Did a plant-herbivore arms race drive chemical diversity in Euphorbia?

Abstract

The genus Euphorbia is among the most diverse and species-rich plant genera on Earth, exhibiting a near-cosmopolitan distribution and extraordinary chemical diversity, especially across highly toxic macro-and polycyclic diterpenoids. However, very little is known about drivers and evolutionary origins of chemical diversity within Euphorbia. Here, we investigate 43 Euphorbia species to understand how geographic separation over evolutionary time has impacted chemical differentiation. We show that the structurally highly diverse Euphorbia diterpenoids are significantly reduced in species native to the Americas, compared to the Eurasian and African continents, where the genus originated. The localization of these compounds to young stems and roots suggest ecological relevance in herbivory defense and immunomodulatory defense mechanisms match diterpenoid levels, indicating chemoevolutionary adaptation to reduced herbivory pressure.One Sentence SummaryGlobal chemo-evolutionary adaptation of Euphorbia affected immunomodulatory defense mechanisms.

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

“…A key interest is therefore to find species within the genus producing higher quantities of ingenol mebutate or other close diterpenoid analogs exhibiting biological activities with pharmaceutical interest. We have previously assessed chemical diversity within a representative subset of species of the plant genus Euphorbia [Ernst et al, 2018]. A major challenge is the rapid identification of known and unknown Euphorbia diterpenoid structures.…”

“…Publicly available mass spectrometry fragmentation data sets from four studies were used for this study. Details on how samples and data were collected can be found in the original studies [24,25,44,46]. Here, we list links to the different analyses that were done on each of the studies.…”

“…Furthermore, it would be practically advantageous to combine all these results in one place. We have previously described the integration of Mass2Motifs and chemical classifications with molecular networks to assess the chemical diversity within a subset of species of the plant genus Euphorbia [24] and the plant family Rhamnaceae [25]. However, in those studies, integration was achieved using custom in-house scripts in R, hampering adoption by the community.…”

Combining Evolutionary Inference and Metabolomics to Identify Plants With Medicinal Potential

“…However, the focus of these studies was on single taxa without considering their evolutionary history. Multi-species studies do exist, as in the recent study of acetylcholinesterase inhibitors produced by a range of Iranian monocot and eudicot flowering plant taxa, where new compounds as well as taxa responsible for expressing the new compounds were identified (Abbas-Mohammadi et al, 2018), but again the study taxa here were restricted only to known medicinal species, with little consideration of phylogenetic relatedness except in few recent research concerning Euphorbiaceae (Ernst et al, 2018). However, with the existence of phylogenetic and metabolomic information across multiple species, this approach could be further scaled up to comprising taxa with ethnobotanic medicinal use as well as their sister taxa for which no known records of ethnobotanical medicinal use exist, to identify target taxa for medicinal potential (see Figure 3).…”

A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters

“…Finally, in the Arabidopsis metabolomics study applied here, we did not generate dedicated metabolite fragmentation data, such as MS/MS and MS n spectra [29], making it yet difficult to predict the nature of the unknown metabolites linked to putative BGCs of unknown function. New technologies that generate and exploit metabolite fragmentation data based on molecular networking and substructure identification [30–32] will make it easier to obtain structural information for such unknowns, and thus facilitate assessing whether the combination of molecular and genetic (enzymatic) features observed in an mQTL-BGC pair shows high potential or not.…”