH, Shen ZX, Chen SJ, Chen Z, 2008. Dissection of mechanisms of Chinese medicinal formula Realgar-Indigo naturalis as an effective treatment for promyelocytic leukemia. Proc Natl Acad Sci USA 105(12): 4826-4831. Xu Z, 2011. Modernization: One step at a time. Nature 480(7378): S90-S92. Zhang XW, Yan XJ, Zhou ZR, Yang FF, Wu ZY, Sun HB, Liang WX, Song AX, Lallemand-Breitenbach V, Jeanne M, Zhang QY, Yang HY, Huang QH, Zhou GB, Tong JH, Zhang Y, Wu JH, Hu HY, de The H, Chen SJ, Chen Z, 2010. Arsenic trioxide controls the fate of the PML-RAR alpha oncoprotein by directly binding PML. Science 328(5975): 240-243. Zhao Z, Guo P, Brand E, 2012. The formation of daodi medicinal materials. J Ethnopharmacol 140(3): 476-481. Zheng P, Wang Y, Chen L, Yang D, Meng H, Zhou D, Zhong J, Lei Y, Melgiri ND, Xie P, 2013. Identification and validation of urinary metabolite biomarkers for major depressive disorder. Mol Cell Proteomics 12(1): 207-214.
97
Available online at SciVarse ScienceDirect
Chinese Herbal Medicines (CHM) ISSN 1674-6384 Journal homepage: www.tiprpress.com
E-mail: [email protected]
Editorial
Systems Biology Strategy in Chinese Materia Medica Research Wei-dong Zhang, Editorial Board Member of CHM Chang-Xiao Liu, Editor-in-Chief of CHM DOI: 10.1016/S1674-6384(16)60016-3 Current pharmaceutical research has realized that the conventional principles in drug discovery might have some oversights. These included that many drugs were previously believed to bind exclusively to a single target, actually interact with multiple targets, and only in such way it could take effect (Dar et al, 2012). Combinatory therapy using multiple drugs aiming at better efficacy and less toxicity has been increasingly studied and developed (Furlow, 2016). Systems biology, therefore, naturally came in sight of researchers attempting to investigate such complex interactions. Systems biology integrates methods from a broad range of biomedical research and is set to elucidate the life systems not only from a bottom-up re-assemble, but also from top-down segregation. It usually employs high throughput techniques for computational and experimental investigations that result in quantitative datasets (Selimkhanov et al, 2014). Systems biology aims at a more comprehensive understanding of the subject, which offers researchers the opportunity to construct a holistic view over the subject. Such methodology aligned well within the central demand of current Chinese materia medica (CMM) research, which focuses on the explanation of why the therapeutic effect of CMM is much greater than its single constituent. Consequently, methods and techniques from systems biology research are now actively introduced to CMM studies, contributing major breakthroughs in the area. In this issue, we present several fine examples of applying systems biology methods to CMM research. These include both experimental and computational developments in the area, where we believe such approach will answer the long-waited questions and push the long-hindered progress. In the review of Integrated Systems Biology and Chemical Biology Approach to Exploring Mechanisms of Traditional Chinese Medicines (CHM, 2016, 8(2): 99-106), Bai et al described approaches to TCM research incorporating methodologies from systems biology to chemical biology, and
set new perspectives for integrative screening of CMM’s key constituents, and characterization of novel therapeutic targets and mechanisms (Liang et al, 2014). Such integrated approach would accelerate the understanding of the core mechanisms underlying the efficacy of CMM prescriptions, promising new development of innovative and highly effective new drugs, and ultimately improve strategies for developing complex disease therapies (Xu, 2011). A more detailed practice was reported by Chai et al, Screening and Validation of Active Ingredients in Sini Decoction by Combination Method of Pharmacophore Modeling and Molecular Docking (CHM, 2016, 8(2): 126-132), in which a bioinformatical database of candidate active constituents of Sini Decoction, an efficacy-proven CMM formulae, was constructed and examined by a TNF-α pharmacophore model computationally. The resulting candidate binding molecule, higenamine, was further subjected to a cellular model in DOX-induced H9c2 and validated experimentally. Bioinformatical methods applied in searching drug targets and druggable molecules can be of particular use in CMM research, where datasets with large number of sequence, structure and interactions of potential drug targets are compiled with potential active compounds from CMM in mathematical models to simulate the drug impact at a large scale in silico. Gong et al used in silico molecular docking to study the binding mode of bentysrepinine (an anti-HBV active compound) and its derivatives with DNA polymerase has been driven by hydrophobic interaction. Two compounds, T2 and T4, exhibited the improved binding affinity to HBV DNA polymerase protein (CHM, 2016, 8(2): 139-142). They suggested that the variant docking poses of T2 and T4 might imply novel recognition of inhibition effect on T2 and T4, in comparison with bentysrepinine. The advantage of bioinformatic methods developed to explain the complex mechanism of CMM is illustrated in Network Pharmacology-based Approaches Capture Essence
98
Zhang WD et al. Chinese Herbal Medicines, 2016, 8(2): 97-98
of Chinese Herbal Medicines (CHM, 2016, 8(2): 107-116). The authors (Li et al) reviewed the proposition, development, and application of “TCM network pharmacology”, which integrates TCM theory with molecular networks and utilizes “network target” as a key concept that focuses on the systematic effects of drug targets on the biological network. Such pipeline has been proved with remarkable capability to decipher the mechanisms of the therapeutic effects of drugs, or TCM herbal formulae, and to understand their possible toxicity and unknown pharmacological activities. The connectivity map (CMAP) database is established initially to connect biology, chemistry and clinical conditions, which helps to discover the connection of disease-gene-drug. CMAP database in study of TCMs can prevent unnecessary animal experiments, reduce experimental cost, short development cycle, and improve research efficiency. Lv et al suggested that CMAP as a new weapon will have more profound application and implication in the field of TCMs research (CHM, 2016, 8(2): 117-120). Similarly, high throughput technologies developed together with systems biology research are now actively applied in CMM studies. Perhaps the biggest impact comes from the introduction of metabolomics techniques into the field. Such technique allows researchers to capture and characterize CMM’s active compounds and their metabolism in vivo, establishing their key constituents. By quantitative screening of disease markers and functioning molecules, disease and corresponding CMM are associated (Zheng et al, 2013). Investigation on Endogenous Metabolites in Pancreas of Diabetic Rats after Treatment of Genipin through 1 H-NMR-based Metabolomic Profiles (CHM, 2016, 8(2): 133-138) presented an NMR-based investigation of the effect of genipin in treatment of diabetes. From the high throughput screening and multi-variate statistics analysis of major metabolic species, researchers established the metabolic response of genipin in pancreas of rat models. The holistic paradigm shared by systems biology and CMM research certainly bridges the two community to a natural collaboration. Techniques and methods from systems biology has offered inspiring opportunities to transform CMM from an experience-based medicine into an evidence-based system. The knowledge in return, serves as established models for systems medicine, offering researchers clinically positive examples to exploit the underlying principles of systems pharmacology. For years, Chinese acknowledge the wisdom in harnessing natural medicines for their therapeutic efficacy, which is now increasingly accepted in global pharmaceutical practice (Andersson et al, 2015). Inevitably, our method has attracted a worldwide attention (Cyranoski, 2001; Normile, 2003; Stone, 2008), under which the entire community is endeavoring to develop a more accessible TCM system. TCM is very complicated, e.g. it involves multiple elements working together, it is individual-specific, its prescribing theory is incompatible with the current reductionism methods
therefore hardly elicit able. The good news is, those odds never stop us from trying (Wang et al, 2008; Zhang et al, 2010). We do not simply hold on to history books, rather in contrary, we seek every chance in applying new methods to understanding more of TCM (Buriani et al, 2012; Zhao et al, 2012; Liu, 2015), hoping that one day, we can truly transform our ancestor’s knowledge and present it to the world. References Andersson J, Lendahl U, Forssberg H, 2015. 2015 Nobel Prize in better health for millions of people thanks to drugs against parasites. Lakartidningen 112. Buriani A, Garcia-Bermejo ML, Bosisio E, Xu Q, Li H, Dong X, Simmonds M S, Carrara M, Tejedor N, Lucio-Cazana J, Hylands PJ, 2012. Omic techniques in systems biology approaches to traditional Chinese medicine research: Present and future. J Ethnopharmacol 140(3): 535-544. Cyranoski D, 2001. Hong Kong seeks secrets of Chinese medicine. Nature 412(6842): 7. Dar AC, Das TK, Shokat KM, Cagan RL, 2012. Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 486(7401): 80-84. Furlow B, 2016. Combination therapy as safe as fluticasone alone in asthma. Lancet Respir Med DOI: http://dx.doi.org/10.1016/ S2213-2600(16)30015-7. Liang X, Li H, Li S, 2014. A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol Biosyst 10(5): 1014-1022. Liu J, Lee J, Hernandez MAS, Mazitschek R, Ozcan U, 2015. Treatment of obesity with celastrol. Cell 161(5): 999-1011. Normile D, 2003. Asian medicine. The new face of traditional Chinese medicine. Science 299(5604): 188-190. Selimkhanov J, Taylor B, Yao J, Pilko A, Albeck J, Hoffmann A, Tsimring L, Wollman R, 2014. Systems biology. Accurate information transmission through dynamic biochemical signaling networks. Science 346(6215): 1370-1373. Stone R, 2008. Biochemistry. Lifting the veil on traditional Chinese medicine. Science 319(5864): 709-710. Wang L, Zhou GB, Liu P, Song JH, Liang Y, Yan XJ, Xu F, Wang BS, Mao JH, Shen ZX, Chen SJ, Chen Z, 2008. Dissection of mechanisms of Chinese medicinal formula Realgar-Indigo naturalis as an effective treatment for promyelocytic leukemia. Proc Natl Acad Sci USA 105(12): 4826-4831. Xu Z, 2011. Modernization: One step at a time. Nature 480(7378): S90-S92. Zhang XW, Yan XJ, Zhou ZR, Yang FF, Wu ZY, Sun HB, Liang WX, Song AX, Lallemand-Breitenbach V, Jeanne M, Zhang QY, Yang HY, Huang QH, Zhou GB, Tong JH, Zhang Y, Wu JH, Hu HY, de The H, Chen SJ, Chen Z, 2010. Arsenic trioxide controls the fate of the PML-RAR alpha oncoprotein by directly binding PML. Science 328(5975): 240-243. Zhao Z, Guo P, Brand E, 2012. The formation of daodi medicinal materials. J Ethnopharmacol 140(3): 476-481. Zheng P, Wang Y, Chen L, Yang D, Meng H, Zhou D, Zhong J, Lei Y, Melgiri ND, Xie P, 2013. Identification and validation of urinary metabolite biomarkers for major depressive disorder. Mol Cell Proteomics 12(1): 207-214.