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Record W4280501942 · doi:10.1016/j.celrep.2022.110802

Adipose mitochondrial metabolism controls body growth by modulating systemic cytokine and insulin signaling

2022· article· en· W4280501942 on OpenAlexafffund
Shrivani Sriskanthadevan-Pirahas, Michael J. Turingan, Joel S. Chahal, Erin Thorson, Shahoon Khan, Abdul Qadeer Tinwala, Savraj Grewal

Bibliographic record

VenueCell Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthCanadian Institutes of Health ResearchCancer Research Society
KeywordsAdipose tissueCytokineInsulinMetabolismInsulin receptorSignal transductionEndocrinologyCell biologyInternal medicineBiologyInsulin resistanceMedicine

Abstract

fetched live from OpenAlex

Animals must adapt their growth to fluctuations in nutrient availability to ensure proper development. These adaptations often rely on specific nutrient-sensing tissues that control whole-body physiology through inter-organ communication. While the signaling mechanisms that underlie this communication are well studied, the contributions of metabolic alterations in nutrient-sensing tissues are less clear. Here, we show how the reprogramming of adipose mitochondria controls whole-body growth in Drosophila larvae. We find that dietary nutrients alter fat-body mitochondrial morphology to lower their bioenergetic activity, leading to rewiring of fat-body glucose metabolism. Strikingly, we find that genetic reduction of mitochondrial bioenergetics just in the fat body is sufficient to accelerate body growth and development. These growth effects are caused by inhibition of the fat-derived secreted peptides ImpL2 and tumor necrosis factor alpha (TNF-α)/Eiger, leading to enhanced systemic insulin signaling. Our work reveals how reprogramming of mitochondrial metabolism in one nutrient-sensing tissue can couple nutrient availability to whole-body growth.

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)0.000
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 score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations22
Published2022
Admission routes2
Has abstractyes

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