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Record W4399693429 · doi:10.1101/2024.06.11.598322

Renalase inhibition regulates β cell metabolism to defend against acute and chronic stress

2024· preprint· en· W4399693429 on OpenAlexaff
Tara MacDonald, Birgitta Ryback, Jéssica Aparecida da Silva Pereira, Siying Wei, Bryhan Mendez, Erica P. Cai, Yuki Ishikawa, Gordon C. Weir, Susan Bonner‐Weir, Stephan Kissler, Yi Peng

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMetabolismCell metabolismChronic stressCellCell biologyChemistryMedicineBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract Renalase (Rnls), annotated as an oxidase enzyme, is a GWAS gene associated with Type 1 Diabetes (T1D) risk. We previously discovered that Rnls inhibition delays diabetes onset in mouse models of T1D in vivo , and protects pancreatic β cells against autoimmune killing, ER and oxidative stress in vitro . The molecular biochemistry and functions of Rnls are entirely uncharted. Here we find that Rnls inhibition defends against loss of β cell mass and islet dysfunction in chronically stressed Akita mice in vivo . We used RNA sequencing, untargeted and targeted metabolomics and metabolic function experiments in mouse and human β cells and discovered a robust and conserved metabolic shift towards glycolysis, amino acid abundance and GSH synthesis to counter protein misfolding stress, in vitro . Our work illustrates a function for Rnls in mammalian cells, and suggests an axis by which manipulating intrinsic properties of β cells can rewire metabolism to protect against diabetogenic stress.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.195
Teacher spread0.190 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicDiabetes and associated disorders→French-language works237,207→