Clinical and molecular correlation defines activity of physiological pathways in life-sustaining kidney xenotransplantation.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values were directly comparable
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡The deviation was non-trivial in magnitude
- 🟡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, described well enough to reproduce. The authors' published R code (github.com/egenesis/Xenokidney-Physiology-Nature-Communications, single Rmd) runs a deterministic DESeq2 bulk DE (biopsy vs contralateral) starting from a DESeqDataSet that GEO ships as GSE210556_kidney_rnaseq.RDS (6.8 MB; the code's local 'nhp_timeseries_dds.RDS'). I re-ran that exact pipeline on «our HPC» (DESeq2 1.50.2/apeglm 1.32.0; paper used 1.36.0) on the 27-sample analysis set. RESULT: every reported gene reproduces in DIRECTION and SIGNIFICANCE; CASR (1.26 vs 1.3) and CALB1 (2.30 vs 2.3) are quantitatively exact; AGT/REN/CLDN14/CYP27B1 match in sign and order of magnitude (LFC differs 0.4-0.85). DEG count 1868 vs reported 1742 (+7.2%; up 954 vs 847, down 914 vs 895). NOT a 1:1 byte match because (a) version gap DESeq2 1.50 vs 1.36, and (b) three QC-excluded contralateral samples (pigs 1501/1502/21450) are ABSENT from the GEO-deposited object, so the exact n could not be matched. AUDITABLE FINDING: the paper Methods describe a pig-ID covariate model, but the shipped code uses ~sample_source only, and the ~sample_source model reproduces the reported numbers MUCH better (CALB1 2.30 exact vs covariate-model 1.32) -> reported values came from the published code, not the Methods-text model. No fabrication signal: shipped data+code regenerate the reported effects. NOT ATTEMPTED (out of scope / hard-20%): pathfindR KEGG/GO/Reactome enrichment (Fig 1e/1f, stochastic active-subnetwork search over unpinned Biogrid PIN + KEGG version); scRNA-seq heatmaps (Fig 3/4, start from a separate pre-computed SCE RDS not in GSE210556); PCA % (no numeric value in text); Salmon re-quant from RAW.tar (944 MB, redundant - DDS already encodes counts). Grades are provisional machine judgments for a human auditor.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
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.
-
v1 current initial assessment Score 69assessed: 2026-06-15 ⛓ eaecbd15b385
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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: opusCan porcine kidney xenografts from gene-edited minipigs faithfully recapitulate renal endocrine functions—xenograft growth, renin-angiotensin-aldosterone-system (RAAS) participation, and calcium-vitamin D-PTH/electrolyte regulation—after life-sustaining transplantation into non-human primates?
- ★ Porcine kidney xenografts transplanted into NHPs show only modest growth over time. finding
- ★ Porcine xenografts do not substantially contribute to recipient (NHP) RAAS pathway activity; porcine-derived renin does not efficiently initiate NHP RAAS. finding
- ★ PTH-independent hypercalcemia and hypophosphatemia are common after kidney xenotransplantation. finding
- ★ The recipient calcium-vitamin D-PTH axis and bone are not the sources of dysregulated calcium and phosphorus (PTH is appropriately suppressed in response to hypercalcemia). mechanism
- Normal or low urine calcium in the context of hypercalcemia indicates renal retention of calcium by the xenograft. finding
- Combined clinical chemistry, hormone assays, ultrasonography, and porcine-specific RNA-seq can define xenograft endocrine pathway activity to inform clinical trial design. method
- RNA-seq shows upregulation of calcium-handling genes (CLDN14, CaSR, CALB1) in xenograft biopsies versus contralateral untransplanted kidney. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (porcine transcripts only; DESeq2 + pathfindR pathway analysis) | Yucatan minipig kidney xenografts in cynomolgus macaques (biopsy, necropsy, contralateral untransplanted kidney) | kidney xenotransplantation (gene-edited porcine donor) | differential gene expression / enriched pathways | — |
| serial ultrasonography | cynomolgus macaque kidney xenograft recipients (n=17) | kidney xenotransplantation | xenograft long-axis length (sagittal plane), absolute and relative change over time | — |
| plasma renin activity assay (in vitro generated Angiotensin I) | NHP (cynomolgus macaque) xenotransplant recipients | kidney xenotransplantation (pre vs post) | Angiotensin I generated (pg/mL/6 h) | — |
| aldosterone assay | NHP xenotransplant recipients | kidney xenotransplantation (pre vs post) | serum aldosterone level | — |
| clinical chemistry (serum/urine electrolytes and creatinine) | cynomolgus macaque xenotransplant recipients (n=17) | kidney xenotransplantation | serum creatinine, calcium, phosphorus, sodium, potassium; urinary calcium/phosphorus normalized to creatinine | — |
| hormone immunoassays (calcifediol, calcitriol, PTH, PTHrP) | NHP xenotransplant recipients (n=6) | kidney xenotransplantation (pre vs post) | serum calcifediol, calcitriol, PTH, PTHrP levels | — |
| beta-C-terminal-telopeptide (CTx) assay | NHP xenotransplant recipients | kidney xenotransplantation (pre vs post) | bone resorption marker CTx | — |
| histopathology | parathyroid and thyroid tissue from single long-term survivor (M2519, 511 days) | kidney xenotransplantation | tissue abnormality (hypo/hyperplasia) | — |
- ▲ Xenograft size increased modestly but significantly over time (absolute and relative measures). 0.04 cm per month (absolute); 0.7% per month (relative)
- ▼ Plasma renin activity decreased markedly after xenotransplantation versus pre-transplant. ~7.1% of pre-transplant level by 30-40 days PTT; ~4.3% by 80-90 days PTT
- ▲ AGT and REN were among the most upregulated porcine transcripts, suggesting reduced negative feedback inhibition. AGT LFC=2.5 (Padj<5E-7); REN LFC=4.5 (Padj<9E-18)
- ▲ Hypercalcemia developed in the majority of recipients by 30 days PTT. 82% (14/17) ≥1 elevated calcium; 35% (6/17) ≥1 severe (>14 mg/dL)
- ▼ Hypophosphatemia developed in most recipients by day 30 PTT. 88% (15/17) below normal; 47% (8/17) severe (<1 mg/dL)
- ▼ PTH was suppressed in all tested animals after transplant, an appropriate response to hypercalcemia; no PTHrP detected. 100% (6/6), P<0.001
- ▲ CLDN14 and other calcium-handling genes were upregulated in xenograft biopsies vs contralateral kidney. CLDN14 LFC=4.4 (Padj<4E-13); CALB1 LFC=2.3 (Padj<1E-4); CaSR LFC=1.3 (Padj<1E-4); TRPV5 LFC=0.2 (Padj=0.64)
- – Urine phosphorus to creatinine ratio increased significantly in the 0-30 day post-transplant period; urine calcium showed a non-significant trend toward decrease. urine phosphorus P<0.001; urine calcium P=0.70
- count 1742 porcine genes differentially expressed (847 LFC>0.5; 895 LFC<-0.5), Padj<0.05 (biopsy vs contralateral untransplanted kidney DEGs)
- fold_change REN LFC=4.5, Padj<9E-18 (renin among most upregulated transcripts)
- fold_change AGT LFC=2.5, Padj<5E-7 (angiotensinogen upregulated)
- fold_change CLDN14 LFC=4.4, Padj<4E-13 (claudin-14 dramatically increased (calcium reabsorption))
- count 82% (14/17) hypercalcemia; 35% (6/17) severe hypercalcemia >14 mg/dL (calcium dysregulation by 30 days PTT)
- count 88% (15/17) hypophosphatemia; 47% (8/17) severe <1 mg/dL (phosphorus below normal by day 30 PTT)
- pvalue P<0.001 (PTH suppression after transplant (100%, 6/6))
- other PRA ~7.1% of pre-transplant by 30-40 days; ~4.3% by 80-90 days (reduced plasma renin activity post-xenotransplant)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This preclinical study of pig-to-non-human-primate kidney xenotransplantation (17 recipients) combined longitudinal clinical chemistry, hormone/enzyme assays, serial ultrasonography, and kidney RNA-sequencing. Differential gene expression was assessed with DESeq2 (Wald test, Benjamini-Hochberg FDR), followed by pathway/network enrichment with pathfindR (hypergeometric test on FDR-adjusted DESeq2 results); longitudinal size and pre-vs-post hormone/electrolyte comparisons used fixed-effects/generalized linear models incorporating recipient ID, and trends were visualized with LOESS smoothing and 95% confidence bands. Results were largely reported as box-and-whisker plots (median/IQR) and longitudinal scatter with LOESS estimates, with selected p-values and log2 fold changes reported.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 Wald test (differential gene expression) | Fig 1d volcano plot, biopsy vs contralateral untransplanted kidney; gene-level LFC/p-values throughout (e.g., AGT, REN, CLDN14, CaSR, TRPV5, CALB1) | 9 contralateral, 16 biopsy, 4 necropsy samples (biologically independent); biopsy vs CUK comparison | not stated |
| pathfindR hypergeometric (enrichment) test | Fig 1e,f; Fig 3d,e; Fig 4e pathway/subnetwork enrichment | FDR-adjusted DESeq2 gene results as input | not stated |
| Fixed-effects model (linear) for longitudinal size | xenograft length over time, absolute (0.04 cm/month, P<0.001) and relative (0.7%/month, P<0.001); Supplementary Tables 4,5 | 17 biologically independent transplants | not stated |
| Generalized linear model with transplant bin (pre vs post) and recipient ID as factors | plasma renin activity (Fig 3b), aldosterone (Fig 3c), urinary calcium/creatinine (Fig 4c), urinary phosphorus/creatinine (Fig 4d) | n=7 (renin, Fig 3b), n=6 (aldosterone, Fig 3c), n=12 (urinary Ca and P, Fig 4c,d) | not stated |
| Comparison of pre- vs post-transplant PTH (P<0.001) | Fig 5d PTH suppression in 6/6 animals | 6 animals | not stated |
| LOESS non-parametric regression (descriptive smoothing, not a hypothesis test) | serum creatinine (Fig 1b), graft length (Fig 2b,c), serum calcium/phosphorus (Fig 4a,b) | 17 biologically independent transplants | na |
-
Pre- vs post-transplant hormone, renin, and electrolyte comparisons were analyzed with generalized linear / fixed-effects models using recipient ID as a factor.↳ Could also: Linear mixed-effects models with a random intercept (and possibly random slope) per animal could also be used. — A mixed-effects formulation explicitly models the within-animal correlation of repeated measures via random effects and would also handle unbalanced sampling, offering one transparent way to represent the repeated-measures structure.
-
Many clinical/hormone outcomes were summarized with box-and-whisker plots showing median and IQR.↳ Could also: Reporting accompanying summary statistics such as mean ± SD, or a 95% confidence interval for the group difference alongside the plots, would also be informative. — Adding an interval estimate for the effect itself conveys both the magnitude and the uncertainty of the pre-vs-post change, which complements the distributional picture given by the box plot, especially helpful at small n.
-
Multiplicity correction (Benjamini-Hochberg) was applied within the RNA-seq differential expression and pathway analyses.↳ Could also: A correction (e.g., Benjamini-Hochberg or Bonferroni) could also be applied across the several clinical/biochemical hypothesis tests (renin, aldosterone, calcium, phosphorus, PTH, urinary ratios). — Extending an explicit multiplicity framework to the panel of physiological comparisons would also bound the family-wise or false-discovery rate across that set of tests in the same way it is bounded for the gene-level analyses.
-
Longitudinal trends in size and chemistries were displayed using LOESS smoothing with 95% confidence bands.↳ Could also: A parametric or semi-parametric longitudinal model (e.g., generalized additive mixed model, or a spline term within a mixed model) could also describe the time trend. — A model-based time trend would also yield an estimated slope or curve with a formal confidence interval and a significance test, allowing the visual LOESS pattern to be tied directly to an inferential statement while still accommodating non-linearity.
-
Statistical assumptions for the tests were not explicitly stated in the text.↳ Could also: Reporting the assumed error/link family for the GLMs and any diagnostic checks (e.g., residual or distributional checks), or using a nonparametric alternative (e.g., Wilcoxon signed-rank for paired pre-vs-post), would also be an option. — Stating the modeling assumptions or pairing a robust nonparametric test would also help readers gauge how distributional features of small-n biomarker data relate to the reported p-values.
-
The xenograft cohort was compared to a separate allotransplant group (n=4) descriptively to contextualize calcium/phosphorus and renin findings.↳ Could also: A formal between-group model (e.g., group × time interaction in a mixed model) could also be used to compare xeno vs allo trajectories. — An interaction-based comparison would also provide a single estimate and confidence interval for the difference in trajectories between graft types, complementing the side-by-side descriptive contrast.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
-
Porcine kidney xenograft long-axis length increased modestly but significantly (~0.04 cm/month absolute, ~0.7%/month relative) over time in cynomolgus macaque recipients.imaging cynomolgus macaque up 2023×1papers★ This paper is the founder (earliest)
-
Serum calcium was elevated in 82% of cynomolgus macaque kidney xenograft recipients by 30 days post-transplant, with severe hypercalcemia in 35%.other cynomolgus macaque up 2023×1papers★ This paper is the founder (earliest)
-
Serum phosphorus was below normal in 88% of cynomolgus macaque kidney xenograft recipients by day 30 post-transplant, with severe hypophosphatemia in 47%.other cynomolgus macaque down 2023×1papers★ This paper is the founder (earliest)
-
Serum PTH was suppressed in 100% of tested cynomolgus macaque kidney xenograft recipients post-transplant, an appropriate hormonal response to hypercalcemia; no PTHrP was detected.other cynomolgus macaque down 2023×1papers★ This paper is the founder (earliest)
-
Plasma renin activity decreased to ~7% of pre-transplant levels by 30-40 days post-xenotransplantation, indicating functional porcine renin secretion suppressing host systemic renin.other cynomolgus macaque down 2023×1papers★ This paper is the founder (earliest)
-
Urine phosphorus-to-creatinine ratio increased significantly in cynomolgus macaque kidney xenograft recipients in the first 30 days post-transplant, indicating xenograft-driven renal phosphate wasting.other cynomolgus macaque up 2023×1papers★ This paper is the founder (earliest)
-
CLDN14 (LFC=4.4) and CALB1 (LFC=2.3) are upregulated in porcine kidney xenografts versus contralateral control kidneys, implicating active calcium reabsorption regulation.RNA-seq minipig kidney xenograft up 2023×1papers★ This paper is the founder (earliest)
-
Porcine REN (LFC=4.5) and AGT (LFC=2.5) are markedly upregulated in kidney xenografts versus contralateral control kidneys, indicating reduced negative feedback on the intrarenal renin-angiotensin system.RNA-seq minipig kidney xenograft up 2023×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37311769
Paper: Anand RP, Layer JV, Heja D, et al. (2023) Design and testing of a humanized porcine donor for xenotransplantation. / "Clinical and molecular correlation defines activity of physiological pathways in life-sustaining kidney xenotransplantation." Nat Commun 14:3266. PMID 37311769 · PMCID PMC10264453 · DOI 10.1038/s41467-023-38465-x.
Code: https://github.com/egenesis/Xenokidney-Physiology-Nature-Communications
— single file final physiology RNAseq code.Rmd, pinned commit
a0d45d944bf991f894e2f597b58f241c36c12e5a (branch main, pushed 2023-04-24, not archived, no license).
Data: GEO GSE210556. Ships:
GSE210556_kidney_rnaseq.RDS.gz(6.8 MB) — presumed = thenhp_timeseries_dds.RDSDESeqDataSet object the codereadRDS()s (sample names in the code's exclusion list match the GEO sample titles). This is the reproduction entry point.GSE210556_RAW.tar(944 MB) — per-sample Salmon*_Quants.tar.gz(upstream of the DDS).GSE210556_PL15S.{fa,gtf}.gz(tiny) — custom transgene/construct sequence + annotation.
Method (what the bulk pipeline does)
Bulk RNA-seq DE in R with DESeq2 (1.36.0) + apeglm LFC shrinkage:
readRDSa per-species list of DESeqDataSets; take[["Sscrofa"]](pig).- Drop 5 named samples (QC exclusions).
rlog(blind=TRUE); factorsample_source= {contralateral, biopsy, necropsy}, relevel reference = contralateral.- Published code:
design <- ~ sample_source;DESeq()(Wald);results(alpha=0.05, name="sample_source_biopsy_vs_contralateral"), and a second call withlfcThreshold=0.5;lfcShrink(coef=2, type="apeglm"). (Note: paper Methods text says design also includes pig-ID as covariate — the shipped code does NOT; we run the code as published and additionally test the covariate model to flag the discrepancy.) - Downstream: pathfindR (KEGG/GO/Reactome) active-subnetwork enrichment; volcano; PCA; per-gene boxplots; scRNA-seq heatmaps from a separate pre-computed SCE RDS.
In scope (pipeline-derived, deterministic — attempted)
| id | reported result | pipeline step | reproducible from public data? |
|---|---|---|---|
| C1 | 1742 DEGs (Padj<0.05, |LFC|>0.5): 847 up (LFC>0.5) + 895 down (LFC<−0.5), biopsy vs contralateral | results(lfcThreshold=0.5) on the shipped DDS |
YES — deterministic DESeq2 on the GEO RDS |
| C2 | Per-gene apeglm-shrunk LFC + Padj: AGT 2.5 (P<5E−7), REN 4.5 (P<9E−18), CLDN14 4.4 (P<4E−13), CaSR 1.3 (P<1E−4), CALB1 2.3 (P<1E−4), CYP27B1 −3.1 (P<2E−13), TRPV5 0.2 (P=0.64, ns) | lfcShrink(apeglm) LFC + results padj |
YES — deterministic; exact-value cross-check |
Out of scope / hard-20% (not attempted, or optional)
- pathfindR KEGG/GO/Reactome enrichment (Fig 1e/1f, "Renin secretion",
"Aldosterone-regulated sodium reabsorption", "Endocrine/…calcium reabsorption"):
stochastic active-subnetwork search (
iterations=30, random seeds) over a Biogrid PIN- KEGG gene-set version that are not pinned → not bit-reproducible; identity of top terms is qualitatively checkable only. Optional.
- PCA variance % (Fig 1c): no numeric value stated in text → no pinnable expected value.
- scRNA-seq heatmaps (Fig 3/4): start from a separate pre-computed SCE end-product RDS
(
physiology_manuscript_scrnaseq_v1.0.0.RDS) not in GSE210556 supplementary → visualization, no headline number. - Salmon re-quantification from RAW.tar (944 MB): the DDS already encodes counts; rebuilding it from Salmon quants is the redundant upstream 20%, not attempted.
Honest framing
The clearly-specified, deterministic core (C1 DEG counts, C2 named-gene effect sizes) is fully reproducible by re-running the published DESeq2 code on the GEO-shipped DDS object. The pathway-enrichment and single-cell visualizations are either stochastic/under-pinned or start from non-shipped end-product objects, and are
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.
This is a solid, explainable reproduction: re-running the authors' published ~sample_source DESeq2 code on the GEO-deposited DESeqDataSet reproduces every reported gene in direction and significance — CALB1 exact (2.30 vs 2.3), CASR near-exact (1.26 vs 1.3) — with no fabrication signal. Residual deviations (DEG count 1742→1868 = +7.2%, up-DEGs +12.6%, LFCs off 0.4–0.85 for REN/CLDN14/CYP27B1) sit on the input/technical side: the DESeq2 1.50 vs 1.36 version gap plus 3 QC-excluded contralateral samples absent from the deposited object, so the exact n couldn't be matched. One authors-side documentation defect is worth flagging — the Methods describe a pig-ID covariate model but the reported numbers actually come from the shipped ~sample_source code — but it does not undermine derivability or the central conclusion.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.