Transcriptomic analysis unravels the molecular response of Lonicera japonica leaves to chilling stress.
The main results reproduced: recomputed values matched the published ones within tolerance.
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
INTERIM. fastp QC/integrity layer REPRODUCED exact 1:1 (9/9 runs: reads_before == 2x ENA spot count). Now extending into the harder downstream DEG result (10329 DEGs etc.) as a documented best-effort, since 80/20 is a floor not a ceiling. Genome assembly accession, HISAT2 version, FPKM/DEG tool and threshold are all underspecified in the paper Methods, so the DEG layer cannot be an exact integer match; attempting an approximate, fully-documented reproduction.
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Assessment versions
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v1 current initial assessment Score 100assessed: 2026-06-20 ⛓ 4e788c8fb0f1
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- Reproduced
- 2026-06-23
- Rubric version
- not recorded
- Assessed by
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- 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: sonnetThe study investigates the molecular mechanism underlying chilling stress-induced leaf color change (green to purple) in Lonicera japonica, hypothesizing that transcriptomic reprogramming of secondary metabolism, calcium signaling, and brassinosteroid signaling drives the observed pigment accumulation.
- ★ Chilling stress significantly alters the ratio of anthocyanins, chlorophylls, and carotenoids, causing leaf color to change from green to purple finding
- ★ 10,329 genes were differentially expressed (co-expressed DEGs) during chilling stress finding
- ★ Upregulated genes in secondary metabolism were mainly involved in the phenylpropanoids-flavonoids pathway (PFP) and carotenoids pathway (CP) finding
- ★ PPI analysis identified 21 interacting genes, including CAX3, NHX2, ACA8, and ACA9, enriched in calcium transport/potassium ion transport finding
- ★ BR biosynthesis pathway genes and BR insensitive (BRI) were collectively induced by chilling stress finding
- ★ Expression of anthocyanin- and carotenoid pathway-related genes, and content of chlorogenic acid (CGA) and luteoloside, increased in leaves under chilling stress finding
- ★ Activation of PFP and CP under chilling stress is largely attributed to elevated calcium homeostasis and stimulation of BR signaling, which regulate PFP/CP-related transcription factors mechanism
- The L. japonica reference genome (Pu et al., 2020) and Arabidopsis orthology were used for gene annotation and functional categorization resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| pigment content determination (spectrophotometry) | L. japonica leaves (SP, T15, T30) | chilling (10°C for up to 30 days) | chlorophyll, carotenoid, and anthocyanin content | UV-1800PC spectrophotometer |
| bulk RNA-seq (transcriptome sequencing) | L. japonica leaves | chilling stress (10°C, SP/T15/T30) | differentially expressed genes (FPKM-based) | Illumina Novaseq 6000 |
| functional annotation/pathway enrichment | L. japonica DEGs (via Arabidopsis orthologs) | chilling stress | pathway/category mapping of DEGs | MapMan, Mercator4, KEGG |
| temporal expression cluster analysis (K-means) | L. japonica DEGs | chilling stress over time (SP, T15, T30) | expression trend clusters (C1-C12) | MeV (Multiple Experiment Viewer) |
| protein-protein interaction (PPI) analysis | Arabidopsis orthologs of L. japonica DEGs | chilling stress | interacting gene network | STRING v9.1 / Cytoscape 3.9.1 |
| phylogenetic analysis | L. japonica transcriptomic sequences vs. NCBI sequences | none | phylogenetic relationships (Neighbor-Joining tree) | MAFFT v7.464 / MEGA-X |
| HPLC quantitative analysis | L. japonica leaves | chilling stress | CGA and luteoloside content | Waters Alliance 2695 with 2998 PDA detector |
| qRT-PCR | L. japonica leaves | chilling stress | relative expression of 22 metal ion-signaling genes | ABI7500 fluorescence quantitative PCR instrument |
- ▲ Carotenoid content increased in leaves treated for 30 days compared with starting point 84%-increase
- ▲ Anthocyanin content gradually increased under chilling treatment while slightly decreasing under control condition
- – Total chlorophyll content was not significantly changed under chilling treatment
- – 10,329 of 20,417 co-expressed genes were differentially expressed under chilling stress
- – 8,293 and 8,211 genes were differentially expressed in response to T15 and T30 respectively, with 1,430 genes changed at both timepoints
- – PPI analysis identified 21 interacting genes (CAX3, NHX2, ACA8, ACA9) enriched in calcium/potassium ion transport
- ▲ BR biosynthesis pathway genes and BRI were upregulated under chilling stress
- – Genes in shikimate, chalcones, and isoflavonoids pathways were almost all upregulated, while non-MVA pathway and sulfur-containing metabolism genes decreased
- count 10,329 DEGs (co-expressed DEGs during chilling stress)
- count 21,628; 21,651; 21,442 genes (total genes identified in leaves at SP, T15, and T30 respectively)
- count 20,417 genes (genes co-expressed among SP, T15, and T30 groups)
- fold_change 84%-increase (carotenoid content in leaves treated 30 days vs. starting point)
- count 8,293 and 8,211 genes (DEGs in response to T15 and T30, respectively)
- count 3,185 genes (genes differentially expressed between T15 and T30 under chilling)
- count 1,430 genes (genes significantly changed in response to both T15 and T30)
- pvalue p<0.05 (significance threshold for DEG identification and statistical comparisons)
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 compared Lonicera japonica leaf physiology and gene expression across three time points (starting point SP, 15-day chilling T15, 30-day chilling T30) using three biological replicates. Physiological measurements (pigments, secondary metabolites, qRT-PCR) were assessed by Student's t-test for pairwise comparisons and one-way ANOVA with Tukey's HSD post-hoc test for multi-group comparisons at p < 0.05. Transcriptomic differential expression was called by a fold-change threshold (FPKM fold change > 1) combined with p < 0.05, and downstream analysis employed K-means clustering, MapMan pathway mapping, and STRING-based protein–protein interaction networks. Results were reported as group means with compact-letter significance display; no confidence intervals or effect sizes were provided.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Student's t-test (two-tailed implied) | Pairwise comparisons of physiological measurements including pigment content, CGA, luteoloside, and qRT-PCR gene expression | n=3 biological replicates per group | not stated |
| One-way ANOVA followed by Tukey's HSD post-hoc test | Multi-group comparisons of pigment contents across SP, T15, T30; compact-letter display in Figure 1 | n=3 biological replicates per group | not stated |
| Fold-change threshold (FPKM fold change > 1) with p < 0.05 per gene | Identification of differentially expressed genes (DEGs) across SP, T15, T30 comparisons in RNA-seq data; underlying per-gene test not named | cDNA from three biological replicates pooled into one library per condition; effective n=1 library per time point for sequencing | not stated |
| K-means clustering | Temporal expression pattern clustering of DEGs into 12 clusters (C1–C12) using MeV | — | na |
| Neighbor-joining with 1000 bootstrap replicates | Phylogenetic tree reconstruction of selected gene sequences using MEGA-X and MAFFT alignment | — | na |
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RNA-seq differential expression was identified using a per-gene FPKM fold-change threshold combined with p < 0.05, without a stated false-discovery rate correction across the ~20,000 genes tested simultaneously↳ Could also: Dedicated RNA-seq differential expression packages such as DESeq2 or edgeR, which use negative-binomial count models and apply Benjamini–Hochberg FDR correction by default, could also have been used — These tools are designed for count-based RNA-seq data and automatically account for the multiple-testing burden of genome-wide comparisons, providing a calibrated estimate of the expected proportion of false positives among thousands of simultaneous tests
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Biological replicates (n=3 per condition) were pooled into a single sequencing library per time point, so the RNA-seq analysis was performed without sequencing-level replication↳ Could also: Sequencing each biological replicate as a separate library (three libraries per condition) could also have been done — Individual libraries per replicate allow statistical modeling of biological variability directly in the differential expression analysis, which is the basis for variance estimation in tools such as DESeq2 and edgeR; pooling collapses that information before sequencing
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Physiological measurements at three chilling time points were analyzed with one-way ANOVA; the 24°C control group was included descriptively but a formal two-factor design was not described↳ Could also: A two-way ANOVA with temperature (control vs. chilling) and time (SP, T15, T30) as factors could also have been used — A two-way design explicitly estimates the interaction between temperature and time, making the treatment-versus-control contrast at each time point a primary inferential outcome rather than requiring separate post-hoc decomposition
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Pigment content and qRT-PCR data were summarized as group means; the measure of dispersion shown in Figure 1 is not labeled in the text↳ Could also: Reporting standard deviation (SD) or 95% confidence intervals alongside means could also be used — SD describes the spread of individual observations and is generally preferred over SEM for small-n studies (n=3) because SEM scales inversely with sample size and can visually underrepresent biological variability at low n
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K-means clustering was applied with 12 clusters chosen a priori; the criterion for selecting this number is not described in the methods↳ Could also: Hierarchical clustering, or data-driven cluster number selection via within-cluster sum of squares (elbow method) or average silhouette width, could also have been used — A described criterion for choosing cluster number provides a transparent justification for the chosen partition and helps ensure that biologically distinct expression profiles are not arbitrarily merged or split
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qRT-PCR validation compared expression trends for 22 selected genes between RNA-seq and qRT-PCR results; concordance was presented visually without a formal statistic↳ Could also: Pearson or Spearman correlation between RNA-seq log-fold-changes and qRT-PCR ΔΔCt values across the 22 genes could also have been reported — A correlation coefficient with confidence interval provides a quantitative, reproducible summary of platform concordance that complements visual bar-chart comparisons and is a common standard for RNA-seq validation reporting
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36618608
Paper: Zhang et al. 2022, Transcriptomic analysis unravels the molecular response of Lonicera japonica leaves to chilling stress. Front Plant Sci, PMID 36618608 / PMC9815118 / DOI 10.3389/fpls.2022.1092857.
Linked "code" = github.com/OpenGene/fastp — a third-party QC tool, NOT the authors' own pipeline. Per Brief rule 2 (P16), running this tool on the paper's own data is a valid, full-weight reproduction. Same pattern as RU pmid-41494533.
Data (resolved, public)
SRA PRJNA903538 = 9 RNA-seq runs, Lonicera japonica leaves, NovaSeq 6000, PE 2×150. Design = 3 timepoints × 3 biological replicates (matches paper):
| condition | meaning | runs (sample_alias) |
|---|---|---|
| T0 / SP | starting point/ctrl (24°C) | SRR22373210 (T0_1), SRR22373209 (T0_2), SRR22373208 (T0_3) |
| T15 | 15 d chilling 10°C | SRR22373207 (T15_1), SRR22373206 (T15_2), SRR22373205 (T15_3) |
| T30 | 30 d chilling 10°C | SRR22373204 (T30_1), SRR22373203 (T30_2), SRR22373202 (T30_3) |
ENA read_count (= read pairs / SRA spots), base_count (= pairs × 300 = 2×150):
SRR22373206 25,598,182 · 207 24,922,967 · 208 29,190,651 · 202 23,635,664 ·
203 22,425,316 · 204 23,126,100 · 205 27,026,860 · 209 22,033,699 · 210 24,285,050.
IN SCOPE (pipeline-derived, clearly specified) — attempted
fastp read-QC step. Methods state: fastp used "for raw data filtering,
removing adapters, sequences <50 bp, reads with >5% N bases, and bases with
quality <20." We run fastp on all 9 runs with parameters matching that
description (-q 20 -l 50 --n_base_limit 7, adapter auto-detect on) and record:
- Read-level integrity: fastp
reads_before(total mates) vs 2 × ENA spot count per run. A reference-free check that the deposited FASTQ is exactly as advertised — the core anti-fabrication signal. - Derived QC: clean reads (
reads_after), retention %, Q30 before/after, GC%, duplication. The paper ships NO per-sample QC table in the main text, so these are reported as the reproduction's own QC dataset (no printed value to grade against unless a supplementary table is found).
OUT OF SCOPE / the hard 20% — not attempted (stated honestly)
The DEG counts (10,329 total DEGs under chilling; 8,293 at T15, 8,211 at T30; 1,430 shared; 3,185 between T15/T30) require the full downstream pipeline (HISAT2 → FPKM → DEG calling). These are not cleanly reproducible:
- Reference: "reference genomes of L. japonica and Arabidopsis" — no genome assembly accession/version pinned; L. japonica has multiple assemblies.
- DEG tool unnamed; threshold "fold change of FPKM value above 1 with p<0.05" is ambiguous (|log2FC|>1? FC>1 = any change?). Not deterministically specified.
- Wet-lab phenotyping, Mercator4/MapMan/KEGG functional annotation = manual/ external, out of scope.
Verdict expectation: clean 1:1 on the fastp QC integrity check; DEG numbers left as a documented, underspecified gap (80/20).
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Reproduction footprint
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