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Record W4392199830 · doi:10.1093/plcell/koae059

It takes two to tango: Plant hosts influence bacterial effector function through post-translational modifications

2024· editorial· en· W4392199830 on OpenAlexaff
Bradley Laflamme

Bibliographic record

VenueThe Plant Cell · 2024
Typeeditorial
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEffectorBiologyPseudomonas syringaeVirulenceSUMO proteinArabidopsisFunction (biology)Cell biologyUbiquitinSecretionMicrobiologyPathogenGeneticsGeneMutantBiochemistry

Abstract

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Many bacterial phytopathogens secrete virulence proteins (often termed effectors) directly into plant cells to aid in infecting and colonizing host tissues (Cunnac et al. 2011). While certain effectors have well-established virulence functions inside of the plant cell—typically involving interference with host immune mechanisms—we have a paler understanding of how the host might influence effector activities. After all, eukaryotic cells are wired with organelles and biological processes that prokaryotes do not possess. However, prokaryotic pathogens, despite lacking these cellular mechanisms, display an intriguing ability to leverage and exploit eukaryotic features during infection. Thus, it is exciting to consider how coevolution has driven prokaryotic pathogens to integrate eukaryotic tools into their infection strategy. In this issue, Li and colleagues (Li et al. 2024) investigated the contribution of SUMOylation (SUMO = Small Ubiquitin-related MOdifier), a common post-translational modification (PTM) in eukaryotes, to the function of effectors from the bacterial pathogen Pseudomonas syringae, a model bacterial phytopathogen with a well-cataloged effector repertoire. By performing an in vitro SUMOylation screen in Escherichia coli engineered to express Arabidopsis SUMOylation machinery, the group found that 16 out of 36 P. syringae effectors could be SUMOylated. This screen thus suggested that almost one-half of the tested bacterial effectors—spanning a broad range of biochemical functions and in planta targets—could potentially serve as substrates for PTMs once inside the plant cell. The group then focused on 2 SUMOylated effectors with well-established virulence activities, HopB1 and HopG1, to characterize the impact of their PTMs on P. syringae pathogenesis—after, of course, confirming that both effectors were SUMOylated in planta. HopB1 is a serine protease that cleaves the Arabidopsis co-receptor BAK1, inducing a host immune response that results in programmed cell death (Li et al. 2016). Remarkably, a HopB1 mutant deficient in 2 SUMOylation sites was found to be more effective at cleaving this receptor, ultimately triggering a more robust cell death response than the wild-type allele and having a detrimental impact on virulence when expressed in P. syringae. HopG1 is an effector that localizes to the plant mitochondria, where it induces mitochondrial dysfunction and oxidative stress (Block et al. 2010). A SUMOylation-deficient mutant of HopG1 showed reduced virulence activities, resulting in a lower accumulation of reactive oxygen species and a higher number of functional plant mitochondria per cell. Thus, SUMOylation of both HopB1 and HopG1 appears to benefit P. syringae, resulting in increased effector virulence (Fig.). A proposed model for SUMOylation of HopB1 and HopG1 in plant infection. A SUMOylation-deficient mutant (2KR) of HopB1 triggers an amplified cell death response by cleaving the BAK1 transmembrane receptor that is detrimental to P. syringae virulence. A similar 2KR mutant of HopG1 fails to damage mitochondria (represented by green dots for active mitochondria and grey dots for inactive), ultimately leading to a reduced production of reactive oxygen species (ROS). S: SUMO; HT: high temperature. Reproduced from Figure 4I. The authors simultaneously investigated the transcriptomic responses of plants to both the wild-type and SUMOylation-deficient alleles of both HopB1 and HopG1. Their analyses demonstrated that SUMOylation dampens the induction of immune-related genes by HopB1 and improves the capacity of HopG1 to positively regulate genes encoding negative regulators of jasmonic acid signaling, thus showing that the observed benefits of SUMOylations to pathogen virulence can be reflected in the host transcriptome. Additionally, the group explored the impact of heat stress on effector function, as SUMOylation of HopB1 and HopG1 was increased at higher temperatures. Remarkably, both effectors had a greater requirement for SUMOylation at higher temperatures: the immune-activating properties of the HopB1 SUMOylation-deficient mutant were exacerbated at higher temperatures, leading to reduced pathogen virulence, while the SUMOylation-deficient mutant of HopG1 lost much of its virulence outputs at higher temperatures. Thus, in a warming climate, the pathogen's cooption of host PTMs may be of greater importance to establishing infection. Li and colleagues provide strong evidence for SUMOylation as an underacknowledged factor that regulates the interactions between plants and phytopathogenic bacteria, and their findings on the effects of heat stress on effector SUMOylation may also have implications in a warming climate. Their observations are especially interesting because bacterial effectors frequently “mimic” host proteins in function, likely to conceal their invasion of host tissues (Galán 2009). Interestingly, most of the effectors present in the “minimal functional repertoire” of P. syringae—that is a reduced, core set of 8 effectors that the pathogen can use to achieve virulence (Cunnac et al. 2011)—are likely SUMOylation substrates. Considering the use of SUMOylation sites by bacterial pathogens from an evolutionary perspective (as the authors encourage us to do), we might opine that the adoption of eukaryotic sequence motifs represents a key step in the development of bacterial pathogenesis. In this case, the plant can only be so upset about getting infected—after all, the bacterium learned some of its tricks from its host. The following phenotypic, genotypic, and functional terms are of significance to the work described in this paper: PTM AmiGo: PO:0005039 BAK1 Gramene: AT4G33430 BAK1 Araport: AT4G33430

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0020.002
Research integrity0.0100.016
Insufficient payload (model declined to judge)0.0090.008

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.

Opus teacher head0.017
GPT teacher head0.233
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2024
Admission routes1
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