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Record W4213221167 · doi:10.1128/mbio.03576-21

Pervasive RNA Regulation of Metabolism Enhances the Root Colonization Ability of Nitrogen-Fixing Symbiotic α-Rhizobia

2022· article· en· W4213221167 on OpenAlexafffund
Natalia I. García‐Tomsig, Marta Robledo, George C. diCenzo, Alessio Mengoni, Vicenta Millán, Alexandra Peregrina, Alejandro Uceta, José I. Jiménez‐Zurdo

Bibliographic record

VenuemBio · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsQueen's University
FundersAgencia Estatal de InvestigaciónEuropean Regional Development FundGovernment of CanadaJunta de AndalucíaConsejo Superior de Investigaciones CientíficasMinisterio de UniversidadesNatural Sciences and Engineering Research Council of CanadaMinisterio de Ciencia e Innovación
KeywordsSinorhizobium melilotiBiologyRhizobiaRhizobiumRhizosphereTranscriptomeCell biologySymbiosisGeneticsGeneGene expressionBacteria

Abstract

fetched live from OpenAlex

Nitrogen-fixing root nodule symbioses between rhizobia and legume plants provide more than half of the combined nitrogen incorporated annually into terrestrial ecosystems, rendering plant growth independent of environmentally unfriendly chemical fertilizers. The success of symbiosis depends primarily on the capacity of rhizobia to establish competitive populations in soil and rhizosphere environments. Here, we provide insights into the regulation and architecture of an extensive RNA posttranscriptional network that fine-tunes the metabolism of the alfalfa symbiont S. meliloti, thereby enhancing the ability of this beneficial bacterium to colonize nutrient-rich but extremely selective niches, such as the rhizosphere of its host plant. This pervasive RNA regulation of metabolism is a major adaptive mechanism, predicted to operate in diverse rhizobial species. Because RNA regulation relies on modifiable base-pairing interactions, our findings open unexplored avenues for engineering the legumes rhizobiome within sustainable agricultural practices.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.272
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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 teacher head, 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

Citations16
Published2022
Admission routes2
Has abstractyes

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