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Record W4306902090 · doi:10.1126/science.abn9886

An evolutionary trade-off between host immunity and metabolism drives fatty liver in male mice

2022· article· en· W4306902090 on OpenAlexaff
Joni Nikkanen, Yew Ann Leong, William C. Krause, Denis Đermadi, J. Alan Maschek, Tyler Van Ry, James E. Cox, Ethan J. Weiss, Ömer Gökçümen, Ajay Chawla, Holly A. Ingraham

Bibliographic record

VenueScience · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on AgingNational Institutes of Health
KeywordsBiologyBCL6PhenotypeImmunityTranscription factorGeneFatty liverDiseaseLipogenesisImmunologyGeneticsImmune systemInternal medicineAntibodyMedicineB cell

Abstract

fetched live from OpenAlex

Adaptations to infectious and dietary pressures shape mammalian physiology and disease risk. How such adaptations affect sex-biased diseases remains insufficiently studied. In this study, we show that sex-dependent hepatic gene programs confer a robust (~300%) survival advantage for male mice during lethal bacterial infection. The transcription factor B cell lymphoma 6 (BCL6), which masculinizes hepatic gene expression at puberty, is essential for this advantage. However, protection by BCL6 protein comes at a cost during conditions of dietary excess, which result in overt fatty liver and glucose intolerance in males. Deleting hepatic BCL6 reverses these phenotypes but markedly lowers male survival during infection, thus establishing a sex-dependent trade-off between host defense and metabolic systems. Our findings offer strong evidence that some current sex-biased diseases are rooted in ancient evolutionary trade-offs between immunity and metabolism.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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

Citations90
Published2022
Admission routes1
Has abstractyes

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