MétaCan
Menu
Back to cohort
Record W4313783106 · doi:10.1101/2023.01.07.523123

Innate immunity can distinguish beneficial from pathogenic rhizosphere microbiota

2023· preprint· en· W4313783106 on OpenAlexaff
David Thoms, Melissa Y. Chen, Yang Liu, Zayda Morales Moreira, Youqing Luo, Siyu Song, Nicole R. Wang, Cara H. Haney

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyInnate immune systemPathogenVirulenceImmune systemPseudomonas syringaeMicrobiologyRhizosphereImmunityPlant ImmunityPathogenic bacteriaMutantBacteriaGeneticsGeneArabidopsis

Abstract

fetched live from OpenAlex

Abstract For optimal growth and development, hosts depend on their ability to promote healthy symbiotic interactions while restricting pathogen growth. To ask whether hosts can distinguish phylogenetically similar pathogens and beneficial bacteria, we used two closely related plant-associated strains of Pseudomonas fluorescens where one is beneficial and the other exhibits toxin-dependent virulence. We show that while the two strains co-exist in vitro , the beneficial outcompetes that pathogen in planta . Using several readouts for plant innate immunity, we found that the beneficial and pathogenic strains elicit mechanistically distinct immune responses that occur in distinct root compartments. We show that while both the pathogenic and beneficial bacterial have plant recognizable MAMPs, the pathogen uniquely induces MAMP-independent immune responses. We found that the pathogen induces both a toxin-independent and a unique toxin-dependent defense response that remains intact in immune signaling mutants including bak1/bkk1 and npr1/4D . We conclude that hosts can distinguish between phylogenetically similar microbes.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.207
Teacher spread0.186 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

Explore more

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