Comprehensive Transcriptome Analysis Reveals Genome-Wide Changes Associated with Endoplasmic Reticulum (ER) Stress in Potato (Solanum tuberosum L.).
The main results reproduced, with only marginal, non-material deviations.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- 🟡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
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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
PARTIAL reproduction (honest 1:1 attempt, well-documented forced deviation). Potato ER-stress RNA-seq (tunicamycin TM vs Mock, 2h/5h, 3 reps = 12 libs, PRJNA865435), authors' own repo (HISAT2 2.2.1 -> htseq-count 2.0.1 intersection-strict -> edgeR exactTest). KEY DATA FINDING: the SRA deposit is DE-PAIRED single-end - paper states PE150 but each pair is stored as 2 single-end spots with an empty mate (sra-stat: 75.6M spots, 11.3Gbp = 37.8M pairs x2; fastq-dump --split-files yields only read-2). All bases present, pairing irrecoverable -> FORCED single-end alignment (htseq --stranded no; strand probe confirms de-paired pool is 50/50 sense/antisense). Reference Castle Russet v2.0 (SpudDB), authors' _clean GTF not shipped so GFF3->GTF via gffread. RESULTS: alignment reproduces cleanly (overall 94.06-94.73% vs >94%; 37-38M pairs-equiv reads). DEG counts in same magnitude AND same up/down asymmetry direction but ~20-40% higher (2h 248/370 vs 204/278; 5h 197/180 vs 157/129; total 966 vs 806) - attributable to single-end+stranded-no+converted-GTF. Cross-timepoint overlap reproduced closely (8/8 vs paper 9/10). NOT attempted: isoform-switching (optional). Also flagged: shipped edgeR script uses decideTestsDGE lfc=0 (FDR-only) not the |log2FC|>=1 the paper text states; we report both. Honest verdict: pipeline + reported magnitudes are reproducible; exact DEG counts are not, primarily due to a real SRA deposit defect (de-pairing) outside our control.
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Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 50assessed: 2026-06-19 ⛓ 865ef64a05c6
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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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetThe study tests whether treating potato (Solanum tuberosum) leaves with tunicamycin (TM) to induce ER stress reveals genome-wide transcriptional and post-transcriptional changes underlying the unfolded protein response (UPR), including responses not previously reported in Arabidopsis.
- ★ TM treatment of potato leaves produces widespread, time-dependent differential gene expression associated with ER stress and the UPR finding
- ★ Chromatin remodeling and transcriptional reprogramming (histone methyltransferases, SWI/SNF components, NAP proteins, histone acetyltransferase complex, DNA methylation/demethylation factors) occur as an early ER stress response finding
- ★ Limited genome-wide changes in alternative RNA splicing/isoform usage of protein-coding transcripts occur following TM treatment finding
- ★ RNA metabolism, translation machinery components, and protein folding/maturation factors are extensively affected by TM treatment, involving a broader set of genes than reported in Arabidopsis finding
- ★ Antioxidant defense and oxygen metabolic enzymes are differentially regulated, consistent with oxidative stress during ER stress finding
- Surges in protein kinase gene expression indicate early signal transduction events during ER stress finding
- Potato has an expanded set of ER stress-responsive StbZIP genes (StbZIP17/28/33/60/67/70/71) relative to Arabidopsis orthologs, suggesting unique stress-adaptation factors resource
- At least 39 differentially expressed transcripts contribute to structure/function of the ER, Golgi, endocytic, and vacuolar networks finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq (BGI platform) | Potato (Solanum tuberosum cv. Russet Norkotah) leaves | Tunicamycin (TM) treatment vs DMSO (solvent) control | Differentially expressed genes (DEGs) at 2 h and 5 h post-treatment, log2 fold change, adjusted p-value, FDR | BGI RNA-seq; reads aligned to Castle Russet potato genome |
| Gene ontology (GO) annotation and functional enrichment analysis | Potato leaf transcriptome DEG dataset (TM vs mock, 2 h and 5 h) | TM vs DMSO | GO term distribution and enrichment (Biological Process, Molecular Function, Cellular Component) via Fisher's exact test | BLAST2GO tool within OmicsBox |
| Alternative splicing / transcript isoform usage analysis | Potato leaf RNA-seq transcriptome | TM vs DMSO | Loci showing shifts in RNA isoform usage (alternative TSS/termination) at 2 h and 5 h | — |
- – TM treatment produced 806 unique DEGs total across 2 h and 5 h (fold change threshold 1.0, p<0.05) 806 genes
- – At 2 h: 204 uniquely upregulated and 278 uniquely downregulated genes 204 up / 278 down
- – At 5 h: 157 uniquely upregulated and 129 uniquely downregulated genes 157 up / 129 down
- – Overlap classes: 9 genes up at both 2&5h, 9 down-to-up (2h to 5h), 9 up-to-down (2h to 5h), 10 down at both timepoints 9/9/9/10 genes
- – RNA-seq reads aligned to Castle Russet potato genome with high overall alignment rate; majority uniquely mapped >94% overall alignment; 56-62% unique
- ▲ Enrichment for endomembrane system (ER, Golgi) increased from 2 h to 5 h post-TM treatment ER-associated sequences ~2-7%; Golgi elevated to 4% at 5h
- – 9 loci at 2 h and 15 loci at 5 h showed changes in RNA isoform usage, largely without overall change in gene expression 9 loci (2h), 15 loci (5h)
- – At least 28 factors in mRNA synthetic processes, 21 in rRNA processing/ribosome biogenesis, and 32 in tRNA modification/translation were differentially expressed 28/21/32 factors
- count 806 unique DEGs (Total DEGs across 2h and 5h TM treatment vs DMSO)
- pvalue p < 0.05 (Significance threshold for DEG calling, with fold change threshold of 1.0)
- count 37-38 million quality read pairs (RNA-seq sequencing depth per sample)
- other >94% overall alignment rate; 21.3-22.8 million (56-62%) uniquely aligned read pairs (RNA-seq alignment statistics to Castle Russet genome)
- other 14.1 million (39%) to 15.8 million (43%) read pairs returned multiple hits (RNA-seq multi-mapping reads)
- other 703,240 (1.9%) to 886,837 (2.4%) read pairs unaligned (RNA-seq unmapped reads)
- count 204 up / 278 down (2h); 157 up / 129 down (5h) (Uniquely regulated DEGs per timepoint)
- count 39 transcripts (Genes contributing to ER, Golgi, endocytic, and vacuolar network structure/function)
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 RNA-seq (BGI platform) to compare gene expression in tunicamycin (TM)-treated versus DMSO (mock)-treated potato leaves at 2 and 5 hours, identifying differentially expressed genes (DEGs) using a fold-change threshold of 1.0 and p < 0.05, with adjusted p-values and false discovery rate (FDR) reported in supplementary tables. Gene ontology enrichment of the DEG sets was performed using Blast2GO (within OmicsBox) with Fisher's exact test to assess enrichment of cellular component/biological process/molecular function categories. Results were visualized with volcano plots, Venn-style overlap counts, GO distribution charts, and word clouds; the provided text does not include a separate Materials and Methods statistics subsection detailing replicate numbers or the specific DE-calling algorithm.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential gene expression analysis (specific statistical model/tool not named in provided text, e.g., count-based RNA-seq DE test) | TM- vs DMSO-treated leaf transcriptomes at 2 h and 5 h (Figure 1A,B; Tables S2-S4) | not stated | not stated |
| Fisher's exact test (via Blast2GO enrichment analysis) | GO term/cellular component enrichment comparing TM-treated vs mock datasets (Figure 3A,C,D) | not stated | not stated |
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Differentially expressed genes were called using a fold-change threshold (1.0) combined with p < 0.05, without naming the underlying statistical model for the RNA-seq counts.↳ Could also: A count-based differential expression model such as DESeq2 (Wald or likelihood-ratio test) or edgeR (negative binomial exact test/GLM), explicitly reporting biological replicate number and dispersion estimation — Naming the statistical model and its assumptions (e.g., negative binomial mean-variance modeling) makes explicit how significance was derived from raw counts and allows others to reproduce or reanalyze the DEG calls.
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FDR correction is referenced for the DEG p-values, but the specific multiple-testing procedure is not stated in the provided text.↳ Could also: Explicitly specifying the correction method, such as the Benjamini-Hochberg procedure — Naming the exact procedure clarifies the family-wise scope of the correction (e.g., per-comparison vs. genome-wide) and lets readers directly compare thresholds across studies.
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GO enrichment of DEGs was assessed with Fisher's exact test via Blast2GO.↳ Could also: Gene-length-aware enrichment tools such as GOseq, or hierarchy-aware tools such as topGO (elim/weight algorithms) — These approaches can additionally account for transcript-length sequencing bias or the nested structure of GO terms, which can refine which categories appear most enriched.
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DEGs were defined using a fixed fold-change cutoff (1.0) together with a nominal p-value threshold (p < 0.05).↳ Could also: Ranking/selecting genes primarily by the adjusted p-value (FDR) with fold-change reported alongside, or using shrinkage-based effect-size estimates (e.g., DESeq2's apeglm/ashr shrinkage) — This can reduce reliance on an unadjusted p-value threshold for calling significance and can stabilize fold-change estimates for lower-count genes.
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Comparisons were made independently at 2 h and 5 h, with genes then categorized post hoc by their pattern across the two time points (Figure 1B).↳ Could also: A joint time-course differential expression framework (e.g., maSigPro, ImpulseDE2, or a time-by-treatment interaction term in a single model) — Modeling both time points jointly can directly test for time-by-treatment interaction and may increase power to detect temporally dynamic expression patterns.
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The text does not state the number of biological or technical replicates used for the RNA-seq comparisons.↳ Could also: Reporting the replicate number and, where feasible, a power/sample-size justification — Stating replicate numbers helps readers assess the precision of the fold-change and p-value estimates for the reported DEGs.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36430273
Paper: Herath V, Verchot J. "Comprehensive Transcriptome Analysis Reveals Genome-Wide Changes Associated with ER Stress in Potato (Solanum tuberosum L.)." Int J Mol Sci 2022. DOI 10.3390/ijms232213795. PMCID PMC9696714.
Design: Potato (cv. not relevant; ref = Castle Russet) treated with tunicamycin (TM, 5 µg/mL in DMSO) vs Mock (DMSO) to induce ER stress / UPR. Two timepoints (2 h, 5 h), 3 biological replicates each → 12 bulk RNA-seq libraries (BGISEQ-500, PE150, strand-specific RF). SRA: PRJNA865435 (SRR20760923–SRR20760934).
Repo: https://github.com/venuraherath/TM-Transcriptome-Potato — authors' own SLURM bash + R scripts (Grace_HISAT2.sh, Grace_HISAT2_SAMTools.sh, Grace_HTSeq_Count_Clean.sh, Grace_DE_edgeR_2hr.r / _5hr.R, Grace_IsoformSwitch_Analysis.R).
IN SCOPE (pipeline-derived, attempted)
| Result | Pipeline | Reproducible? |
|---|---|---|
| Alignment / mapping rates (>94% overall, 56–62% unique, 37–38M pairs) | HISAT2 2.2.1 --dta --rna-strandness RF → samtools sort |
YES |
| Per-gene counts | htseq-count 2.0.1 --mode intersection-strict --stranded reverse --minaqual 1 --type exon --idattr gene_id |
YES |
| DEG counts per timepoint (2 h 204↑/278↓, 5 h 157↑/129↓) | edgeR exactTest (classic, TMM + tagwise disp), BH FDR≤0.05, paper applies |log2FC|≥1 | YES (see threshold note) |
| Total unique DEGs (806) + overlap (9↑/10↓/18 opp) | set operations on DEG lists | YES |
THRESHOLD NOTE (important for honest comparison)
The shipped Grace_DE_edgeR_*.R calls decideTestsDGE(et, adjust.method="BH", p=.05)
with the default lfc=0 — i.e. the script's printed up/down counts are FDR-only,
not the |log2FC|≥1 the paper text states. To match the paper's 204/278/157/129
the logFC cutoff must be applied downstream. Our edgeR.R reports BOTH
(fdronly and fdr_lfc1) so the discrepancy is visible and auditable.
REFERENCE NOTE
Paper/repo annotation = cr.working_models.pm.locus_assign_clean.gtf (a "clean"
locus-assigned working-models GTF). The cleaned GTF is not shipped; we download
the public cr.working_models.pm.locus_assign.gff3 from SpudDB and convert with
gffread -T. Minor count differences from the unshipped cleaning step are possible.
OUT OF SCOPE (not attempted / harder)
- Isoform switching (9 loci 2 h / 15 loci 5 h): IsoformSwitchAnalyzeR + Kallisto + external CPC2/PFAM/SignalP5/IUPred2A — many external deps; OPTIONAL, attempt only after core DEG/alignment results land.
- GO/KEGG functional enrichment, heatmaps, wet-lab qRT-PCR validation — manual / external, not pipeline-deterministic.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.