PID1 regulates insulin-dependent glucose uptake by controlling intracellular sorting of GLUT4-storage vesicles
Part of the results reproduced; minor but material deviations remained.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓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
▸Reproduction agent’s raw note
DROP (non_pipeline). 'PID1 regulates insulin-dependent glucose uptake by controlling intracellular sorting of GLUT4-storage vesicles' (Fischer AW, Albers K, Schlein C, Sass F, Krott LM, Schmale H, Gordts PLSM, Scheja L, Heeren J; BBA Mol Basis Dis 2019; PMID 30904610; PMC6624118; DOI 10.1016/j.bbadis.2019.03.010) is a wet-lab molecular/cell-biology paper. All Methods (§2.1-2.11) are bench experiments: Pid1-KO mice with GTT/ITT/plasma insulin, qPCR, SDS-PAGE/Western with LI-COR densitometry, immunoprecipitation, GST-pulldown, membrane fractionation, confocal immunofluorescence. The only computation is descriptive statistics (t-test / one-way ANOVA) in Microsoft Excel + GraphPad Prism 6 -- not a bioinformatic pipeline. No deposited dataset (Europe PMC/NCBI dbCrossReferenceList is null: no GEO/SRA/ENA/ArrayExpress/PRIDE/figshare/zenodo), no Data- or Code-availability statement, no code repository; raw values exist only inside figures. There is nothing pipeline-derived to recompute, so no «our HPC»/SLURM job was run (forcing one would fabricate activity). drop_reason=non_pipeline (secondary corroborating gaps: no_data_accession, no_code). NOT ATTEMPTED: any compute -- correctly, because no pipeline/data/code exists. Verdict is provisional and human-checkable; see scope.md, AUDIT.md, agreement.json. Note: paper was described well enough to classify confidently; the drop is about the absence of a computational artifact, not about poor description.
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 assessmentassessed: 2026-06-18 ⛓ f8ae90e0d2ad
✎ 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
- 2026-06-18
- Rubric version
- v1.0
- Assessed by
-
🤖 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: sonnetPID1, an LRP1 adaptor protein, has a fundamental role in systemic glucose homeostasis by regulating insulin-dependent sorting of GLUT4-storage vesicles via its control of LRP1 trafficking.
- ★ PID1 serves as an insulin-regulated retention adaptor protein controlling co-translocation of LRP1 and GLUT4 to the adipocyte plasma membrane mechanism
- ★ Loss of PID1 causes LRP1 and GLUT4 to sort to the plasma membrane independent of insulin stimulation in brown adipocytes finding
- ★ PID1-deficient mice on high fat diet show improved hyperglycemia, glucose tolerance, and reduced basal plasma insulin versus wild type finding
- ★ PID1 does not directly affect insulin receptor/AKT signaling in vitro or in vivo finding
- ★ PID1 binds LRP1 and co-precipitates LRP1, GLUT4 and AS160 from muscle, indicating PID1 interacts with GLUT4-storage vesicles via LRP1 mechanism
- Cold-induced BAT activation reduces PID1 and LRP1 expression while increasing GLUT4 expression, inversely correlating with glucose uptake finding
- ★ LRP1-deficiency in primary brown adipocytes mimics PID1-deficiency by increasing GLUT4 cell-surface abundance finding
- PID1-deficiency selectively affects GLUT4-storage vesicle components (GLUT4, IRAP, LRP1) but not the insulin-independent transporter GLUT1 finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| qRT-PCR gene expression | brown adipose tissue, wild type mice | ambient temperature acclimation (6, 22, 30°C) | mRNA levels of Pid1, Lrp1, Glut4 and thermogenic/lipid genes | Applied Biosystems assays-on-demand |
| Western blot / densitometry | brown adipose tissue, wild type mice | ambient temperature acclimation | PID1, LRP1, GLUT4 protein levels | Amersham Imager 600 / LICOR Image Studio Lite |
| Western blot (p-AKT/AKT) | primary brown adipocytes, WT vs Pid1-/- mice | insulin stimulation | AKT phosphorylation | — |
| In vivo insulin signaling / Western blot | BAT, inguinal and epididymal WAT, WT vs Pid1-/- mice | intraperitoneal insulin injection | phosphorylated AKT levels | — |
| Immunofluorescence / confocal microscopy | primary brown adipocytes, WT vs Pid1-/- mice | insulin stimulation | subcellular localization of LRP1, GLUT4, IRAP, GLUT1 | Nikon A1 confocal laser scanning microscope |
| Co-immunoprecipitation | brown adipose tissue and muscle, wild type mice | none (fasted) | PID1 enrichment in LRP1 pulldown fractions | — |
| GST-pulldown | muscle lysates, wild type mice | GST-PID1 fusion protein incubation | co-pulldown of LRP1, GLUT4, AS160 | — |
| Plasma membrane fractionation / Western blot | muscle tissue, WT vs Pid1-/- mice | Pid1 knockout | GLUT4 and LRP1 levels in isolated plasma membranes | — |
- – Cold exposure decreased PID1 and LRP1 mRNA/protein while increasing GLUT4 in BAT
- – PID1 and LRP1 expression correlated negatively, and GLUT4 positively, with BAT glucose uptake
- ▲ In Pid1-/- brown adipocytes, LRP1 and GLUT4 localized primarily at plasma membrane regardless of insulin, unlike WT which required insulin for translocation
- – Phosphorylated AKT levels were similar between WT and Pid1-/- adipocytes/tissues at baseline and after insulin stimulation
- ▲ PID1 highly enriched in LRP1 co-immunoprecipitation pulldown fractions from BAT and muscle lysates
- ▲ GST-PID1 pulled down LRP1, GLUT4 and AS160 from detergent-free muscle lysates
- ▲ Plasma membranes from Pid1-/- muscle showed higher GLUT4 and LRP1 levels than WT
- ▲ LRP1-deficient (Cre-infected) adipocytes showed higher cell-surface GLUT4 than LRP1-positive adipocytes in the same culture
- other P ≤ 0.05 (statistical significance threshold applied across t-tests/ANOVA analyses)
- other ~50% (Ad-Cre adenovirus infection rate in Lrp1 fl/fl primary brown adipocytes)
- other 0.79 kBq 2-deoxy-D-[1,2-3H(N)]-glucose per g body weight (radioactive tracer dose used for in vivo glucose uptake studies)
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 paper reports a mouse genetics study (global and adipose-specific Pid1 knockout vs. wild-type on chow and high-fat diet) combined with primary brown adipocyte in vitro experiments. Main comparisons used two-tailed unpaired Student's t-tests for pairwise group contrasts and one-way ANOVA with Dunnett post-hoc correction for multi-group comparisons (e.g., three temperature-acclimation conditions). Correlation analyses related expression of Pid1/Lrp1 to Glut4 mRNA and radioactive glucose uptake across individuals; the correlation method is not explicitly named. Results were interpreted against an α threshold of P ≤ 0.05.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-tailed unpaired Student's t-test | Pairwise comparisons between wild-type and Pid1−/− mice (body/organ weights, glucose levels, insulin levels, glucose uptake, Western blot densitometry, AKT phosphorylation, mRNA expression) | — | not stated |
| One-way ANOVA with Dunnett post-hoc correction | Multi-group comparisons, likely temperature-acclimation groups (6°C, 22°C, 30°C) for mRNA/protein expression and glucose uptake | — | not stated |
| Correlation analysis (method unspecified) | Association between Pid1/Lrp1 expression and Glut4 expression (Figures 2A–B) and between Pid1/Lrp1/Glut4 expression and BAT glucose uptake (Figures 2D–F) | — | not stated |
| ΔΔCT relative quantification | All mRNA expression comparisons, normalised to Tbp housekeeping gene | — | na |
-
Correlation between Pid1/Lrp1/Glut4 expression and BAT glucose uptake was assessed by an unspecified correlation method↳ Could also: Spearman rank correlation could also be used (or reported alongside Pearson) — With small n typical of mouse studies and gene-expression data that may not be normally distributed, Spearman's ρ makes no parametric distributional assumption; reporting both coefficients and a scatter plot with the fit lets readers judge linearity vs. monotonic association
-
Multiple pairwise two-tailed t-tests were used across numerous outcomes (body weight, organ weight, glucose, insulin, gene expression, protein levels, glucose uptake) comparing genotype groups↳ Could also: A linear mixed model or two-way ANOVA (genotype × diet as fixed factors, litter as a random effect) could also be used to analyse these data jointly — Treating litter as a random effect accounts for within-litter correlation among littermate controls, and a factorial model estimates the genotype × diet interaction directly; this also reduces the number of separate tests and the associated inflation of type-I error
-
The Dunnett correction was applied within ANOVA comparisons, but the family of multiple t-tests performed across metabolic readouts did not have a stated multiplicity correction↳ Could also: A Benjamini-Hochberg FDR correction across all tests within an experiment could also be applied — FDR control is common in studies with many simultaneous comparisons (expression, weight, plasma analytes, uptake) and would provide a transparent framework for interpreting the set of p-values collectively rather than each in isolation
-
Temperature-acclimation comparisons (6°C, 22°C, 30°C) used one-way ANOVA with Dunnett post-hoc, comparing each temperature to a single reference↳ Could also: Tukey's HSD post-hoc test could also be used if all pairwise contrasts among the three temperature groups are of interest — Dunnett is optimal when comparisons are made only against one control group; if 6°C vs. 30°C or 6°C vs. 22°C contrasts are also scientifically meaningful, Tukey provides all pairwise comparisons while still controlling the family-wise error rate
-
Dispersion measure used in figures is not stated in the available text↳ Could also: SD or 95% CI could also be reported alongside or instead of SEM — With the small group sizes typical of mouse metabolic studies, SEM can appear artificially narrow; SD describes the spread of individual observations and 95% CIs convey estimation uncertainty in a way that is directly interpretable for inference, so either is commonly preferred in small-n biological experiments
-
Sample sizes per group are not stated in the methods, and no formal power calculation is described↳ Could also: A priori power analysis (or post-hoc reporting of observed power and effect size) could also be reported — Stating the assumed effect size, variance estimate, and target power used to determine group size allows readers to assess whether the study was adequately powered for its primary comparisons, and is increasingly expected by journals and funders
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope analysis — pmid-30904610
Title: PID1 regulates insulin-dependent glucose uptake by controlling intracellular sorting of GLUT4-storage vesicles Authors: Fischer AW, Albers K, Schlein C, Sass F, Krott LM, Schmale H, Gordts PLSM, Scheja L, Heeren J. Journal: Biochim Biophys Acta (BBA) — Molecular Basis of Disease, 2019. PMID 30904610 · PMCID PMC6624118 (NIHPA author manuscript) · DOI 10.1016/j.bbadis.2019.03.010
Verdict: OUT OF SCOPE — no pipeline-derived computational result (non_pipeline drop)
This is a wet-lab molecular/cell-biology paper. Every reported result is generated by bench experiments and read out by manual/instrument quantification, then summarized with basic statistics in GraphPad Prism. There is no bioinformatic pipeline whose output could be regenerated, and no deposited dataset / code to run.
Methods inventory (from the PMC full text, §2 Materials and Methods)
| § | Method | Nature | In scope? |
|---|---|---|---|
| 2.1 | Mice and animal housing (global + adipocyte-specific Pid1-KO) | wet-lab, in vivo | no |
| 2.2 | Experimental procedures and organ harvest (GTT, ITT, plasma insulin) | wet-lab, in vivo | no |
| 2.3 | mRNA expression analysis — TRIzol RNA, quantitative real-time RT-PCR (assays-on-demand) | wet-lab qPCR | no |
| 2.4 | Cell culture (primary brown adipocytes) | wet-lab | no |
| 2.5 | Antibodies | reagent list | no |
| 2.6 | Protein extraction, SDS-PAGE, Western blotting (densitometry in LI-COR Image Studio Lite) | wet-lab | no |
| 2.7 | Immunoprecipitation | wet-lab | no |
| 2.8 | GST-pulldown experiments | wet-lab | no |
| 2.9 | Membrane preparation (surface vs intracellular GLUT4/LRP1) | wet-lab | no |
| 2.10 | Immunofluorescence — Nikon A1 confocal, co-localization | wet-lab imaging | no |
| 2.11 | Statistical analyses and data processing — Microsoft Excel + GraphPad Prism 6; two-tailed unpaired Student's t-test or one-way ANOVA + Dunnett | descriptive statistics, NOT a pipeline | no |
Data / code availability
- No
dbCrossReferenceentries in Europe PMC / NCBI metadata (no GEO/SRA/ENA/ArrayExpress/PRIDE/figshare/zenodo accession). - No "Data availability" or "Code availability" statement in the article.
- No GitHub/GitLab repository and no analysis scripts mentioned (the keyword "script" in the XML resolves to "Author Manuscript"; the keyword "proteom" resolves to a cited reference — Jedrychowski et al. 2010, JBC — not a method used here).
- Underlying numeric data exist only as bar graphs / blots inside the figures; raw values are not deposited.
Why no «our HPC» compute was run
There is nothing pipeline-derived to recompute. Running SLURM jobs would not reproduce any reported value because no omics/sequence/imaging dataset was deposited and no analysis code exists. Per HARD RULE 3 ("do not force it"), forcing a compute job here would be fabrication of activity, not reproduction.
Drop classification
drop_reason = non_pipeline (primary). Secondary corroborating gaps that would each
independently disqualify: no_data_accession and no_code. This is a text-mining false
positive for "computational reproduction" — a classic wet-lab paper.
No individual results have been recorded for this entry yet.
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 wet-lab molecular/cell-biology paper (Fischer et al., BBA Mol Basis Dis 2019, PMID 30904610) whose only computation is descriptive t-test/ANOVA in Excel + Prism 6 — there is no bioinformatic pipeline, no deposited dataset, and no code (dbCrossReferenceList null). Nothing could be recomputed, so the failure is purely one of data availability / non-pipeline scope, sitting on the input side rather than in any core computation. Following the rubric's principle, I keep q5/q7 at yellow (unverifiable, not an authors' defect or fabrication) while marking q1/q2/q4 red for the absence of comparable data and a derivable artifact. Overall a correctly classified non_pipeline drop with no discrepancy or fabrication concern — yellow, pending human confirmation.
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.