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Record W2130127620 · doi:10.1681/asn.2011070690

Inhibition of MTOR Disrupts Autophagic Flux in Podocytes

2011· article· en· W2130127620 on OpenAlexafffund
Davide Cina, Tuncer Onay, Aarti Paltoo, Chengjin Li, Yoshiro Maezawa, Javier de Arteaga, Andrea Jurisicova, Susan E. Quaggin

Bibliographic record

VenueJournal of the American Society of Nephrology · 2011
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity Health NetworkMount Sinai HospitalSt. Michael's HospitalLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAutophagyPI3K/AKT/mTOR pathwayPodocyteProteinuriaSirolimusCell biologyPathogenesisRPTORMitochondrionCancer researchBiologyKidneyEndocrinologyChemistryInternal medicineMedicineSignal transductionApoptosisBiochemistry

Abstract

fetched live from OpenAlex

Inhibitors of the mammalian target of rapamycin (MTOR) belong to a family of drugs with potent immunosuppressive, antiangiogenic, and antiproliferative properties. De novo or worsening proteinuria can occur during treatment with these agents, but the mechanism by which this occurs is unknown. We generated and characterized mice carrying a podocyte-selective knockout of the Mtor gene. Although Mtor was dispensable in developing podocytes, these mice developed proteinuria at 3 weeks and end stage renal failure by 5 weeks after birth. Podocytes from these mice exhibited an accumulation of the autophagosome marker LC3 (rat microtubule-associated protein 1 light chain 3), autophagosomes, autophagolysosomal vesicles, and damaged mitochondria. Similarly, human podocytes treated with the MTOR inhibitor rapamycin accumulated autophagosomes and autophagolysosomes. Taken together, these results suggest that disruption of the autophagic pathway may play a role in the pathogenesis of proteinuria in patients treated with MTOR inhibitors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.275
Teacher spread0.255 · 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 designObservational
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

Citations170
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicRenal Diseases and GlomerulopathiesFrench-language works237,207