Iridoids as systematic markers in dicotyledon

Character evolution and missing (morphological) data across Asteridae

PREMISE OF THE STUDY

Our current understanding of flowering plant phylogeny provides an excellent framework for exploring various aspects of character evolution through comparative analyses. However, attempts to synthesize this phylogenetic framework with extensive morphological data sets have been surprisingly rare. Here, we explore character evolution in Asteridae (asterids), a major angiosperm clade, using an extensive morphological data set and a well-resolved phylogeny.

METHODS

We scored 15 phenotypic characters (spanning chemistry, vegetative anatomy, and floral, fruit, and seed features) across 248 species for ancestral state reconstruction using a phylogenetic framework based on 73 plastid genes and the same 248 species.

KEY RESULTS

Iridoid production, unitegmic ovules, and cellular endosperm were all reconstructed as synapomorphic for Asteridae. Sympetaly, long associated with asterids, shows complex patterns of evolution, suggesting it arose several times independently within the clade. Stamens equal in number to the petals is likely a synapomorphy for Gentianidae, a major asterid subclade. Members of Lamianae, a major gentianid subclade, are potentially diagnosed by adnate stamens, unilacunar nodes, and simple perforation plates.

CONCLUSIONS

The analyses presented here provide a greatly improved understanding of character evolution across Asteridae, highlighting multiple characters potentially synapomorphic for major clades. However, several important parts of the asterid tree are poorly known for several key phenotypic features (e.g., degree of petal fusion, integument number, nucellus type, endosperm type, iridoid production). Further morphological, anatomical, developmental, and chemical investigations of these poorly known asterids are critical for a more detailed understanding of early asterid evolution.

Neural Networks in Chemosystematic Studies of Asteraceae: A Classification Based on a Dichotomic Approach

This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R>0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied.

Ethnopharmacology versus chemosystematics in the search for biologically active principles in plants

The chemicals to which Brazilian angiosperms owe their use in the preparation of arrow poisons, hallucinogens, fish poisons, drugs, stimulants, spices, perfumes and pigments are correlated with the systematic position of the species in which they occur. Most compounds are produced either by the primitive Magnoliidae (sensu Cronquist) or by the advanced Asteridae. The Rosidae-Dilleniidae, precisely the group of subclasses of widest distribution over the country, have heretofore yielded relatively few useful compounds. This fact can be rationalized by the presence in their species of massive quantities of gallo- and ellagitannins, general defences which make the presence of specific alleochemics superfluous. The natural occurrence of specific biologically active compounds is thus ecologically and systematically conditioned and it should be possible to build a system capable of predicting the existence and the nature of useful chemicals in plant taxa.