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Record W4206769899 · doi:10.1101/2022.01.07.475423

Trade-Offs Between Hepatic Host Defense and Metabolic Programs Underlie Sex-Biased Diseases

2022· preprint· en· W4206769899 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

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsInstitute of Infection and Immunity
FundersNational Health and Medical Research CouncilLiver Center, University of California, San FranciscoUniversity of California, San FranciscoNational Institutes of Health
KeywordsBiologyDiseaseBCL6Host (biology)PhenotypeTranscription factorFatty liverGeneImmunologyGeneticsInternal medicineMedicine

Abstract

fetched live from OpenAlex

ABSTRACT Current concepts in evolutionary medicine propose that trade-offs and mismatches with a shifting environment increase disease risk. While biological sex also impacts disease prevalence, contributions of environmental pressures to sex-biased diseases remain unexplored. Here, we show that sex-dependent hepatic programs confer a robust (~300%) survival advantage for male mice during lethal bacterial infection. The transcription factor BCL6, which masculinizes hepatic gene expression at puberty, is essential for this advantage. However, protection by BCL6 comes at a cost following dietary excess, resulting in overt fatty liver and glucose intolerance in males. Deleting hepatic BCL6 reverses these phenotypes but markedly lowers male fitness during infection, thus establishing a sex-dependent tradeoff between host defense and metabolic systems. We suggest that these tradeoffs, coupled with current environmental pressures, drive metabolic disease in males.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.228
Teacher spread0.215 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicEvolution and Genetic Dynamics→French-language works237,207→