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Record W4383532359 · doi:10.1101/2023.07.06.548016

A Novel Drosophila Model to Investigate Adipose Tissue Macrophage Infiltration (ATM) and Obesity highlights the Therapeutic Potential of Attenuating Eiger/TNFα Signaling to Ameliorate Insulin Resistance and ATM

2023· preprint· en· W4383532359 on OpenAlexfundno aff
Zhasmine Mirzoyan, Alice Valenza, Sheri Zola, Carola Bonfanti, Lorenzo Arnaboldi, Nicholas Ferrari, John R. Pollard, Valeria Lupi, Matteo Cassinelli, Matteo Frattaroli, Mehtap Sahin, Maria Enrica Pasini, Paola Bellosta

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesScheme for Promotion of Academic and Research CollaborationUniversity of Toronto
KeywordsAdipose tissueInsulin resistanceAdipocyteImmune systemInfiltration (HVAC)InflammationBiologyInsulinCancer researchEndocrinologyImmunology

Abstract

fetched live from OpenAlex

Abstract Obesity is a global health concern associated with various metabolic disorders including insulin resistance and adipose tissue inflammation characterized by adipose tissue macrophage (ATM) infiltration. In this study, we present a novel Drosophila model to investigate the mechanisms underlying ATM infiltration and its association with obesity-related pathologies. Furthermore, we demonstrate the therapeutic potential of attenuating Eiger/TNFα signaling to ameliorate insulin resistance and ATM. To study ATM infiltration and its consequences, we established a novel Drosophila model (OBL) that mimics key aspects of human adipose tissue and allows for investigating ATM infiltration and other related metabolic disorders in a controlled experimental system. We employed genetic manipulation to reduce ecdysone levels to prolong the larval stage. These animals are hyperphagic, and exhibit features resembling obesity in mammals, including increased lipid storage, adipocyte hypertrophy, and high levels of circulating glucose. Moreover, we observed a significant infiltration of immune cells (hemocytes) in the fat bodies accompanied by insulin resistance and systemic metabolic dysregulation. Furthermore, we found that attenuation of Eiger/TNFα signaling and using metformin and anti-oxidant bio-products like anthocyanins led to a reduction in ATM infiltration and improved insulin sensitivity. Our data suggest that the key mechanisms that trigger immune cell infiltration into adipose tissue are evolutionarily conserved and may provide the opportunity to develop Drosophila models to better understand pathways critical for immune cell recruitment into adipose tissue, in relation to the development of insulin resistance in metabolic diseases such as obesity and type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). We believe that our OBL model can also be a valuable tool and provide a platform either to perform genetic screens or to test the efficacy and safety of novel therapeutic interventions for these diseases.

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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.044
GPT teacher head0.268
Teacher spread0.224 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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