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Record W3000850799 · doi:10.3389/fcimb.2019.00481

Long Non-coding RNA LincRNA-EPS Inhibits Host Defense Against Listeria monocytogenes Infection

2020· article· en· W3000850799 on OpenAlexfundno aff
Federica Agliano, Katherine A. Fitzgerald, Anthony T. Vella, Vijay Rathinam, Andrei E. Medvedev

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthSchool of Medicine, New York UniversityYork University
KeywordsListeria monocytogenesMicrobiologyRNAHost (biology)ListeriaBiologyBacteriaGeneGenetics

Abstract

fetched live from OpenAlex

Long noncoding RNAs (lncRNAs) have emerged as key regulators of gene expression in several biological systems. The long intergenic RNA-erythroid pro-survival (lincRNA-EPS) has been shown to play a critical role in restraining inflammatory gene expression. However, the function of lincRNA-EPS during bacterial infections remains unknown. Here, we demonstrate that following infection with the intracellular bacterium Listeria monocytogenes, both mouse macrophages and dendritic cells lacking lincRNA-EPS exhibit an enhanced expression of proinflammatory cytokine genes, as well as an increased expression of the inducible nitric oxide synthase (iNos) and nitric oxide (NO) production. Importantly, we found that lincRNA-EPS-/- mice intraperitoneally infected with L. monocytogenes exhibit lower bacterial burdens in spleen and liver and produce more NO than control mice. Furthermore, lincRNA-EPS-/- mice are less susceptible to a lethal dose of L. monocytogenes than wild type (WT) mice. Collectively these findings show that lincRNA-EPS suppresses host protective NO expression and impairs the host defense against L. monocytogenes infection.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.238
Teacher spread0.224 · 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.

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

Citations30
Published2020
Admission routes1
Has abstractyes

Explore more

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