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Chapter 5
Use of Omics Methods To Determine the Mode of Action of Natural Phytotoxins Stephen O. Duke,1,* Zhiqiang Pan,1 Joanna Bajsa-Hirschel,1 Adela M. Sánchez-Moreiras,2 and Justin N. Vaughn3 1United
States Department of Agriculture, University of Mississippi, Oxford, Mississippi 38677, United States 2Dept. Plant Biology & Soil Sci., Univ. of Vigo, 36310, Vigo, Spain 3United States Department of Agriculture, Athens, Georgia, United States *E-mail:
[email protected] Technology has greatly increased the power of omics methods to profile transcription, protein, and metabolite responses to phytotoxins. These methods hold promise as a tool for providing clues to the modes of action of such compounds. However, to date, only two putative modes of action have been found with these methods; one with proteomics and the other with metabolomics. As with traditional physiological methods (physionomics) for mode of action discovery, differentiating between primary and secondary and tertiary effects is problematic. This problem can partially be overcome by careful experimental design. More powerful tools for metabolic pathway analysis are making transcriptome data easier to interpret with regard to the potential phytotoxin target site. Stable isotope measurement of metabolite pool turnover (fluxomics) has the potential to improve the insight into metabolomics for mode of action discovery.
Introduction Like all other living things, largely based on their genomes, plants have a complex array of responses to anything that affects them. Many of these responses begin with transcriptional changes that cascade to the proteome and metabolome, © 2018 American Chemical Society
resulting in an array of biochemical and physiological changes. Advances in capabilities in sequencing mRNA and proteins and identifying components of metabolite extracts, as well as determining the quantity of each component, have resulted in the rise of “omics” technologies to analyze responses of plants to anything that affects them at the transcriptome, proteome, and metabolome levels in great detail. The physiological profile of a plant that results from changes at these more fundamental levels has been termed physionomics. The “omics” suffix has been use to describe several subsets of each of the three basic omics levels (e.g., glycomics and lipidomics under metabolomics), but we only discuss the main omics categories in this short chapter. The topic of using omics to find herbicide and natural phytotoxin modes of action has been reviewed before (1, 2). This short review updates our earlier review. A herbicide with a new mode of action has not been introduced for over thirty years (3). Rapidly evolving herbicide resistance has made the value of a new herbicide mode of action greater than at any time since the advent of the age of herbicides in agriculture (4). Natural products are good sources of phytotoxic compounds with both proven and putative novel modes of action that could be used for herbicides (5). The modes of action of many of these promising compounds is unknown, largely because the determination of the molecular target of a phytotoxin is not a trivial pursuit. Omics technologies offer powerful tools in mode of action discovery research. Any one of these “omics” technologies can be used to develop profiles of plant responses to herbicides or phytotoxins with known specific modes of action. The omic response of a plant to a compound with an unknown mode of action can be compared to a library of responses to compounds with known modes of action. At a minimum, this approach should be able to identify the modes of action that are similar to those in the library. If the compound has a novel mode of action, the effect on the omics profile should not be in the library, but it might provide a hint as to the molecular target. Several herbicide discovery companies have tried this approach to new target site discovery, but only BASF has published papers that provide their methods in detail and a description of their limited success (2, 6). Like any tool, omics technologies have their weaknesses. The omics response to a toxin is dependent on the dose of the toxin and the time after exposure. Although all commercial herbicides appear to have one primary molecular target site, at high doses, a phytotoxin might directly affect secondary targets, and thereby complicate the results. At low doses, the plant might compensate too rapidly to see an effect or even stimulate growth, as low doses of phytotoxins commonly cause hormesis – the stimulatory effect of a non-toxic dose of a toxicant (7). So, intermediate doses such as concentrations needed to inhibit growth by 50 to 80% (ED50 or ED80) are preferred. After the target site is inhibited, there can be many secondary and/or tertiary effects of a herbicide. In fact, at higher doses, most everything is eventually affected. Stress and detoxification responses can be dramatic with herbicides, athough these effects are not generally mode of action specific. Thus, early time points after exposure are preferred to avoid a cascade of effects that can mask effects more closely tied to the primary target. In whole plants or plant organs (e.g., the leaf), there can be tissue to tissue and cell type to cell type variation in response to a herbicide, even if each cell 34
gets the same dose. To make things more complicated, the distribution of a herbicide in a plant can vary dramatically between organs, tissues and cell type, depending on many factors (8). Compounds with different modes of action can have most of their effects in different cell types. For example, compounds that inhibit photosynthesis or production of carotenoids or chlorophyll should act primarily on green, photosynthesizing cells, whereas mitotic inhibitors act mostly in meristematic cells. Furthermore, the mRNA, proteins, and metabolites can vary dramatically between different cells types even before being affected by a phytotoxin. Although, there are methods for sampling specific cell types (especially for transcriptomics), if the mode of action is unknown, this approach may not be useful (9). Thus, in most studies, the mRNA, protein, or metabolites measured are a mixture from different tissues and cell types. Less affected tissues and cell types can mask what might be dramatic effects in more affected tissues and cell types. Another consideration is whether a compound might have more than one molecular target site. This may be unlikely, as the twenty or so commercial herbicide mechanisms of action involve only one target at the doses used for weed management (3). However, this may not be the case with some natural phytotoxins. For example, the allelochemical sorgoleone targets several molecular targets (10). Evolution of target site resistance to a compound with several molecular targets is much less likely than to a compound with a single target, a fact that has plagued agriculture in recent decades. Thus, results from these omics technologies must be interpreted with several qualifications. Omics results alone cannot prove a mode of action, but can only provide clues. Clues from omics studies must be followed up with in vitro assays of effects on putative molecular targets and/or genetic alteration of the putative target for unequivocal proof of the target site. Nevertheless, the unprecedented power of these methods to provide detailed information on effects at several levels promises to provide considerable new information about mode of action and effects of herbicides and phytotoxins on the biochemistry and physiology of plants.
Transcriptomics After genomics provided complete genomes of Arabidopsis thaliana with reasonably good annotation of the genes, especially those of primary metabolism, transcriptomic responses of plants to herbicides and phytotoxins via DNA microarray chips became possible. Because Arabidopsis has genes for all primary metabolism enzymes, and most all herbicide targets are enzymes of primary metabolism, transcriptomics of Arabidopsis seemed like a good approach for probing the mode of action of herbicides. Our earlier review covered what was had been achieved with herbicides and transcriptomics up until 2012 (1). A review earlier than this mostly hypothesized how this technology could be used to study herbicide modes of action (11). To summarize, despite the promise of this technology, no new modes of action have been discovered with this method. Tresch summarized the results of this methodology up to about 2012: 35
“So far, transcriptomics techniques were used to describe the similarities of new compounds with well characterized ones, but a substantial contribution to the description of a new target site has rarely been reported” (12). From the published literature, the term ‘rarely’ may be generous, but there may be successes in the herbicide discovery and development industry that are unreported. However, these early studies provided some good information about the limitations of transcriptome studies of herbicide action. Very soon after exposure of a plant to a phytotoxins with an unknown mode of action, genes involved in stress responses and detoxification are strongly upregulated (13). Thus, there is no “smoking gun” in the large number of upregulated or downregulated genes. For example, our earlier work with yeast transcriptomics, as affected by inhibitors of enzymes of the ergosterol pathway, showed that even when a molecular target site is known, the gene for the target enzyme may be less affected than genes of other enzymes in the same metabolic pathway (14). Likewise, Zhu et al. (15) did not find any indication that 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) is the target of glyphosate from glyphosate-susceptible soybean transcriptome responses to glyphosate. In the case of the natural phytotoxin cantharidin, transcription of as much as 10% of the approximately 24,000 genes of Arabidopsis is significantly affected within 24 h of treatment by a dose that inhibits growth by 30% (16). This compound and its commercial herbicide analog, endothall, inhibit all of the several serine/threonine protein phosphatases (PPPs) of the plant, thereby affecting many different processes simultaneously (17). The transcriptome response fits the mode of action, but, as with the yeast example, it is doubtful that the transcriptome results would point to PPPs as the target site if the target was not known. Expression of the genes encoding both catalytic and regulatory A subunits of PPPs was not effected by cantharidin. Expression of the B regulatory subunits was significantly, but not dramatically, upregulated at 2 h, but not at 10 and 24 h after treatment. A drawback to DNA chip transcriptomics technology is inaccurate measurements of low abundance transcripts. RNA-Seq has become the preferred method for transcriptome analysis due to its much improved efficiency for low abundance transcripts (18). RNA-Seq has a 9000-fold range of sensitivity, versus a few hundred-fold ranges for microarray methods. RNA-Seq has not been used so far for phytotoxin mode of action studies, but it has been used extensively in studies of mechanisms of herbicide resistance (19). However, all of this resistance work has been done with weed species for which we do not have the full genome and for which gene annotation is less developed than that of Arabidopsis. It has been mostly used to determine nontarget site-based resistance changes in expression of multiple genes. RNA-Seq can also reveal mutations that might be associated with the evolved resistance, whether target site or non-target site based. There are serious problems in much of this work related to the lack of genetic uniformity within the populations studied (19). This is not an issue in the use of RNA-Seq for mode of action studies. Despite the superiority of RNA-Seq over DNA chip technologies for transcriptomics research, there have been essentially no mode of action studies published on this topic using RNA-Seq methods. This is unfortunate, as the massive amount of detailed data generated with RNA-Seq allows for detection of gene transcription effects, even 36
for genes with a low transcription rate. There is evidence that the best herbicide target sites are those that are present in low abundance, thus requiring less herbicide to poison an effective percentage of the target (3). The only publications using RNA-Seq to probe herbicides and plant responses other than resistance studies have been on stress responses (20, 21). The improved resolution of a wide range of gene expression with RNA-seq allows better analyses of effects of a toxin on genes of a complete biochemical pathway. Most herbicides have modes of action associated with enzymes or energy transduction proteins of primary metabolism. One of the analytical tools for analyzing effects on genes associated with primary metabolism is the R software (keggseq) associated with the Kyoto Encyclopedia of Genes and Genomes (KEGG). This software will determine which pathways and processes are significantly affected and will provide a detailed image of an entire pathway, showing effects on all of the genes. For example, the effects of an IC50 concentration of t-chalcone, a demonstrated phytotoxin, on the genes of the phenylpropanoid pathway of Arabidopsis thaliana are shown in Figure 1 (22). KEGG analysis is extremely helpful in identifying what pathways might be first and/or most affected by a phytotoxin, thereby indicating a potential mode of action. Other pathway analysis software for transcriptomics data is available (e.g., GAGE and Pathview). In summary, although there have been huge improvements in the technology of transcriptomics and in the software for analysis of the massive amounts of data produced, this approach to determination of new modes of action of phytotoxins has not yet been successful. Other omics approaches that are at a level more closely related to the target site might be more appropriate.
Proteomics Proteomics is less exact than transcriptomics, partly because low abundance proteins are often missed with current methods. There are significantly fewer proteins than genes to deal with, simplifying analysis. However, post-translational modification of proteins can complicate interpretation of results. Our previous review covered the small amount of use of this technology for herbicide and phytotoxin mode of action research through 2012 (1). Little has been done since then, with herbicide related proteomics studies being almost entirely focused on herbicide resistance (23). An exception is the publication of Bajsa et al. (24) of the proteomics study that came from the same experiment that we discuss above regarding the effects of cantharidin on the transcriptome (16). The samples for protein analysis were from the same experiment, but there was little correspondence between affected gene transcripts and proteins, other than with glutathione-S-transferases and enzymes involved in xenobiotic detoxification. The 2D gel analysis of the proteins could not resolve low abundance proteins, such as PPP subunits. By immunochemistry, the catalytic subunits of PP2Ac, one of the cantharidin target sites, were upregulated at 2 and 10 h after treatment, even though the transcription of these genes was not affected (see above). These effects were too subtle to have lead to identification of the target site of this potent 37
phytotoxin. Lack of correlations between transcriptome and proteome effect is not unusual and can be due to multiple factors (1, 25, 26). There has been one paper in which proteomics has been used to identify a putative target of a natural phytotoxin. Zhao et al. (27) found α-terthienyl to affect sixteen proteins associated with energy transduction in A. thaliana. In particular, transketolase protein was signficantly reduced, even though there was higher mRNA expression for the transketolase gene. A mutant with an altered transketolase was less affected by the phytotoxin, and the in vitro activity of the enzyme from the mutant was less affected by the phytotoxin than that from the wild type (Figure 2). Nevertheless, the very weak effect of the toxin on the in vitro enzyme activity is hard to reconcile with a primary target site.
Figure 1. Effects of 21 μM t-chalcone on glutathione metabolism transcripts of Arabidopsis seedling roots 3 h after treatment. Red = upregulation; Green = downregulation. (see color insert) 38
Figure 2. Effects of α-terthienyl on the growth (photographs) of wild type (Col-0) and transketolase mutant (attkl1) A. thaliana plants and on in vitro transketolase activity from the plants. Reproduced with permission from Zhao et al. (27). (see color insert)
In a less definitive study, Monazzah et al. (28) found the broad spectrum phytotoxin oxalic acid to cause differential expression of 17 proteins in sunflower. Upregulated proteins were involved in carbon fixation, and photosynthesis, as well as stress responses such as apoptosis, heat shock proteins, flavonol synthesis, and antioxidant enzymology. Downregulated proteins included actin, an ATP synthase subunit, the cupin family, and ketol-acid reducto-isomerase. There was no clear indication of a primary target. Similarly, Xie et al. (29) examined changes on the proteome of cotton plants caused by the toxin produced by Verticillum dahlia, and observed upregulation of stress-related protein and downregulation of proteins involved in cell structure and primary metabolism, but no indication of the toxin’s molecular target was discerned. Protein site identification based on interaction of the inhibitor or affector with the protein (chemoproteomics) is being used to find target sites of pharmaceuticals (30). This approach has recently been used to find the target site of the natural product-based herbicide cinmethylin (see discussion below).
Metabolomics Metabolomic profiling has been a method of choice for BASF for new mode of action discovery of herbicides (2, 6, 31). This method generally involves measuring the levels of relatively small number of primary plant metabolites – 39
compounds with molecular weights of 500 or less. Few of these compounds are clear biomarkers of a particular mode of action. Exceptions are large increases in shikimic acid, protoporphyrin IX, and sphingoid bases in the cases of inhibitors of EPSPS, protoporphyinogen IX oxidase, and ceramide synthase, respectively (32–34). Such clear examples of biomarker metabolites for particular modes of action are exceptions. The BASF group claimed that metabolite and physionomic (see below) profiling were critical to their determination that the cineole analog herbicide cinmethlin was an inhibitor of tyrosine amino transferase (6). However, as with the case of transketolase inhibitor discussed above, the relatively high amount of compound needed for in vitro enzyme inhibition was hard to reconcile with the herbicidal activity of the compound. Most herbicides are active at micromolar or lower concentrations at the enzyme level. More recent work by BASF, using chemoproteomics, found that acyl-ACP thioesterases, enzymes involved in fatty acid biosynthesis, are the actual target sites of cinmethylin (35). Cinmethylin is an old herbicide based on natural cineole structure, but the MOA was not confirmed before this recent work. Since our review of 2013, most papers on metabolomics and herbicide effects on plants deal with differentiating herbicide-resistant from wild type weeds (1, 36–38). There are many more papers on the effect of herbicides on the metabolome of mammals. Nevertheless, Pederson et al.,examined the effects of glyphosate and two phytochemical phytotoxins (biochanin A and catechin) on the metabolome of A. thaliana (39). Growth-reducing doses of the three compounds affected 72 to 80% of the plant metabolites. Shikimate increases in response to glyphosate was the only clear indication of a mode of action. Kim et al. (40) provided a metabolomic analysis of the effects of the natural phytotoxin coronatine on duckweed. The concentrations of this jasmonic acid analog that were used were not phytotoxic, so the results could not be used to study phytotoxicity. A problem with all studies based on pool sizes, whether it be mRNA, proteins, or metabolites is that the pool size does not give an indication of the pool turnover rate. The turnover rate in two pools of the same size can be quite different. An effector, whether it is environmental or chemical, can have a profound effect on pool size and/or pool turnover rate. For example, 24 h of light increases the phenylalanine pool flux rate in dark-grown maize seedling roots more than three fold, even though the pool size was reduced by 40% (41). Pool size alone would have indicated that phenyalanine synthesis was reduced when it was actually increased. This early work was done with 14C pulse chase experiments. With GC/MS or LC/MS, similar studies can now be done in metabolomics with stable isotopes (usually 13C or 15N), an approach termed fluxomics (42). Using stable isotope-resolved metabolomics, glyphosate was found to increase de novo amino acid synthesis in glyphosate-susceptible and -resistant Amaranthus palmeri (Figure 3) (37). This more sophisticated approach to metabolomics is more likely to provide a clearer insight into the effects of a phytotoxin on plant metabolism.
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Figure 3. 15N isotopologue enrichment of amino acids in glyphosate-treated sensitive (S)- and resistant (R)-biotypes of Amaranthus palmeri at 36 h after treatment. Asterisks designate a significantly enhanced enrichment in the R biotype. Reproduced with permission from Maroli et al. (37). (see color insert)
Araniti et al. analyzed the phytotoxic effects of coumarin through a metabolomic, proteomic, and morpho-physiological approach in mature Arabidopsis plants (43). Metabolomic analysis found increases of certain amino acids and a high accumulation of soluble sugars, although no definitive mode of action could be established for this phytotoxic secondary metabolite. Proteomic analysis provided no clues to the mode of action.
Physionomics Physionomics is the use of a battery of physiological assays and whole plant bioassays to profile the physiological effects of a phytotoxin (2). Coupled with metabolomics, BASF has used this approach to profile the physiological effects of all known herbicide modes of action as well as the mode of action of several phytotoxins. A recent example of the profile of an antimalarial compound with good herbicidal activity is shown in Figure 4 (44). Such physiological data can be used to focus on aspects of the transcriptome, proteome, or metabolome that are related to observed physiological effects.
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Figure 4. Physiological profile of the effects of MMV007978 in a series of bioassays compared to an untreated control. See Corral et al. (43) for details. Reproduced with permission of Wiley press.
Conclusions Omics methods are powerful tools; however, like any tool, they have be used properly. To date, we cannot point to any purely omics study that has clearly found a new phytotoxin mode of action. However, the studies that have been done provide a wealth of information on secondary and tertiary effects of natural phytotoxins. At best, omics can suggest a mode of action, after which it has to be verified by biochemical, physiological, and/or genetic means.
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