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Record W3179697950 · doi:10.1101/2021.07.12.452110

TOR signalling is required for host lipid metabolic remodelling and survival following enteric infection in <i>Drosophila</i>

2021· preprint· en· W3179697950 on OpenAlexaff
Rujuta Deshpande, Byoungchun Lee, Yuemeng Qiao, Savraj Grewal

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsBiologyLipid metabolismInnate immune systemImmune systemHost (biology)LipolysisCell biologySignallingMicrobiologyHedgehog signaling pathwayMetabolic pathwayMetabolismAdipose tissueImmunologySignal transductionGeneticsBiochemistry

Abstract

fetched live from OpenAlex

SUMMARY When infected by enteric pathogenic bacteria, animals need to initiate local and whole-body defence strategies. While most attention has focused on the role of innate immune anti-bacterial responses, less is known about how changes in host metabolism contribute to host defence. Using Drosophila as a model system, we identify induction of intestinal target-of-rapamycin (TOR) kinase signalling as a key adaptive metabolic response to enteric infection. We find TOR is induced independently of the IMD innate immune pathway, and functions together with IMD signalling to promote infection survival. These protective effects of TOR signalling are associated with re-modelling of host lipid metabolism. Thus, we see that TOR switches intestinal metabolism to lipolysis and fatty acid oxidation. In addition, TOR is required to limit excessive infection mediated wasting of adipose lipid stores by promoting an increase in the levels of fat body-expressed de novo lipid synthesis genes. Our data support a model in which induction of TOR represents a host tolerance response to counteract infection-mediated lipid wasting in order to promote survival.

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)0.002
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 score0.026
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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