Plants have evolved intracellular immune receptors to detect pathogen proteins known as effectors. How these immune receptors detect effectors remains poorly understood. Here we describe the structural basis for direct recognition of AVR-Pik, an effector from the rice blast pathogen, by the rice intracellular NLR immune receptor Pik. AVR-PikD binds a dimer of the Pikp-1 HMA integrated domain with nanomolar affinity. The crystal structure of the Pikp-HMA/AVR-PikD complex enabled design of mutations to alter protein interaction in yeast and in vitro, and perturb effector-mediated response both in a rice cultivar containing Pikp and upon expression of AVR-PikD and Pikp in the model plant Nicotiana benthamiana. These data reveal the molecular details of a recognition event, mediated by a novel integrated domain in an NLR, which initiates a plant immune response and resistance to rice blast disease. Such studies underpin novel opportunities for engineering disease resistance to plant pathogens in staple food crops.DOI: http://dx.doi.org/10.7554/eLife.08709.001

The Arabidopsis genes FT and TERMINAL FLOWER1 (TFL1) encode related proteins with similarity to human Raf kinase inhibitor protein. FT, and likely also TFL1, is recruited to the promoters of floral genes through interaction with FD, a bZIP transcription factor. FT, however, induces flowering, while TFL1 represses flowering. Residues responsible for the opposite activities of FT and TFL1 were mapped by examining plants that overexpress chimeric proteins. A region important in vivo localizes to a 14-amino-acid segment that evolves very rapidly in TFL1 orthologs, but is almost invariant in FT orthologs. Crystal structures show that this segment forms an external loop of variable conformation. The only residue unambiguously distinguishing the FT and TFL1 loops makes a hydrogen bond with a residue near the entrance of a potential ligand-binding pocket in TFL1, but not in FT. This pocket is contacted by a C-terminal peptide, which also contributes to the opposite FT and TFL1 activities. In combination, these results identify a molecular surface likely to be recognized by FT-and/or TFL1-specific interactors.

Accelerated adaptive evolution is a hallmark of plant-pathogen interactions. Plant intracellular immune receptors (NLRs) often occur as allelic series with differential pathogen specificities. The determinants of this specificity remain largely unknown. Here, we unravelled the biophysical and structural basis of expanded specificity in the allelic rice NLR Pik, which responds to the effector AVR-Pik from the rice blast pathogen Magnaporthe oryzae. Rice plants expressing the Pikm allele resist infection by blast strains expressing any of three AVR-Pik effector variants, whereas those expressing Pikp only respond to one. Unlike Pikp, the integrated heavy metal-associated (HMA) domain of Pikm binds with high affinity to each of the three recognized effector variants, and variation at binding interfaces between effectors and Pikp-HMA or Pikm-HMA domains encodes specificity. By understanding how co-evolution has shaped the response profile of an allelic NLR, we highlight how natural selection drove the emergence of new receptor specificities. This work has implications for the engineering of NLRs with improved utility in agriculture.

Metals are needed by at least one-quarter of all proteins. Although metallochaperones insert the correct metal into some proteins, they have not been found for the vast majority, and the view is that most metalloproteins acquire their metals directly from cellular pools. However, some metals form more stable complexes with proteins than do others. For instance, as described in the Irving-Williams series, Cu(2+) and Zn(2+) typically form more stable complexes than Mn(2+). Thus it is unclear what cellular mechanisms manage metal acquisition by most nascent proteins. To investigate this question, we identified the most abundant Cu(2+)-protein, CucA (Cu(2+)-cupin A), and the most abundant Mn(2+)-protein, MncA (Mn(2+)-cupin A), in the periplasm of the cyanobacterium Synechocystis PCC 6803. Each of these newly identified proteins binds its respective metal via identical ligands within a cupin fold. Consistent with the Irving-Williams series, MncA only binds Mn(2+) after folding in solutions containing at least a 10(4) times molar excess of Mn(2+) over Cu(2+) or Zn(2+). However once MncA has bound Mn(2+), the metal does not exchange with Cu(2+). MncA and CucA have signal peptides for different export pathways into the periplasm, Tat and Sec respectively. Export by the Tat pathway allows MncA to fold in the cytoplasm, which contains only tightly bound copper or Zn(2+) (refs 10-12) but micromolar Mn(2+) (ref. 13). In contrast, CucA folds in the periplasm to acquire Cu(2+). These results reveal a mechanism whereby the compartment in which a protein folds overrides its binding preference to control its metal content. They explain why the cytoplasm must contain only tightly bound and buffered copper and Zn(2+).

Plants use autophagy to safeguard against infectious diseases. However, how plant pathogens interfere with autophagy-related processes is unknown. Here, we show that PexRD54, an effector from the Irish potato famine pathogen Phytophthora infestans, binds host autophagy protein ATG8CL to stimulate autophagosome formation. PexRD54 depletes the autophagy cargo receptor Joka2 out of ATG8CL complexes and interferes with Joka2's positive effect on pathogen defense. Thus, a plant pathogen effector has evolved to antagonize a host autophagy cargo receptor to counteract host defenses.DOI: http://dx.doi.org/10.7554/eLife.10856.001

Genes in the TERMINAL FLOWER1 ( TFL1 ) /CENTRORADIALIS family are important key regulatory genes involved in the control of flowering time and floral architecture in several different plant species. To understand the functions of TFL1 homologs in pea, we isolated three TFL1 homologs, which we have designated PsTFL1a , PsTFL1b , and PsTFL1c . By genetic mapping and sequencing of mutant alleles, we demonstrate that PsTFL1a corresponds to the DETERMINATE (

We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed on experimental results from 57 publicly available data sets. When cross-validation comparisons were tested for statistical significance, the best method was statistically more accurate than bagging on only eight of the 57 data sets. Alternatively, examining the average ranks of the algorithms across the group of data sets, we find that boosting, random forests, and randomized trees are statistically significantly better than bagging. Because our results suggest that using an appropriate ensemble size is important, we introduce an algorithm that decides when a sufficient number of classifiers has been created for an ensemble. Our algorithm uses the out-of-bag error estimate, and is shown to result in an accurate ensemble for those methods that incorporate bagging into the construction of the ensemble.

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