SMRT and Illumina RNA sequencing reveal novel insights into the heat stress response and crosstalk with leaf senescence in tall fescue.
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”.
- ✓Any deviation was negligible
- 🔴Could not use the authors’ exact input data
- 🔴Reported values were only indirectly 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 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. The publication's METHODS are described well enough to know what was run (SMRTlink 6.0 CCS -> ICE -> LoRDEC -> CD-HIT -> Cogent for the long-read assembly; HISAT/FPKM DEG for the Illumina half), and the named code (Magdoll/Cogent, a third-party tool, P16) resolves and was cloned (commit c44054b). The 1:1 check succeeds only for the RAW PacBio sequencing-output numbers: from the single deposited run SRR12277032 we counted 11,961,341 subreads (paper 11,961,314; +27, within-tol), mean length 1354.6 bp (paper 1355; within-tol), and total 16,202,559,191 bp (== ENA). EVERYTHING DERIVED is not reproducible from the deposit, for concrete data reasons: (1) SRA stripped the PacBio movie/ZMW read names, so subreads cannot be grouped per ZMW and CCS (286,049) cannot be regenerated -- ccs 6.4.0 also refuses non-PacBio BAMs and does not support RS II/P6-C4 chemistry; (2) the unigene pipeline's LoRDEC polishing needs the Illumina reads, which are absent; (3) all 12 Illumina mRNA-seq samples driving the DEG/FPKM/annotation/AS results are NOT deposited in PRJNA647166 (only the 1 PacBio run is present); (4) the assembled 62,443-unigene transcriptome is not deposited (no TSA). NOT a fabrication signal -- the two checkable paper numbers match within rounding; this is a data-deposition completeness gap (~1/13 of declared sequencing units present, plus lossy SRA conversion of the PacBio data). NOT ATTEMPTED beyond honest blockers: faithful CCS/FLNC/Cogent reconstruction and the entire Illumina DEG analysis (no input data exists in the deposit). Grades are provisional; a human reviewer signs off in AUDIT.md.
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v1 current initial assessment Score 30assessed: 2026-06-18 ⛓ 08126342c482
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- Reproduced
- 2026-06-18
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-18no 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: opusThis study aims to characterize the similarity and difference between short- and long-term heat stress responses and the relationship (crosstalk) between heat stress response and heat-induced leaf senescence in tall fescue at transcriptional and post-transcriptional levels, given the lack of a reference genome.
- ★ Short-term heat stress strongly and specifically activates Hsps, Hsfs, FKBPs, calcium signaling, glutathione S-transferase, photosynthesis, and phytohormone signaling genes. finding
- ★ Long-term heat stress shares most DEGs with senescence, including up-regulated chlorophyll catabolic, phytohormone synthesis/degradation, stress-related genes and NACs, and down-regulated photosynthesis genes, FKBPs and catalases. finding
- ★ Short-term HS stimulates gene responses and improves thermotolerance, whereas long-term HS is damaging and may accelerate leaf senescence. mechanism
- ★ FaHsfA2a reduces HS-induced cell membrane damage, while FaNAC029 and FaNAM-B1 increase the damage in transient tobacco overexpression assays. finding
- ★ Alternative splicing is widely observed in HS- and senescence-responsive genes including Hsps, Hsfs, and phytohormone signaling/synthesis genes. finding
- ★ Combined PacBio SMRT and Illumina sequencing generated a full-length reference transcriptome (62,443 unigenes) for tall fescue lacking a genome sequence. resource
- SMRT full-length sequencing enables accurate identification of alternative splicing isoforms in the absence of a reference genome. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Illumina RNA-seq (second-generation) | tall fescue 'Houndog 5' leaves (Con, HT_1h, HT_72h, Sen) | 38 °C heat treatment (1 h, 72 h) and natural senescence | differential gene/isoform expression (FPKM, DEGs, DEIs) | Illumina HiSeq |
| SMRT full-length cDNA (Iso-seq) sequencing | mixed RNA from 12 tall fescue 'Houndog 5' leaf samples | none (pooled reference) | full-length transcripts/unigenes and alternative splicing events | PacBio RSII |
| Electrolyte leakage (EL) measurement | tall fescue leaves | heat treatment (1 h, 72 h) and senescence | cell membrane damage | — |
| Chlorophyll fluorescence (Fv/Fm) measurement | tall fescue leaves | heat treatment (1 h, 72 h) and senescence | maximum quantum efficiency of PSII photochemistry | — |
| Transient overexpression assay | tobacco (Nicotiana) | overexpression of FaHsfA2a, FaNAC029, FaNAM-B1 | cell membrane damage under heat stress | — |
- – DEGs detected in HT_1h vs Con (4076), HT_72h vs Con (6917), and Sen vs Con (11,918) 4076 / 6917 / 11,918 DEGs
- – Most HT_72h DEGs (3835) overlap with senescence (V5), while most HT_1h DEGs (2237) are unique to HT_1h 55.44% (3835) and 54.88% (2237)
- ▲ Hsps strongly induced by HS, with higher expression under short-term than long-term heat treatment; Hsps = 43.6% of co-induced up-regulated DEGs 126/289 (43.60%)
- – Electrolyte leakage increased with heat treatment time and in senescent leaves; Fv/Fm significantly decreased after long-term HS and in senescence
- – FaHsfA2a reduced HS membrane damage; FaNAC029 and FaNAM-B1 increased it in tobacco
- – 298 AS events detected; RI most abundant followed by A3; DEASE = 42 (HT_1h), 65 (HT_72h), 43 (Sen) vs Con 298 AS events; 42/65/43 DEASE
- – 62,443 unigenes (avg 2595 bp, N90 1282 bp) identified; 55,075 (88.20%) annotated in at least one database 62,443 unigenes; 88.20% annotated
- ▲ After 1 h HS, FKBPs (six FKBP62, one FKBP65, 42 FKBP70s) and Ca2+ signaling genes specifically up-regulated
- count 4076; 6917; 11,918 DEGs (DEGs in HT_1h, HT_72h, Sen vs Con)
- count 62,443 unigenes (full-length unigenes identified by SMRT)
- count 318,932 polymerase reads; 235,889 FLNCs (PacBio sequencing output (PRJNA647166))
- fold_change |log2 Ratio| ≥ 1 (≥2-fold) (DEG threshold with p ≤ 0.05, FPKM ≥ 10)
- count 1626 DEGs and 193 DEIs with ≥32-fold change (HT_1h-specific up-regulated genes/isoforms)
- count 658 differentially expressed Hsp genes (30.95%) (Hsps among HT_1h-specific DEGs)
- count 3537 genes with AS generating 8312 isoforms (alternative splicing analysis via COGENT/SUPPA)
- pvalue P < 0.01 (**), P < 0.001 (***) (Student's t-test on EL and Fv/Fm, three biological repeats)
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 PacBio SMRT and Illumina sequencing to profile the transcriptome of tall fescue under control, short-term heat stress (1 h), long-term heat stress (72 h), and natural senescence conditions (n = 3 biological replicates per condition for physiological assays). Physiological endpoints (electrolyte leakage and Fv/Fm) were compared across conditions using Student's t-test with SD reported as dispersion. Differentially expressed genes were identified by a combined fold-change filter (|log2 ratio| ≥ 1) and uncorrected p-value cut-off (p ≤ 0.05), together with an FPKM ≥ 10 inclusion threshold. Alternative splicing events and differentially expressed isoforms were detected using COGENT and SUPPA; results were further interpreted via Venn diagrams, hierarchical clustering heatmaps, and KEGG pathway enrichment.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Student's t-test (two-group; directionality not specified) | Electrolyte leakage (EL) and Fv/Fm comparisons across Con, HT_1h, HT_72h, and Sen (Fig. 1) | 3 biological replicates per condition | not stated |
| Fold-change threshold plus p-value cut-off (|log2 ratio| ≥ 1, p ≤ 0.05, FPKM ≥ 10) — underlying statistical model for p-value not named in text | Identification of DEGs in HT_1h vs Con, HT_72h vs Con, and Sen vs Con (Fig. 4a–b) | not stated | not stated |
| Differentially expressed AS events (DEASE) via SUPPA — underlying statistical model not named in text | Differential alternative splicing events in HT_1h vs Con, HT_72h vs Con, and Sen vs Con (Fig. 3b) | not stated | not stated |
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Separate pairwise Student's t-tests were applied when comparing physiological measurements (EL, Fv/Fm) across four conditions (Con, HT_1h, HT_72h, Sen)↳ Could also: A one-way ANOVA followed by a post-hoc multiple-comparison procedure (e.g., Tukey HSD or Dunnett's test vs. control) could also be used for this design — A single omnibus ANOVA controls the family-wise error rate across all group comparisons simultaneously; Dunnett's test is specifically optimized for multiple treatments vs. one shared control, which matches this study's design
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DEGs were called using a raw p-value threshold (p ≤ 0.05) across thousands of genes simultaneously, without a stated multiple-testing correction↳ Could also: A false discovery rate (FDR) correction such as Benjamini-Hochberg could also be applied to the full set of tested transcript p-values — Transcriptome-wide testing involves tens of thousands of simultaneous comparisons; FDR correction quantifies the expected proportion of false discoveries among the reported DEGs and is standard practice in published RNA-seq differential expression workflows
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The underlying statistical model generating DEG p-values was not named; FPKM-normalized values were used with an unspecified test↳ Could also: Negative binomial count-based models implemented in DESeq2 or edgeR could also be used, operating on raw read counts rather than FPKM — Count-based models explicitly parameterize the overdispersion characteristic of RNA-seq data and provide well-calibrated p-values; FPKM normalization followed by tests designed for continuous data may underestimate variance in low-count genes
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Dispersion in physiological endpoint data was reported as SD from three biological replicates↳ Could also: The 95% confidence interval could also be reported as a complement or alternative to SD — With n = 3, a CI explicitly communicates the precision of the mean estimate and directly supports inferential interpretation; SD describes sample spread, while a CI addresses uncertainty about the population mean
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KEGG pathway enrichment was performed on DEG lists, but the enrichment test and any correction for multiple pathways tested were not described in the provided text↳ Could also: A hypergeometric test or Fisher's exact test with Benjamini-Hochberg FDR correction across all tested pathways, or alternatively gene set enrichment analysis (GSEA) on the full ranked gene list, could also be used — Reporting the enrichment method and its multiple-testing correction allows readers to assess the reliability of pathway findings; GSEA additionally avoids the need for an arbitrary fold-change threshold and uses the full gene ranking
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Sample size was stated as 'three biological repeats' for physiological assays with no prospective power analysis reported↳ Could also: A prospective power calculation referencing expected effect sizes from prior tall fescue or cool-season grass studies, or a sensitivity analysis, could also be included — Documenting the basis for sample size selection helps readers assess the study's sensitivity to detect the magnitudes of physiological change reported and is an increasingly common transparency expectation
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