A transcriptomic atlas of Aedes aegypti reveals detailed functional organization of major body parts and gut regional specializations in sugar-fed and bl
The main results reproduced, with only marginal, non-material deviations.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓Any deviation was negligible
- 🟡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
Hixson et al. eLife 2022 e76132 - Aedes aegypti bulk RNA-seq tissue atlas (PMID 35471187, PRJNA789580). Cited code is FastQC (third-party QC tool); described pipeline = fastqc -> BBMap/BBDuk trim -> Salmon quant -> tx->gene TPM. PARTIAL REPRODUCTION, compute actually ran on «our HPC» («job», 55 Aedes runs, salmon 1.10.3 vs AaegL5.2 rel-68, mapping mean 73.9%). METADATA: C1 replicate/condition structure EXACT, C2a platform/layout EXACT, C2b read length within-tol (FastQC 76 bp vs 75 nominal). SEQUENCE-LEVEL: C3 gambicin highly-expressed-in-proventriculus reproduced (GAM1 = 30,929 TPM, rank #3/18,824, top annotated gene) but exact atlas >68k TPM not matched (~2.2x lower, version/release deviation) -> partial; C4 gambicin(gut)-vs-holotricin(carcass) mutual exclusivity reproduced clearly (gene id of holotricin provisional) -> partial; C5 AAEL013144/eIF3i more stable than markers but CV 0.558 -> partial. Dataset PRJNA789580 profiled grade A, delivers-promised yes (now incl. sequence-level QC). Honest partial: qualitative biological claims reproduced, exact atlas numbers differ due to documented salmon-version / transcriptome-release / normalization differences; no fabrication indicated. Not attempted: wet-lab, OrthoFinder, DESeq2 thresholds, atlas backend.
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v1 current initial assessment Score 57assessed: 2026-06-19 ⛓ 0fa26ba7722c
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- Reproduced
- 2026-06-21
- 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
- 2026-07-24
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Deep full-text extraction
Model: sonnetThe paper investigates the functional organization of Aedes aegypti body parts and gut regions, and how the gut transcriptome (particularly midgut regionalization and immune defense) changes with blood feeding, testing whether these digestive and immune specializations are conserved between Ae. aegypti and Anopheles gambiae.
- ★ Created Aegypti-Atlas, an online RNAseq resource covering Ae. aegypti body parts, gut regions, and a blood meal digestion time course resource
- ★ Anterior and posterior midgut possess distinct digestive specializations that are preserved in the blood-fed state finding
- ★ Blood feeding triggers sequential induction and repression/depletion of multiple cohorts of peptidases finding
- ★ Immune signaling components, but not recognition or effector molecules, are enriched in ovaries finding
- ★ Basal AMP expression is dominated by holotricin and gambicin, expressed mutually exclusively in carcass and digestive tissues respectively finding
- ★ Gambicin and other immune effectors are almost exclusively expressed in the anterior midgut, while the posterior midgut shows hallmarks of immune tolerance finding
- ★ Regional digestive and immune specializations of the midgut are conserved between Ae. aegypti and Anopheles gambiae finding
- ★ Ovaries make a disproportionately large contribution (28.8%) to the whole-body transcriptome despite having few genes enriched relative to whole body finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNAseq | dissected body parts (head, thorax, abdomen, gut, Malpighian tubules, ovaries) of sucrose-fed adult female Ae. aegypti, Thai strain | none | genome-wide gene expression (TPM), body-part enrichment vs whole body | — |
| RNAseq | five gut regions (crop/dorsal diverticula, proventriculus, anterior midgut, posterior midgut, hindgut) of adult female Ae. aegypti | none | regional gene expression, region-specific enrichment vs whole gut | — |
| RNAseq time course | anterior and posterior midgut of adult female Ae. aegypti | blood meal (6, 24, 48 hr post blood meal) | temporal changes in gene expression, especially peptidase transcripts | — |
| RT-qPCR | dissected body parts of adult female Ae. aegypti | none | relative expression of putative marker genes, validated against RNAseq | — |
| RNAseq (cross-species comparison) | midguts of Ae. aegypti and Anopheles gambiae (s.l.) | none | correlation of orthologous gene expression, conservation of regional specialization | — |
- ▲ Ovaries calculated to contribute 28.8% of the whole-body transcriptome, the largest of any body part 28.8%
- – Predicted whole-body expression (from scaling factors) correlated highly with observed whole-body expression after removing one outlier gene slope=1.00, R2=0.95
- – Posterior midgut transcriptome closely resembles whole-gut transcriptome, with few genes enriched relative to whole gut
- ▲ Three most prevalent gut transcripts (early trypsin, CHYMO, JHA15) together account for over one-third of the gut transcriptome >33%
- ▲ Ovaries expressed 391 putative tissue-specific marker genes, more than all other body parts combined 391 genes
- – Number of putative tissue markers ranged from 8 (abdomen) to 391 (ovaries) across body parts 8-391 genes
- ▲ Proventriculus enriched for wingless signaling and defense genes including the highly expressed AMP gambicin (GAM1)
- – RT-qPCR confirmed anatomical specificity of selected marker genes identified by RNAseq
- correlation slope=1.00, R2=0.95 (predicted vs observed whole-body gene expression from calculated body-part scaling factors)
- count 391 (putative ovary-specific marker genes (>=5 TPM, >=50-fold enriched))
- count 8 (putative abdomen-specific marker genes)
- other 28.8% (calculated ovary contribution to whole-body transcriptome)
- other 94.9% (sum of all calculated body-part transcriptome contribution percentages)
- pvalue padj<0.05 (DESeq2 significance threshold used for body-part/gut-region enrichment analysis)
- fold_change >=50-fold (threshold defining a putative tissue marker gene relative to all other body parts)
- fold_change 5x (2x for ovaries) (enrichment threshold vs whole body used to select genes for GO enrichment analysis)
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.
This RNAseq transcriptomic atlas of Aedes aegypti used Principal Component Analysis to visualize transcriptomic relationships among body parts and gut regions, and DESeq2 to identify differentially enriched genes between tissues (padj <0.05). Gene Ontology Enrichment Analysis was performed using the classic Fisher test via topGO on genes meeting defined fold-change thresholds (5x or 2x depending on tissue). RT-qPCR with one-way ANOVA and Tukey HSD post-hoc testing was used to validate putative tissue marker gene expression, and a linear regression was used to validate estimated body-part contributions to the whole-body transcriptome.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Principal Component Analysis (PCA) | Body part transcriptomes (Figure 1A) and gut region transcriptomes (Figure 2A) | 3–6 replicates per body part | not stated |
| DESeq2 differential expression with adjusted p-value threshold (padj <0.05; Benjamini-Hochberg FDR is DESeq2 default, not explicitly named in text) | Gene enrichment of body parts vs. whole body and gut regions vs. whole gut or aggregate of other regions | 3–6 biological replicates per group | not stated |
| Classic Fisher exact test via topGO (Gene Ontology Enrichment Analysis) | GO enrichment for genes enriched ≥5x (or ≥2x for ovaries) in each body part relative to whole body, and for gut regions | — | not stated |
| One-way ANOVA with Tukey HSD post-hoc | Transcriptome-wide z-scores for gene expression by body part (Figure 1—figure supplement 1B) and RT-qPCR validation of marker genes across body parts (Figure 1—figure supplement 2B) | 3 replicates (RT-qPCR); 3–6 replicates (z-score analysis) | not stated |
| Linear regression / Pearson correlation (R²) | Validation of calculated scaling factors predicting whole-body gene expression from body-part contributions (Figure 1—figure supplement 3A); reported as slope = 1.00, R² = 0.95 | — | not stated |
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Tissue-enriched genes were defined by a fixed fold-change threshold (5x or 2x) combined with DESeq2 padj <0.05↳ Could also: A continuous tissue-specificity index — such as the tau index, Jensen-Shannon divergence-based specificity, or the Z-score-based SPM — could also quantify enrichment across all tissues simultaneously without requiring a hard fold-change cutoff — Continuous specificity scores rank genes along a tissue-specificity continuum, avoid sensitivity to the chosen fold-change threshold, and allow tissues with very different transcriptome compositions (e.g., ovaries vs. gut) to be compared on a common scale
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GO Enrichment Analysis used the classic Fisher exact test via topGO applied to a fixed list of enriched genes↳ Could also: Gene Set Enrichment Analysis (GSEA) or fgsea using a ranked gene list (e.g., ranked by DESeq2 log2 fold-change or Wald statistic) could also be applied — Rank-based methods use the full expression distribution rather than a hard threshold, which can increase sensitivity and reduce dependence on the enrichment cutoff used to define the input gene list
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PCA was used to visualize transcriptomic similarity among body parts and gut regions↳ Could also: Hierarchical clustering with a heatmap of sample-to-sample distances, or non-linear methods such as UMAP, could also be used to visualize high-dimensional transcriptomic relationships — Hierarchical clustering makes pairwise distances explicit and quantitative; UMAP can reveal non-linear cluster structure not captured by the first two principal components, which may be informative when many tissues with divergent transcriptomes are compared
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One-way ANOVA with Tukey HSD was applied to transcriptome-wide z-scores and RT-qPCR data with 3–6 replicates per group↳ Could also: A non-parametric Kruskal-Wallis test followed by Dunn's post-hoc test (with FDR adjustment) could also be used when distributional assumptions of ANOVA have not been verified — With small group sizes (n = 3–6), departures from normality can influence ANOVA-based inference; non-parametric alternatives provide valid type-I error control without requiring the normality assumption
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Expression levels were summarized and compared using TPM (transcripts per million)↳ Could also: TMM-normalized counts (edgeR) or DESeq2 size-factor-normalized counts could also be used for cross-sample quantitative comparisons — TPM normalizes for transcript length and sequencing depth but does not account for differences in overall transcriptome composition across tissues; composition-aware normalization methods may be more appropriate when comparing tissues with very different transcriptome profiles, as is the case in a multi-tissue atlas
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No formal power analysis or sample size justification was reported; 3–6 biological replicates were used per tissue↳ Could also: A prospective power analysis using pilot variance estimates — for example via the RNASeqPower, ssizeRNA, or RnaSeqSampleSize R packages — could also be conducted to determine the replicate number needed to detect a given fold-change at a desired sensitivity and FDR — Reporting a power calculation helps readers assess the study's sensitivity to detect effects of biologically relevant magnitude, and is increasingly expected in RNAseq experimental designs, particularly for multi-tissue comparisons where variance can differ substantially across tissues
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