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Record W4313453433 · doi:10.15252/emmm.202216225

Targeting gut dysbiosis against inflammation and impaired autophagy in Duchenne muscular dystrophy

2023· article· en· W4313453433 on OpenAlexafffund
Hilal Kalkan, Ester Pagano, Debora Paris, Elisabetta Panza, Mariarosaria Cuozzo, Claudia Moriello, Fabiana Piscitelli, Armita Abolghasemi, Elisabetta Gazzerro, Cristoforo Silvestri, Raffaele Capasso, Andréa Motta, Roberto Russo, Vincenzo Di Marzo, Fabio Arturo Iannotti

Bibliographic record

VenueEMBO Molecular Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersSentinelle Nord, Université LavalInstitute of GeneticsDuchenne Parent Project
KeywordsDuchenne muscular dystrophyInflammationAutophagyEndocannabinoid systemMuscular dystrophyBiologyEndocrinologyReceptorMedicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Nothing is known about the potential implication of gut microbiota in skeletal muscle disorders. Here, we provide evidence that fecal microbiota composition along with circulating levels of short-chain fatty acids (SCFAs) and related metabolites are altered in the mdx mouse model of Duchenne muscular dystrophy (DMD) compared with healthy controls. Supplementation with sodium butyrate (NaB) in mdx mice rescued muscle strength and autophagy, and prevented inflammation associated with excessive endocannabinoid signaling at CB1 receptors to the same extent as deflazacort (DFZ), the standard palliative care for DMD. In LPS-stimulated C2C12 myoblasts, NaB reduces inflammation, promotes autophagy, and prevents dysregulation of microRNAs targeting the endocannabinoid CB1 receptor gene, in a manner depending on the activation of GPR109A and PPARγ receptors. In sum, we propose a novel disease-modifying approach in DMD that may have benefits also in other muscular dystrophies.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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

Citations75
Published2023
Admission routes2
Has abstractyes

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