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Record W2077399856 · doi:10.1186/ar3939

MicroRNA-3148 modulates differential gene expression of the SLE-associated TLR7 variant

2012· article· en· W2077399856 on OpenAlexfundno aff
Yun Deng, Jian Zhao, Daisuke Sakurai, KM Kaufman, JC Edberg, Robert P. Kimberly, Diane L. Kamen, GS Gilkeson, CO Jacob, RH Scofield, CD Langefeld, Jennifer A. Kelly, Marta E. Alarcón‐Riquelme, JB Harley, TJ Vyse, BI Freedman, PM Gaffney, KM Sivils, James Ja, Timothy B. Niewold, R M Cantor, Wei Chen, Beatrice H. Hahn, EE Brown, Betty P. Tsao

Bibliographic record

VenueArthritis Research & Therapy · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsnot available
FundersNational Institutes of HealthCanadian Arthritis NetworkNational Institute of Arthritis and Musculoskeletal and Skin DiseasesArthritis SocietyLupus Research AllianceArthritis Foundation
KeywordsmicroRNARheumatologyGeneGene expressionMedicineTLR7BiologyInternal medicineComputational biologyBioinformaticsGeneticsReceptorToll-like receptor

Abstract

fetched live from OpenAlex

We identified the G allele of TLR7 3'-UTR SNP (rs3853839) associated with increased TLR7 transcripts, a more pronounced IFN signature and risk for SLE in 9,274 Eastern Asians ( P combined = 6.5 × 10 ) [ 1 ]. The current study sought replication of SLE-associated SNP(s) in non-Asian ancestries and explored molecular mechanisms underlying an identified gene variant that affects TLR7 expression. We conducted genotyping, imputation and association for 98 to 116 SNPs (varying among different ancestries) covering 80 kb of TLR7-TLR8 in European Americans (EA), African Americans (AA) and Hispanics enriched for the Amerindian-European admixture (HS). Haplotype-based conditional testing was conducted to distinguish independent association signals. Mantel-Haenszel testing was used in transancestral meta-analysis. Association of genotypes with TLR7 expression was examined using RT-PCR, flow cytometry and reporter assays. Pyrosequencing was used to measure allelic variations in TLR7 transcript levels. The rs3853839 was confirmed as the only variant within TLR7-TLR8 exhibiting consistent and independent association with SLE in our transancestral fine-mapping ( P meta = 7.5 × 10 , OR (95% CI) = 1.24 (1.18 to 1.34)) in 13,339 subjects of EA (3,936 cases vs. 3,491 controls), AA (1,679 vs. 1,934) and HS (1,492 vs. 807) ancestries. PBMCs from normal G-allele carriers exhibited elevated levels of TLR7 mRNA ( P = 0.01 in men and P = 0.02 in women) and protein ( P = 0.009 in men and P = 0.038 in women). PBMCs from heterozygotes exhibited higher G/C allele ratios of TLR7 transcripts 4 hours after incubation with actinomycin D (inhibitor of transcription initiation) ( P = 0.04), indicating slower degradation of G allele-containing transcript. The nonrisk allele, but not the risk allele, was predicted to match microRNA-3148 (miR-3148) at the second base in the binding site. Transcript levels of miR-3148 and TLR7 were inversely correlated in PBMCs from 16 SLE patients and 21 controls ( R = 0.255, P = 0.001), suggesting miR-3148 modulating TLR7 expression. Overexpression of miR-3148 via transfection into HEK 293 cells led to a more than twofold reduction in luciferase activity driven by the TLR7 3'-UTR segment containing the nonrisk allele than that containing the risk allele ( P = 0.001). We identified and confirmed a genome-wide significant association between rs3853839 and SLE susceptibility in 22,613 subjects of Eastern Asian, EA, AA and HS ancestries ( P meta = 6.4 × 10 , OR (95% CI) = 1.26 (1.20 to 1.32)). Reduced modulation by miR-3148 confers slower degradation of the risk allele containing TLR7 transcript, resulting in elevated levels of gene products and a more robust type I IFN signature.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.027
GPT teacher head0.284
Teacher spread0.256 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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