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Record W4409633576 · doi:10.1158/1538-7445.am2025-6588

Abstract 6588: Characterization of TGFb1-induced ApoEVs and their potential role in epithelial cell plasticity

2025· article· en· W4409633576 on OpenAlexaff
Daxime F. Génier, Alicia Viloria‐Petit

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPlasticityCell biologyBiologyCancer researchPhysics

Abstract

fetched live from OpenAlex

Abstract Extracellular vesicles (EVs) are membranous particles that carry cargo such as proteins, lipids, and nucleic acids that can be crucial for intercellular communication. Epithelial-mesenchymal transition (EMT) is implicated in tissue remodeling and has been documented in the mammary gland during involution. Transgenic models and clinical studies suggest that both post-lactational and age-related lobular involution may increase susceptibility for breast cancer development. Transforming growth factor-beta 1 (TGFβ1) induces apoptosis of epithelial cells during involution and is also a potent inducer of EMT. TGFβ-induced apoptosis generates a subpopulation of EVs called apoptotic cell-derived EVs (ApoEVs), a potential cell-cell communication avenue that may be crucial for tissue remodeling. Here, we optimized a protocol to generate, isolate, and analyze TGFβ-induced ApoEVs. This may reveal potential EV-mediated signaling in driving EMT, tissue remodeling and cancer susceptibility in the mammary gland. The murine mammary gland epithelial cell line NMuMG was used, as they are known to both die and undergo EMT in response to TGFβ1 stimulation. NMuMG cells were cultured in DMEM with 10% FBS, 10 µg/mL insulin, and 1% antibiotics. Once 90% confluency was reached, growth media was replaced with starvation media consisting of DMEM, 5% EV-depleted FBS, and antibiotics. After 2.5 hours, one plate was treated with 5 ng/mL of TGFβ1 while the other received PBS. The conditioned media (CM) produced was centrifuged at 400g for 20 minutes to remove debris and then ultracentrifuged for 1 hour at 4ºC at a speed of 10, 000g to generate a pellet of large EVs. This was followed by two PBS washes under the same ultracentrifugation conditions. Next, the CM was again ultracentrifuged at a speed of 100, 000g to isolate smaller EVs. Both EV populations were resuspended in 10mM Hepes (pH 7.2) and characterized by western blot, dynamic light scattering (DLS), and transmission electron microscopy (TEM). Western blots for markers of EVs and potential contaminants suggest successful EV isolation. Blotting for apoptotic markers presented results consistent with ApoEVs, which were enriched in the samples obtained by 10, 000g ultracentrifugation. DLS analysis showed that EVs released by TGFβ-treated cells were larger and more abundant compared to those released by untreated cells; TEM analysis confirmed these results. Culturing NMuMG cells with either full CM or EV-depleted CM from either control or TGFβ1-treated cells showed morphological changes consistent with cell plasticity in response to EVs released by TGFβ1-treated cells. Our findings suggest that EVs produced during TGFβ1-induced apoptosis of NMuMG cells contain cargo capable of modulating epithelial phenotype and may aid directly or indirectly in tissue remodeling. Additional biological studies on target cells are necessary to confirm our results. Citation Format: Daxime F. Génier, Sarai A. Gomez Perez, Alicia Viloria-Petit. Characterization of TGFb1-induced ApoEVs and their potential role in epithelial cell plasticity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6588.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.035
GPT teacher head0.361
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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