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Record W4308732928 · doi:10.1101/2022.11.07.515115

PSKR1 balances the plant growth-defense trade-off in the rhizosphere microbiome

2022· preprint· en· W4308732928 on OpenAlexaff
Siyu Song, Zayda Morales Moreira, Xuecheng Zhang, Andrew C. Diener, Cara H. Haney

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyRhizospherePseudomonas fluorescensMicrobiomeAutoimmunityArabidopsisImmune systemCell biologyPlant ImmunityColonizationTransgeneMutantMicrobiologyGeneticsBacteriaGene

Abstract

fetched live from OpenAlex

Abstract Microbiota provide benefits to their hosts including nutrient uptake and protection against pathogens. How hosts balance an appropriate immune response to avoid microbiota overgrowth while avoiding autoimmunity is not well understood. Here we show that Arabidopsis pskr1 ( phytosulfokine receptor 1 ) loss-of-function mutants display autoimmunity and reduced rhizosphere bacterial growth when inoculated with normally growth-promoting Pseudomonas fluorescens . Transcriptional profiling demonstrated that PSKR1 regulates the plant growth-defense trade-off during plant-microbiome interactions: PSKR1 upregulates plant photosynthesis and root growth but suppresses salicylic acid (SA)-mediated defense responses. Genetic epistasis experiments showed that PSRK1 inhibition of microbiota-induced autoimmunity is fully dependent on SA signaling. Finally, using a transgenic reporter, we showed that P. fluorescens induces PSKR1 expression in roots, suggesting P. fluorescens might manipulate plant signaling to promote its colonization. Our data demonstrate a genetic mechanism to coordinate beneficial functions of the microbiome while preventing autoimmunity.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.199
Teacher spread0.182 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicPlant-Microbe Interactions and ImmunityFrench-language works237,207