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Record W4408667207 · doi:10.1093/stmcls/sxaf012

Nephron progenitor fate is modulated by angiotensin type 1 receptor signaling in human kidney organoids

2025· article· en· W4408667207 on OpenAlexaff
Hyunjae Chung, Waleed Rahmani, Sarthak Sinha, Alexander Pun, Rohit Arora, Arzina Jaffer, Jeff Biernaskie, Justin Chun

Bibliographic record

VenueStem Cells · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsBiologyLosartanNephronPodocyteProgenitor cellCell biologyAngiotensin IIOrganoidKidney developmentCell fate determinationPopulationInternal medicineKidneyEndocrinologyInduced pluripotent stem cellStem cellEmbryonic stem cellGeneticsTranscription factor

Abstract

fetched live from OpenAlex

The renin-angiotensin system (RAS) is essential for normal kidney development. Dysregulation of the RAS during embryogenesis can result in kidney abnormalities. To explore how angiotensin type 1 receptor (AT1R) signaling modulates nephron progenitor (NP) fate specification, we used induced pluripotent stem cell (iPSC) derived human kidney organoids treated with angiotensin II (Ang II) or the AT1R blocker losartan during differentiation. Ang II promoted NP proliferation and differentiation preferentially toward a podocyte fate, depleted the podocyte precursor population, and accelerated glomerular maturation. By contrast, losartan expanded the podocyte precursor population, delayed podocyte differentiation, and regressed the transcriptional signature to a more immature fetal state. Overall, using various in silico approaches with validation by RNAscope, we identified a role for AT1R signaling in regulating NP fate during nephrogenesis in kidney organoids. Our work supports the use of RAS modulators to improve organoid maturation and suggests that RAS may be a determinant of nephron endowment in vivo.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.224
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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