Enhanced microRNA accumulation and gene silencing efficiency through optimized precursor base pairing.
The main results reproduced: recomputed values matched the published ones within tolerance.
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
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
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 1:1. This is a mostly wet-lab paper; the one clear pipeline-derived numeric result (Fig 5c processing accuracy = 90% of mature amiR-NbDXS from both shc and shc-A18G precursors) was reproduced END-TO-END from the paper's own raw SRA (SRR24210313 shc; SRR35183675 shc-A18G) using the documented pipeline (FASTX fastx_collapser + acarbonell/map_sRNA_reads exact-match mapping + the Methods' +/-4nt accuracy metric) on «our HPC»/«infra». Independent results: 89.89% and 89.68%, both rounding to the reported 90%; and the independently re-mapped mature-read counts (31410, 113922) match the authors' deposited Data S3 table EXACTLY -- strong no-fabrication evidence. Precursor refs were reconstructed from Data S3 and differ at exactly position 18 (A->G), validating the A18G claim. NOT attempted (80/20 + out of scope): P-SAMS amiRNA design for AtELF3 (Data S2; MySQL+BLAST web-backend) and all wet-lab quantities (Northern blots, RT-qPCR, chlorophyll, transgenic phenotypes). Brief corrections: code repo is carringtonlab/p-sams (brief's 'psams' 404s); new data accession is PRJNA1312446.
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Assessment versions
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v1 current initial assessment Score 87assessed: 2026-06-16 ⛓ 16b0e32c909e
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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
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: opusDoes base pairing at or near DCL1 cleavage sites within the minimal shc artificial miRNA (amiRNA) precursor affect amiRNA biogenesis and silencing efficiency, and can introducing a base pair upstream of the first cleavage site enhance amiRNA accumulation?
- ★ Introducing a G–C pair immediately upstream of the mature amiRNA (A18G substitution) markedly enhances amiRNA accumulation and gene silencing efficiency in shc precursors. finding
- ★ Eliminating the mismatch at the DCL1 first cleavage site of AtMIR390a (A18G) increases miR390a and amiRNA accumulation and silencing of NbSu and NbDXS. finding
- ★ Base pairing at the DCL1 second cleavage site (positions 40/51) has no significant effect on amiRNA accumulation. finding
- Base pairing at internal basal-stem positions 12/79 and 15/76 generally increases amiRNA accumulation. finding
- ★ A18G-modified shc precursors are accurately processed and predominantly release the intended authentic amiRNAs, as confirmed by deep sequencing. finding
- ★ A single structural modification in the amiRNA precursor provides an optimized, highly specific RNAi tool suited for functional genomics and crop engineering. resource
- Silencing sensor systems in N. benthamiana targeting NbSu and NbDXS enable functional screening of amiRNA precursor variants via bleaching phenotypes. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| sRNA/Northern blot | Nicotiana benthamiana leaves (transient agroinfiltration) | AtMIR390a A18G mutation vs wild-type; amiRNA precursor variants | miR390a / amiRNA (amiR-NbSu, amiR-NbDXS) accumulation | — |
| Quantitative RT-PCR (RT-qPCR) | Nicotiana benthamiana leaves (transient agroinfiltration) | amiRNAs expressed from wild-type vs modified precursors | NbSu and NbDXS target mRNA levels normalized to PP2A | — |
| Chlorophyll a quantification / phenotype imaging | Nicotiana benthamiana leaves (transient agroinfiltration) | amiR-NbSu / amiR-NbDXS vs GUS control | relative chlorophyll a content / bleaching phenotype | — |
| sRNA/Northern blot of shc precursor variants | Nicotiana benthamiana leaves (transient agroinfiltration) | all base-pair combinations at positions 18/73, 40/51, 12/79, 15/76 and combined mutants | amiR-NbSu and amiR-NbDXS accumulation | — |
| Phenotypic analysis in transgenic plants | Arabidopsis thaliana transgenic lines | amiRNAs against endogenous genes from A18G-modified vs wild-type shc precursors | visible/quantifiable silencing phenotype | — |
| High-throughput / deep sequencing of small RNAs | A18G-modified shc precursors (Arabidopsis/N. benthamiana) | A18G modification | precursor processing accuracy and amiRNA identity | — |
- ▲ miR390a accumulation increased from mismatch-corrected AtMIR390a-A18G precursor vs wild-type 26.5% increase
- ▲ amiR-NbSu and amiR-NbDXS accumulation higher from AtMIR390a-A18G precursor 19% (NbSu) and 133% (NbDXS) increase
- ▼ NbSu and NbDXS mRNA reduced more strongly with A18G-modified AtMIR390a precursor to 13.3% (NbSu) and 18.9% (NbDXS) vs 40.7%/44.9% for wild-type
- ▲ shc A18G/C73 variant increased amiR-NbSu accumulation vs wild-type 42.3% increase
- ▲ shc A18G, C73U, A18G/C73G variants increased amiR-NbDXS accumulation 137%, 126%, 85% higher respectively
- ▼ shc A18G/C73 precursor reduced target mRNA levels NbSu to 7.7% and NbDXS to 16.2% (vs 12.1%/25.7% wild-type)
- – Base pairing at DCL1 second cleavage site (40/51) had no significant effect on amiRNA accumulation
- ▲ Internal positions 12/79 and 15/76 base pairing increased amiRNA accumulation (e.g. A12G/C79U for NbSu; A12G/C79G for NbDXS) 54%/53% (NbSu); 102%/129% (NbDXS)
- fold_change 26.5% increase (miR390a accumulation, AtMIR390a-A18G vs wild-type)
- fold_change 133% increase (amiR-NbDXS accumulation from AtMIR390a-A18G precursor)
- mean 13.3% and 18.9% (NbSu/NbDXS mRNA remaining with A18G AtMIR390a precursor vs amiR-GUS control)
- fold_change 42.3% increase (amiR-NbSu from shc A18G/C73 variant)
- fold_change 137%, 126%, 85% increases (amiR-NbDXS from shc A18G, C73U, A18G/C73G variants)
- mean 7.7% and 16.2% (NbSu/NbDXS mRNA remaining with shc A18G/C73 precursor)
- fold_change 54% and 53% increases (amiR-NbSu from shc A12G or C79U variants (P<0.05))
- pvalue P < 0.05 (Student's t-test threshold for significance throughout)
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 used transient expression in Nicotiana benthamiana (n = 3 biological replicates) to test how base pairing modifications in amiRNA precursors affect miRNA accumulation (Northern/sRNA blot densitometry) and target mRNA levels (RT-qPCR). Multiple independent pairwise Student's t-tests were used to compare each precursor variant against a wild-type control at a P < 0.05 threshold. Results were reported as means with either SD or SEM and as percentage changes relative to a reference control; the provided text is truncated before results from Arabidopsis transgenic lines and deep-sequencing analyses are reached.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| pairwise Student's t-test | miR390a accumulation from sRNA Northern blot densitometry (Figure 1b) | n = 3 biological replicates | not stated |
| pairwise Student's t-test | relative chlorophyll a content across agroinfiltrated leaf sectors (Figure 1e) | n not explicitly stated for this figure | not stated |
| pairwise Student's t-test | amiRNA accumulation from sRNA blot densitometry, multiple precursor variants vs wild-type (Figures 1f, 2c, 3b, 3c, 3d) | n = 3 biological replicates | not stated |
| pairwise Student's t-test | RT-qPCR target mRNA levels (NbSu, NbDXS) for precursor variants vs control (Figures 1g, 2d) | n = 3 biological replicates | not stated |
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Multiple pairwise Student's t-tests were used to compare each of many precursor variants independently against a single wild-type control within the same experiment↳ Could also: One-way ANOVA followed by Dunnett's test (optimized for many-vs-one-control comparisons) or Tukey's HSD could also have been applied — An ANOVA framework with a post-hoc correction explicitly accounts for the inflation of Type I error that accumulates across many simultaneous pairwise comparisons, and Dunnett's test is specifically designed for the many-variants-vs-one-reference structure used here
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Dispersion was reported as SD for blot-densitometry data and as SEM for RT-qPCR data within the same paper↳ Could also: Consistent use of SD across all figures, or 95% confidence intervals throughout, could also have been applied — A uniform dispersion measure facilitates cross-figure comparison; 95% CIs additionally convey precision of the mean estimate, which is often considered informative for small-n data and is increasingly recommended by journals
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Statistical significance was reported only as a binary threshold (P < 0.05) with no exact p-values given↳ Could also: Reporting exact p-values (e.g., P = 0.018) alongside the threshold decision could also have been done — Exact p-values let readers assess the strength of evidence continuously rather than dichotomously, and they are required for inclusion in systematic reviews and meta-analyses
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Parametric Student's t-tests were applied to blot densitometry data from n = 3 replicates per group↳ Could also: A non-parametric alternative such as the Mann-Whitney U (Wilcoxon rank-sum) test could also have been used — With only three observations per group, the normality assumption of the t-test cannot be empirically verified; non-parametric rank-based tests make no distributional assumption and are a common alternative in small-n molecular biology experiments
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Effect magnitudes were expressed as percentage changes relative to a control (e.g., '42.3% increase in amiRNA abundance')↳ Could also: A standardized effect size metric such as Cohen's d could also have been reported alongside the percentage change — Standardized effect sizes are scale-independent and allow comparison of effect magnitudes across different assay types (blot vs. qPCR) and across studies, and they support prospective power calculations for follow-up experiments
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amiRNA accumulation was quantified by Northern/sRNA blot densitometry as the primary continuous readout↳ Could also: Small RNA sequencing read counts with a count-based statistical model (e.g., DESeq2 or edgeR negative binomial framework) could also have served as the primary quantitative comparison — Count-based models are well suited to the overdispersion characteristic of sequencing data and provide variance-stabilized estimates; the paper does describe deep-sequencing validation later, and integrating that as the primary quantitative endpoint is a standard alternative for sRNA studies
Citation network
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Data lineage
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41505763
Enhanced microRNA accumulation and gene silencing efficiency through optimized precursor base pairing. Llorens-Gámez et al., Plant J 2026. DOI 10.1111/tpj.70665.
This is a predominantly wet-lab paper (sensor systems in N. benthamiana, transgenic Arabidopsis, Northern blots, RT-qPCR, chlorophyll/phenotyping). Those results are OUT OF SCOPE (manual/experimental, not pipeline-derived).
Pipeline-derived results (IN SCOPE)
| # | result | pipeline | repro target |
|---|---|---|---|
| C1 | Processing accuracy = 90% of amiR-NbDXS from BOTH shc and shc-A18G precursors (Fig 5c) |
sRNA-seq → FASTX collapse → acarbonell/map_sRNA_reads (exact match to precursor +strand) → proportion of 19-24nt(+) reads within ±4nt of the amiRNA 5' end that are the exact 21-nt mature |
re-run pipeline on raw SRA (SRR24210313 shc, SRR35183675 shc-A18G) |
| C2 | 21-nt size class dominates; reads confined to amiRNA/amiRNA* region; sharp 5' end at position 0 (Fig 5a,b) | same pipeline | size distribution + top read = mature at pos 19 |
| C3 | amiR-NbDXS mature read counts/RPM (Data S3 deposited mapping) | same | exact read counts vs Data S3 |
| C4 | P-SAMS optimal amiRNA designs for AtELF3 (Data S2); amiR-AtELF3 = TTCGCCTTGACCTGATCCCTT (Optimal Result 2) |
carringtonlab/p-sams (P-SAMS, Perl) on Araport11/TAIR10.1 |
secondary; cross-check the chosen guide |
Out of scope (not attempted)
miRNA accumulation fold-changes from Northern blots, RT-qPCR target levels, chlorophyll/silencing phenotypes, Arabidopsis transgenic phenotyping — all wet-lab.
Notes / corrections
- Brief's code URL
github.com/carringtonlab/psamsis 404; correct repo iscarringtonlab/p-sams. The actual sRNA-mapping code isacarbonell/map_sRNA_reads(a self-contained exact-substring mapper; full source in its README). - Brief lists data
PRJNA957136(= Cisneros 2023 reference shc data); the new data for THIS paper is PRJNA1312446. Both used (one run from each). - Primary focus (80/20): C1 processing accuracy 90% — the single clearest pipeline-derived number, reproduced end-to-end from raw SRA.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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.
Strong, essentially 1:1 reproduction. The pipeline-derived result (Fig 5c, 90% processing accuracy of mature amiR-NbDXS from the shc and shc-A18G precursors) was re-derived end-to-end from the authors' own raw SRA and gave 89.89%/89.68% (rounds to 90%), while independently re-mapped mature-read counts matched the deposited Data S3 table exactly (31,410 and 113,922). The only deviation is sub-0.5% rounding — on our/technical side, negligible — and the exact count match is strong evidence against fabrication. The single un-attempted claim (C4, P-SAMS amiR-AtELF3 design via a MySQL+BLAST web backend) is secondary/optional and out of scope, so it does not weaken the core conclusion.
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Reproduction footprint
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