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Record W3041951447 · doi:10.1101/2020.07.09.186114

Intestinal-epithelial LSD1 controls cytoskeletal-mediated cell identity including goblet cell effector responses required for gut inflammatory and infectious diseases

2020· preprint· en· W3041951447 on OpenAlexaff
N. Parmar, Kyle Burrows, Håvard T. Lindholm, Rosalie T. Zwiggelaar, Mara Martín-Alonso, Madeleine Fosslie, Bruce A. Vallance, John Arne Dahl, Colby Zaph, Menno J. Oudhoff

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsEffectorBiologyIntestinal epitheliumGoblet cellImmunologyInflammationReprogrammingCell biologyHistoneEpitheliumDemethylaseEpigeneticsCellGeneticsGene

Abstract

fetched live from OpenAlex

ABSTRACT Infectious and inflammatory diseases in the intestine remain a serious threat for patients world-wide. Reprogramming of the intestinal epithelium towards a protective effector state is important to manage inflammation and immunity. The role of epigenetic regulatory enzymes within these processes is not yet defined. Here, we use a mouse model that has an intestinal-epithelial specific deletion of the histone demethylase Lsd1 (cKO mice), which maintains the epithelium in a fixed reparative state. Challenge of cKO mice with chemical-induced colitis, bacteria-induced colitis, and a helminth infection model all resulted in increased pathogenesis. Mechanistically, we discovered that LSD1 directly controls genes that facilitate cytoskeletal organization, and that this is relevant for epithelial attachment as well as for goblet-cell specific effector responses.This study therefore identifies intestinal-epithelial epigenetic regulation by LSD1 as a critical element in host protection from inflammation and 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 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 score0.003
Threshold uncertainty score0.010

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.246
Teacher spread0.232 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

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