Abstract
Chemotaxonomy is a classification method based on the comparison of similarities of natural compounds among the organisms to be classified. However, it presents some difficulties because the chemical compounds present large variability. Currently, different classification methods are available in chemotaxonomy involving classical and modern taxonomy. Asteraceae is one of the largest families of angiosperms, and its sesquiterpene lactones are considered important taxonomic markers. They have been studied as promising new drug sources as a consequence of their enormous pharmacological potential. With regard to modern methods to study taxonomy, in the last two decades, linear approaches such as principal component analysis and partial least squares as well as other machine learning methods like artificial neural networks, k-nearest neighbors, and self-organizing maps have been used as pattern recognition methods to study the distribution of sesquiterpene lactones among the tribes and genera of Asteraceae. For this purpose, thousands of chemical structures and molecular descriptors that encode different physicochemical features of compounds have been used. Such studies are empowered by computational methods and databases of secondary metabolites. In this sense, systems that are able to manage large databases of sesquiterpene lactones are crucial for more robust chemotaxonomic studies of Asteraceae. The web tool AsterDB (www.asterbiochem.org/asterdb) is an interesting database with a friendly interface that contains thousands of chemical structures of secondary metabolites from this family and comprises more than 1000 chemical structures of sesquiterpene lactones. The great evolution in the study of natural products based on computational methods shows that modern and integrated platforms will be available soon for several purposes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acevedo CH, Scotti L, Alves MF et al (2017) Computer-aided drug design using sesquiterpene lactones as sources of new structures with potential activity against infectious neglected diseases. Molecules 22. https://doi.org/10.3390/molecules22010079
Adamczyk JJ, Kurzac M, Park YS et al (2013) Application of a Kohonen's self-organizing map for evaluation of long-term changes in forest vegetation. J Veg Sci 24:405–414. https://doi.org/10.1111/j.1654-1103.2012.01468.x
Afendi FM, Okada T, Yamazaki M et al (2012) KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research. Plant Cell Physiol 53:e1. https://doi.org/10.1093/pcp/pcr165
Aljancic I, Vajs V, Menkovic N et al (1999) Flavones and sesquiterpene lactones from Achillea atrata subsp multifida: antimicrobial activity. J Nat Prod 62:909–911. https://doi.org/10.1021/np980536m
Alston RE, Turner BL, Mabry TJ (1963) Perspectives in chemotaxonomy. Science 142:545–552. https://doi.org/10.1126/science.142.3592.545
Bedoya D, Novotny V, Manolakos ES (2009) Instream and offstream environmental conditions and stream biotic integrity importance of scale and site similarities for learning and prediction. Ecol Model 220:2393–2406. https://doi.org/10.1016/j.ecolmodel.2009.06.017
Bentham G (1873) Notes on the classification, history, and geographical distribution of the Compositae. Bot J Linn Soc 13:335–557
Bremer K (1987) Tribal interrelationships of the Asteraceae. Cladistics 3:210–253
Bremer K (1996) Major clades and grades of the Asteraceae. In: Hind DN, Beebtje HJ (eds) Compositae: systematics. Proceedings of the international Compositae conference, Kew, 1994. Hind DJN (Editor-in-Chief), vol 1, Royal Botanic Gardens, Kew, pp 1–7
Calabria LM, Emerenciano VP, Ferreira MJP et al (2007) A phylogenetic analysis of tribes of the Asteraceae based on phytochemical data. Nat Prod Comm 2:277–285
Carlquist S (1976) Tribal interrelationships and phylogeny of the Asteraceae. Aliso 8:465–492
Cassini H (1816) Tableau exprimant les affinities des tribus naturelles de famile des Synanthérées. In: Dictionnaire des Sciences Naaturelles, vol 3. G Cuvier, 2nd edn. Le Normant, Paris
Chadwick M, Trewin H, Gawthrop F et al (2013) Sesquiterpene lactones: benefits to plants and people. Int J Mol Sci 14:12780–12805. https://doi.org/10.3390/ijms140612780
Chen CYC (2011) TCM database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One 6:e15939. https://doi.org/10.1371/journal.pone.0015939
Correia MV, Scotti MT, Ferreira MJP et al (2008) Self-organizing maps as a new tool for classification of plants at lower hierarchical levels. Nat Prod Comm 3:1723–1730
Correia MV, Fokou HH, Marcelo J et al (2012) Self-organizing maps as a good tool for classification of subfamily Astereoideae. J Med Plant Res 6:1207–1218
Dabb S, Blunt J, Munro M (2014) MarinLit: database and essential tools for the marine natural products community. Abstracts of Papers of the American Chemical Society. 248: 25-CINF
DaCosta FB, Terfloth L, Gasteiger J (2005) Sesquiterpene lactone-based classification of three Asteraceae tribes: a study based on self-organizing neural networks applied to chemo systematics. Phytochemistry 66:345–353. https://doi.org/10.1016/j.phytochem.2004.12.006
Emerenciano VP, Rodrigues GV, Alvarenga SAV et al (1998) Um novo método para agrupar parâmetros quimiotaxonômicos Quím Nova. 21:125–129
Emerenciano VP, Cabrol-Bass D, Ferreira MJP et al (2006) Chemical evolution in the Asteraceae. The oxidation-reduction mechanism and production of secondary metabolites. Nat Prod Comm 1:495–507
Emerenciano VR, Barbosa KO, Scotti MT et al (2007) Self-organizing maps in chemotaxonomic studies of asteraceae: a classification of tribes using flavonoid data. J Braz Chem Soc 18:891–899
Ferreira MJP, Brant AJC, Rufino AR et al (2004) Prediction of occurrences of diverse chemical classes in the Asteraceae through artificial neural networks. Phytochem Anal 15:389–396. https://doi.org/10.1002/pca.799
Ferreira MJP, Brant AJC, Alvarenga SAV et al (2005) Neural networks in chemosystematic studies of Asteraceae: a classification based on a dichotomic approach. Chem Biodivers 2:633–644. https://doi.org/10.1002/cbdv.200590040
Funk VA, Bayer RJ, Keeley S et al (2005) Everywhere but Antarctica: using a supertree to understand the diversity and distribution of the Compositae. Biol Skr 55:343–374
Gottlieb O (1989) The role of oxygen in phytochemical evolution towards diversity. Phytochemistry 28:2545–2558. https://doi.org/10.1016/S0031-9422(00)98039-7
Gottlieb OR, Borin MRMB (2012) Químico-biologia quantitativa: um novo paradigma? Quim Nova 35:2105–2114
Gottlieb OR, Borin MRMB, Pagotto CLAC et al (1998) Biodiversidade: o enfoque interdisciplinar brasileiro. Cien Saude Colet 3:97–102
Graham JG, Farnsworth NR (2010) The NAPRALERT database as an aid for discovery of novel bioactive compounds. In: Mander L, Liu H-W (eds) Comprehensive natural products II: chemistry and biology, vol 3. Elsevier, Amsterdam, pp 81–94
Hatherley R, Brown DK, Musyoka TM et al (2015) SANCDB: a South African natural compound database. J Cheminform 7:29. https://doi.org/10.1186/s13321-015-0080-8
Heywood VH (2009) The recent history of Compositae systematics: from daisies to deep achenes, sister groups and metatrees. In: Funk V, Susanna A, Stuessy TF, Bayer RJ (eds) Systematics, evolution and biogeography of Compositae. International Association for Plant Taxonomy, Institute of Botany, University of Vienna, Vienna, pp 39–44
Hristozov D, Da Costa FB, Gasteiger J (2007) Sesquiterpene lactones-based classification of the family Asteraceae using neural networks and k-nearest neighbors. J Chem Inf Model 47:9–19. https://doi.org/10.1021/ci060046x
Kim S-K, Nam S, Jang H et al (2015) TM-MC: a database of medicinal materials and chemical compounds in northeast Asian traditional medicine. BMC Complement Altern Med 15:218. https://doi.org/10.1186/s12906-015-0758-5
Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59–69. https://doi.org/10.1007/bf00337288
Kohonen T (2001) Self-organizing maps (springer series in information sciences), 3rd edn. Springer, Heidelberg
Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37:52–65. https://doi.org/10.1016/j.neunet.2012.09.018
Loub WD, Farnsworth NR, Soejarto DD et al (1985) NAPRALERT - computer handling of natural product research data. J Chem Inf Comput Sci 25:99–103. https://doi.org/10.1021/ci00046a009
Manallack DT, Livingstone DJ (1999) Neural networks in drug discovery: have they lived up to their promise? Eur J Med Chem 34:195–208. https://doi.org/10.1016/s0223-5234(99)80052-x
Mannheimer CA (1999) An overview of chemotaxonomy and its role in creating a phylogenetic classification system. Agricola 10:87–90
Merfort I (2011) Perspectives on sesquiterpene lactones in inflammation and cancer. Curr Drug Targets 12:1560–1573
Park YS, Tison J, Lek S et al (2006) Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France. Ecol Inform 1:247–257. https://doi.org/10.1016/j.ecoinf.2006.03.005
Picman AK (1986) Biological activities of sesquiterpene lactones. Biochem Syst Ecol 14:255–281
Schlee D (1975) Syst Zool 24:263–268. https://doi.org/10.2307/2412767
Schmidt TJ (1999) Toxic activities of sesquiterpene lactones: structural and biochemical aspects. Curr Org Chem 3:577–608
Schmidt TJ, Nour AMM, Khalid SA et al (2009) Quantitative structure - antiprotozoal activity relationships of sesquiterpene lactones. Molecules 14:2062–2076. https://doi.org/10.3390/molecules14062062
Schmidt TJ, Kaiser M, Brun R (2011) Complete structural assignment of serratol, a cembrane-type diterpene from Boswellia serrata, and evaluation of its antiprotozoal activity. Planta Med 77:849–850. https://doi.org/10.1055/s-0030-1250612
Schmidt TJ et al (2012a) The potential of secondary metabolites from plants as drugs or leads against protozoan neglected diseases - part I. Curr Med Chem 19:2128–2175
Schmidt TJ et al (2012b) The potential of secondary metabolites from plants as drugs or leads against protozoan neglected diseases - part II. Curr Med Chem 19:2176–2228
Scotti MT, Emerenciano V, Ferreira MJP et al (2012) Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family. Molecules 17:4684–4702. https://doi.org/10.3390/molecules17044684
Seaman FC (1982) Sesquiterpene lactones as taxonomic characters in the Asteraceae. Bot Rev 48:121–595. https://doi.org/10.1007/bf02919190
Siedle B, García Piñeres AJ, Murillo R et al (2004) Quantitative structure - activity relationship of sesquiterpene lactones as inhibitors of the transcription factor NF-kappa B. J Med Chem 47:6042–6054. https://doi.org/10.1021/jm049937r
Singh R (2016) Chemotaxonomy: a tool for plant classification. J Med Plant Stud 4(2):90–93
Tung CWCW, Lin YCYC, Chang HSHS et al (2014) TIPdb-3D: the three-dimensional structure database of phytochemicals from Taiwan indigenous plants. Database 2014:1–5. https://doi.org/10.1093/database/bau055
Valli M, dos Santos RN, Figueira LD et al (2013) Development of a natural products database from the biodiversity of Brazil. J Nat Prod 76:439–444
Williams WT (1975) Numerical taxonomy. The principles and practice of numerical classification. Peter H. A. Sneath, Robert R. Sokal. Q Rev Biol 50:525–526. https://doi.org/10.1086/408956
Wink M (2003) Evolution of secondary metabolites from an ecological and molecular phylogenetic perspective. Phytochemistry 64:3–19. https://doi.org/10.1016/s0031-9422(03)00300-5
Wink M (2008) Plant secondary metabolism: diversity, function and its evolution. Nat Prod Comm 3:1205–1216
Wink M, Botschen F, Gosmann C et al (2010) Chemotaxonomy seen from a phylogenetic perspective and evolution of secondary metabolism. In: Wink M (ed) Annual plant reviews vol 40 biochemistry of plant secondary metabolism, 2nd edn. Wiley-Blackwell, Oxford. https://doi.org/10.1002/9781444320503.ch7
Xue R, Fang Z, Zhang M et al (2013) TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res 41:D1089–D1095. https://doi.org/10.1093/nar/gks1100
Zhang JT, Li M (2011, 26-28 July 2011) Self-organizing feature map clustering based on fuzzy equivalence relation and its application in ecological analysis. In: 2011 eighth international conference on fuzzy systems and knowledge discovery (FSKD), Shanghai
Zhang J, Yang H (2008) Application of self-organizing neural networks to classification of plant communities in Pangquangou nature reserve, North China. Front Biol 3:512–517. https://doi.org/10.1007/s11515-008-0061-7
Zupan J, Gasteiger J (1999) Neural networks in chemistry and drug design. Wiley, New York
Züst T, Heichinger C, Grossniklaus U (2012) Natural enemies drive geographic variation in plant defenses. Science 338:116–119. https://doi.org/10.1126/science.1226397
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Alves, M.F., Scotti, L., Da Costa, F.B., Scotti, M.T. (2018). Chemotaxonomic Study of Sesquiterpene Lactones of Asteraceae: Classical and Modern Methods. In: Sülsen, V., Martino, V. (eds) Sesquiterpene Lactones. Springer, Cham. https://doi.org/10.1007/978-3-319-78274-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-78274-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78273-7
Online ISBN: 978-3-319-78274-4
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)