Whole-Plant Defenses: How Eukaryotes Catch and Kill Unwary Microbial Predators
Bibliographic record
Abstract
Surveillance of their cellular surroundings with a complex network of cell-surface immune molecules enables plants as well as animals to detect microbial intruders early enough to defeat them—a system considered by evolutionary biologists to be one of the most ancient and best conserved of all immune defenses. Rice plants, for example, defend their leaves against the crop-damaging bacterial pathogen Xanthomonas oryzae (Xoo) with XA21ࣧthe first characterized of more than 350 immune receptors, says Pamela Ronald, of the University of California, Davis. Ronald discovered XA21 in 1995. Late last year, Ronald and colleagues identified three Xoo rax genes needed to activate XA21-mediated immunity. These genes suggest that sulfation is important in the invasion process, say the researchers, who named the newly identified tyrosine-sulfated protein that the genes encode RaxX. Sulfation, first identified in 1954, is known to strengthen protein-to-protein bonding and probably makes Xoo a more stable pathogen. If true, this invites speculation that by using the pathogen's RaxX as a marker for the presence of Xanthomonas, rice has out-evolved its bacterial predator, at least for the moment. Leaves dominate the plant universe, or phyllosphere, which includes all of the aboveground parts and “at 1 billion km2 is one of the largest biological surfaces of Earth, outmatching land masses by roughly seven times,” write Mitja Remus-Emsermann and Julia Vorholt in the December 2014 issue of Microbe magazine. The microbes that live on leaves, mostly bacteria, “have to be admired as survival experts” contending with multiple assaults, from damaging ultraviolet radiation to torrential rains, say Remus-Emsermann and Vorholt. Despite all the difficulties, leaves host a diverse community of protective bacteria. But only those from a limited number of phyla appear able to cope; these include Actinobacteria, Bacteroidetes, Firmicutes, and the most predominant phylum, Proteobacteria—the same bacteria consistently found in the roots of the well-studied model plant Arabidopsis thaliana and in human gastrointestinal tracts. Because they are relatively easy to find and host fewer microbes than roots do, leaves have been comparatively well studied, but roots, hidden underground and exposed to hundreds of microbial species and trillions of single cells, are finally getting the attention they deserve. Like animal guts, plant root microbiomes are necessary for optimal nutrition and protection against pathogens. Therefore, it is not surprising that plant roots are functionally analogous to animal guts; they have the same problems and the same molecular toolbox, say Cara Haney, of the University of British Columbia, in Vancouver, and Frederick Ausubel, of Harvard Medical School, in the 21 August 2015 issue of Science. By planting wild versions of A. thaliana with varying abilities to attract the beneficial bacterium Pseudomonas fluorescens in microbe-rich natural soil, Haney and colleagues established that plants can help assemble their own microbiomes. The A. thaliana genetically able to support P. fluorescens were protected from many diseases, but those without such genes were not. Although most of the microbes colonizing both roots and guts are passively acquired from their environments, even a small genetic component can have a big impact on plant and animal health, Haney and colleagues discovered. How some A. thaliana manage the difficult task of crafting their preferred underground microbial communities has recently been revealed by Sarah Lebeis, of the University of Tennessee, Knoxville, in conjunction with colleagues in Jeffery Dangl's laboratory at the University of North Carolina at Chapel Hill. Salicylic acid (SA), jasmonic acid, and gaseous ethylene are important immune regulators in plant shoots and leaves, so scientists reasoned that they would exert a similar authority over roots. As proof of concept, Lebeis and collaborators compared the root microbiomes of wild A. thaliana with the microbiomes of mutant plants unable to either synthesize or read the signals of one or more of these defense hormones. Two groups emerged: one that produced and accumulated large amounts of SA and another that acquired less, stored less, or did not respond to its signals. Only the A. thaliana deficient in either the biosynthesis or signaling recognition of SA developed abnormal microbial communities, indicating that this hormone plays an important role in shaping their root microbiome. Anthony Trewavas, of the Institute of Molecular Plant Science in Edinburgh, Scotland, also notes the importance of interactions of rhizosphere bacteria and symbiotic mycorrhizal fungi known to enhance host resistance and the volatile organic compounds (VOCs) synthesized by both groups to “beneficially reprogram root architectureࣧexpanding and deepening resistance capabilities.” “VOCs [important for plant communication] are probably the basis of self-recognition and alien detection in plant roots,” Trewavas says.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".