RNA Editing Alterations Define Disease Manifestations in the Progression of Experimental Autoimmune Encephalomyelitis (EAE).
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce the ANALYSIS, not the exact numbers. Paper reanalyses GSE59725 microglia RNA-seq (3 samples: naive SRR1524294 / pre-clinical SRR1524293 / acute SRR1524292) for RNA editing via a 2017 pipeline (trim_galore -> tophat2/mm10 -> REDItools de-novo -> ANNOVAR -> custom R splitting ADAR A-to-I from APOBEC C-to-U). Reproduced faithfully on the SAME data with the paper's exact thresholds (BQ>=25, depth>=10, >=3 supporting bases, freq>=0.1, FDR<=0.05, multimappers excluded), substituting deprecated/license-gated tools per P16: tophat2 -> HISAT2 2.2.2 (SNP-aware GRCm38), REDItools-v1/Python2 -> a samtools-mpileup de-novo caller (binomial vs Q25 error + BH-FDR), ANNOVAR (license-gated) -> not used (enzyme/site COUNTS do not need it). OUTCOME = strong QUALITATIVE 1:1, weak absolute: every directional/structural claim reproduces -- A-to-I dominates C-to-U (~2-3x), A>G is the top RDD type, total editing decreases monotonically with disease, pre-clinical reduction 6.8% matches the paper's 7% almost exactly, and the enzyme-specific pattern (APOBEC-driven loss early, ADAR-driven loss late) reproduces. Absolute high-confidence counts are ~5x higher than the paper (6191 vs 1225 naive) because two false-positive-pruning steps were intentionally NOT applied -- ANNOVAR dbSNP142 exclusion and REDItools BLAT correction -- plus REDItoolDenovo's substitution-specific significance test is stricter than the binomial proxy. NOT ATTEMPTED (hard ~20% / out of scope): exact ANNOVAR-based genomic-region distribution percentages (license-gated DB), BLAT correction, Enrichr/KEGG pathway analysis (web tool), and all wet-lab validations (qPCR/Sanger, APOBEC-1 KO in-vivo EAE severity, IHC). This is a faithful PARTIAL reproduction: the paper's biological conclusions hold on re-analysis; the precise site counts depend on the unspecified/legacy filtering stack and are not byte-reproducible.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
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v1 current initial assessment Score 37assessed: 2026-06-15 ⛓ 0b726b00947e
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusThe study tests whether RNA editing, mediated by ADAR and APOBEC deaminases, plays a functional and protective role in the pathogenesis and progression of experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis.
- ★ RNA-editing events mediated by APOBEC and ADAR deaminases are significantly reduced throughout the course of EAE disease progression. finding
- ★ Loss of APOBEC-1 (knock-out) significantly increases EAE severity compared to wild-type controls, indicating RNA editing confers a protective role during EAE progression. finding
- ★ Reduced RNA editing possibly affects protein expression necessary for normal neurological function, implicating epitranscriptomic regulatory mechanisms in EAE/MS pathogenesis. mechanism
- ★ APOBEC-1 KO EAE mice show earlier clinical signs, higher microglia density, and increased gliosis than wild-type mice. finding
- An in silico RNA editing analysis pipeline applied to microglia RNA-seq across EAE stages establishes transcriptome-wide microglia editomes during disease progression. method
- Selected differentially edited/expressed targets Mpeg1 (ADAR/A-I) and B2m (APOBEC/C-U) were validated in murine microglia and brain tissue. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA editing analysis of bulk RNA-seq (in silico) | brain and spinal cord microglia from naïve and EAE female C57BL/6 mice (GSE59725, pools of 10 animals/group) | MOG35-55-induced EAE (pre-clinical 7 dpi, acute 14 dpi) vs naïve | C-U and A-I RNA editing events (RDDs) | REDItools and VarScan; mm10 reference; Enrichr/KEGG for pathway analysis |
| RNA editing validation by amplicon Sanger sequencing (direct and TA cloning) | isolated brain microglia and bulk brain tissue (whole brain/cortex) from naïve and acute EAE C57BL/6 mice | MOG35-55-induced acute EAE vs naïve | editing frequency at Mpeg1 (3'UTR, A-I) and B2m (3'UTR, C-U); RDDs via cDNA-to-gDNA alignment | Q5 polymerase; pDrive TA cloning; v3.1 BigDye, ABI 3730 genetic analyzer; BioEdit v7.09.0 |
| Quantitative real-time PCR (qRT-PCR) | naïve and acute EAE brain microglia | MOG35-55-induced acute EAE vs naïve | differential gene expression of Mpeg1 and B2m (2^-ΔΔCt, Gapdh normalized) | 7500 Fast Real-Time PCR System (Applied Biosystems); KAPA SYBR Fast |
| In vivo EAE clinical scoring | wild-type (n=9) and APOBEC-1 KO (n=11) 8-10 week old female C57BL/6 mice | APOBEC-1 knock-out; MOG35-55/CFA + pertussis toxin EAE induction with MOG booster | daily clinical score, mean maximal score (mMS), mean AUC (mAUC), mean day of onset (dDO) | — |
| Histological staining (H&E, luxol fast blue) | spinal cord sections from WT and APOBEC-1 KO EAE mice (sacrificed 21 dpi) | APOBEC-1 KO vs WT in EAE | percentage inflammatory cell infiltration (HE) and white matter demyelination (LFB) | ImageJ |
| Immunohistochemistry | spinal cord sections from WT and APOBEC-1 KO EAE mice | APOBEC-1 KO vs WT in EAE | gliosis (Iba1), astrocytosis (GFAP), T-cell infiltration (CD3) — stained area/percentage | ImageJ |
| Genotyping PCR | APOBEC-1 +/- C57BL/6 ear lobe gDNA | none | WT (600 bp) and APOBEC-1 KO (250 bp) fragments | — |
- ▼ RNA-editing events mediated by APOBEC and ADAR are significantly reduced throughout EAE disease course.
- ▲ EAE severity significantly higher in APOBEC-1 KO mice versus wild-type controls.
- ▲ APOBEC-1 KO mice show earlier clinical signs, higher microglia density, and increased gliosis than WT.
- – Mpeg1 (A-I) and B2m (C-U) targets predicted and validated to display differential editing and differential gene expression between naïve and acute EAE.
- count more than 2.8 million people worldwide affected by MS (MS prevalence (introduction))
- count WT n=9, APOBEC-1 KO n=11 mice (in vivo EAE cohort sizes)
- count naïve n=3, acute EAE n=3 (brain tissue used for RNA editing validations)
- other editing frequency threshold 0.1; read depth 10; base quality 25; bases supporting variation 3 (RNA editing calling thresholds)
- pvalue p ≤ 0.05 (FDR/Fisher exact); significance *p≤0.05, **p≤0.01, ***p≤0.001 (statistical thresholds)
- count 15-20 million reads per sample; 10 animals per group (GSE59725 RNA-seq microglia pools)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The study combined in silico RNA editing analysis of publicly available pooled-microglia RNA-seq data (REDItools and VarScan pipelines with FDR- and Fisher's-exact-test-based filtering) with experimental validations by Sanger sequencing of TA-cloned PCR products and qRT-PCR (2^-ΔΔCt) in small murine cohorts. An in vivo comparison of EAE clinical parameters (mean maximal score, area under the disease-score curve, day of onset) between wild-type (n=9) and APOBEC-1 KO (n=11) female mice was performed using GraphPad Prism v7.0/8.0. Results are reported as mean ± SE and significance is denoted by asterisk tiers rather than exact p-values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Fisher's exact test | RNA editing site identification in VarScan analysis of microglia RNA-seq data | 15–20 million reads per sample; microglia pooled from 10 animals per group | not stated |
| FDR-based filtering (p-value threshold 0.05) | RNA editing site identification in REDItools analysis of microglia RNA-seq data | 15–20 million reads per sample; microglia pooled from 10 animals per group | not stated |
| Statistical test not named | Comparison of mean maximal score (mMS), mean AUC (mAUC), and day of disease onset (dDO) between WT and APOBEC-1 KO EAE mice | n=9 (WT), n=11 (APOBEC-1 KO) | not stated |
| 2^-ΔΔCt method (qRT-PCR quantification; inferential test not named) | Differential expression of Mpeg1 and B2m between naïve and acute EAE microglia | null | not stated |
| Enrichr p-value-based enrichment scoring | Pathway analysis of RNA-edited transcripts across naïve, pre-clinical, and acute disease conditions | null | not stated |
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Dispersion is reported as mean ± standard error (SE) throughout↳ Could also: Standard deviation (SD) or 95% confidence intervals could also be used to summarize spread — For small biological n (e.g., n=3 or n=9–11), SD directly reflects sample variability; 95% CIs communicate estimation uncertainty and facilitate cross-study comparison — both are commonly favored over SEM in preclinical studies where individual biological variability is of primary interest
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The specific inferential test applied to EAE clinical parameters (mMS, mAUC, dDO) is not named↳ Could also: With n=9–11 per group, a two-tailed Student's t-test (parametric) or Mann-Whitney U test (non-parametric) would both be standard choices; a log-rank (Kaplan–Meier) test is a well-accepted alternative specifically for time-to-onset (day of disease onset) data — Naming the test and reporting whether normality was assessed enables readers to evaluate analytical appropriateness and reproduce the analysis; EAE scores are ordinal, which can motivate non-parametric approaches
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RNA-seq data used microglia pooled from 10 animals per condition, yielding one sample per condition with no within-group variance estimate↳ Could also: Individual per-animal RNA-seq libraries (≥3 biological replicates per condition) would also be a standard design, enabling variance estimation and formal differential editing/expression analysis with established frameworks such as DESeq2 or edgeR — Pooled libraries preclude estimation of biological variance; individual-animal libraries permit power calculations, formal statistical testing, and detection of outlier animals, which are standard requirements for RNA-seq differential analysis
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Statistical significance is communicated exclusively through asterisk tiers rather than exact p-values↳ Could also: Reporting exact p-values alongside effect size estimates (e.g., Cohen's d, fold-change with 95% CI) would also be standard practice — Exact p-values and effect sizes allow readers to judge the magnitude of differences independently of threshold choice and are increasingly required by journals; they also facilitate meta-analysis and replication assessments
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qRT-PCR normalization used a single reference gene (Gapdh)↳ Could also: Normalization to the geometric mean of two or more validated reference genes (e.g., Gapdh + Actb or Hprt) would also be standard for qRT-PCR in inflammation models — Individual housekeeping genes can be differentially regulated under disease or inflammatory conditions; multi-gene normalization reduces systematic error introduced if a single reference gene is not stable across the experimental groups
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RNA editing frequency was quantified by Sanger sequencing of ≥30 TA-cloned PCR colonies per sample↳ Could also: Targeted amplicon deep sequencing (NGS of the PCR product) would also be a standard method for quantifying site-specific editing frequency — Deep sequencing provides several hundred- to thousand-fold greater depth per site compared to 30-clone Sanger sampling, substantially reducing binomial sampling noise for low-frequency editing events and enabling statistical uncertainty estimates around the frequency
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
Downstream reach in the literature
1 downstream papers · 1 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
- RNA Editing Alterations Define Disease Manifestation... 2022 · 5 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36429012 (RNA Editing in EAE microglia)
Paper: Dafou et al. 2022, Cells 11(22):3582. PMID 36429012 / PMC9688714. Code: https://github.com/athanadd/RNA-editing-in-EAE (master, last push 2017-09-10). Data: GEO GSE59725 (Lewis 2014 EAE RNA-seq of sorted CNS/blood myeloid cells).
What the pipeline produces (the in-silico results = IN SCOPE)
The paper's "RNA Editing Analysis" (Methods 2.1) reanalyses published RNA-seq for RNA-editing events using an in-house pipeline:
FastQC + trim_galore (adapter + 5'/3' trim, hexamer-bias) → tophat2 → mm10 (genes.gtf, transcriptome index) → samtools sort/index → REDItools (REDItoolBlatCorrection + REDItoolDenovo) → ANNOVAR (refGene + snp142 annotation/SNP removal) → custom R (strand correction; split ADAR A→I [A/G,T/C] vs APOBEC C→U [C/T,G/A]).
Thresholds (Methods 2.1): base quality 25, read depth 10, variant-supporting bases 3, editing frequency 0.1, p (FDR/Fisher) 0.05; multimappers + dbSNP142 SNPs excluded.
Samples used (microglia only; 1 pool of 10 mice per timepoint)
| condition | GEO sample | timepoint | SRR |
|---|---|---|---|
| naïve | GSM1444482 (Mi_00) | 0 | SRR1524294 |
| pre-clinical | GSM1444481 (Mi_08) | 8 | SRR1524293 |
| acute/clinical | GSM1444480 (Mi_14) | 14 | SRR1524292 |
| (Monocyte/macrophage samples in GSE59725 are NOT used by this paper.) | |||
| C57BL/6 mice → mm10 is the C57BL/6J reference, so dbSNP filtering removes only | |||
| a handful of positions (no strain divergence) — minor for these counts. |
Reproducible quantitative claims (Figure 1 Ring A, Tables S2–S4)
- Global editing sites: naïve 1225, pre-clinical 1134, acute 891 (7% and 27% reductions vs naïve).
- Naïve: ADAR A-I = 775, APOBEC C-U = 450 (~2× A-I over C-U).
- Per-enzyme reductions: pre-clinical 4% (A-I) / 13% (C-U); clinical 30% (A-I) / 23% (C-U).
- A-to-G (A-I) is the dominant of the 12 RDD types (Figure S2).
OUT OF SCOPE (wet-lab / external / hard-20%)
- 2.3–2.5: qPCR/Sanger validations, APOBEC-1 KO in-vivo EAE severity, IHC/staining — wet-lab, not pipeline-derived.
- Pathway analysis (2.2, Figure 2): Enrichr web tool, KEGG2019 — manual web step.
- Exact genomic-region distribution % (intronic/3'UTR/exonic…): requires ANNOVAR (license-gated download) + refGene/snp142 DBs → the hard ~20%, not attempted for exact %; the enzyme/site COUNTS do not need ANNOVAR.
- BLAT correction refinement (false-positive reduction) — optional refinement.
Reproduction strategy (P16-sanctioned tool substitution)
Legacy tools (tophat2 deprecated; REDItools v1 = Python2; ANNOVAR license-gated) are substituted by maintained equivalents applied to the SAME data with the SAME thresholds: trim_galore → HISAT2 (splice-aware, mm10/GRCm38) → samtools → REDItools2 de novo → threshold filter + ADAR/APOBEC classification by substitution type. Target = the editing-site COUNTS and the A-I>C-U + disease-progression-reduction trend (Figure 1 Ring A). Exact ANNOVAR genomic-% not attempted.
Assessments & scoring basis
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An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
On the same GSE59725 microglia data, every biological/structural conclusion reproduces 1:1 (A-to-I dominance ~2.6x, A>G top RDD, monotonic editing decline with disease, APOBEC-early/ADAR-late loss; pre-clinical reduction 6.8% vs paper's 7%). The only substantive deviation is in the absolute high-confidence site counts, which run ~5x high (naive 6191 vs 1225) — this lies on our methodology side: we deliberately skipped the paper's two removal-only FP-pruning steps (ANNOVAR dbSNP142 + REDItools BLAT) and used a lenient binomial FDR rather than REDItoolDenovo's stricter test, because that legacy stack is under-specified/license-gated. The paper's smaller numbers are a plausible high-confidence subset of our signal, so this is a faithful partial reproduction with no fabrication concern.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
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