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Ca <sup>2+</sup> Signaling and Barrier Function of Lung Microvascular Endothelial Cells are Modulated by Mesenchymal Stromal Cell Microparticles

2019· article· en· W3176689297 on OpenAlexafffundabout
Mazharul Maishan, Mark J. McVey, Gerard F. Curley, Wolfgang M. Kuebler

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsARDSMesenchymal stem cellMicrovesicleExtracellularThrombinMicrovesiclesIntracellularCell biologyEndothelial stem cellStromal cellChemistryMedicineVascular permeabilityCancer researchLungPathologyPlateletImmunologyBiologyIn vitroInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Introduction Acute respiratory distress syndrome (ARDS) is a fatal condition characterized by hyperinflammation and pulmonary microvascular leak. With no pharmacological cure, treatment remains only supportive. Mesenchymal stromal cells (MSCs) have recently generated excitement as a potential therapy, presumably functioning through paracrine mechanisms which may rely on the release of extracellular vesicles like microparticles (MPs). However, the mechanism by which MPs modulate the pathology of ARDS is poorly understood yet may pave the way to develop cell‐free cell therapeutics. Objective To identify the mechanism(s) by which MSC derived MPs enhance pulmonary microvascular barrier function. Methods MPs were purified from conditioned medium produced by MSCs either stimulated with Ca 2+ ionophore or unstimulated. Monolayers of primary human pulmonary microvascular endothelial cells (HPMECs) were injured with thrombin to induce barrier disruption, measured by transendothelial electrical resistance (TEER), mimicking lung microvascular leak as observed in ARDS. MPs were administered to treat thrombin‐injured HPMECs and changes in intracellular Ca 2+ concentration ([Ca 2+ ] i ) were determined by ratiometric imaging of Fura‐2. Results Thrombin administered to HPMECs caused an initial, brief spike in [Ca 2+ ] i followed by a second phase characterized as a prolonged, gradual increase in [Ca 2+ ] i . Following the thrombin‐induced [Ca 2+ ] i spike, treatment with MPs from unstimulated MSCs eliminated the second phase and caused a sustained, decrease in [Ca 2+ ] i below baseline. In parallel, MPs from unstimulated MSCs enhanced TEER recovery and VE‐cadherin integrity in intercellular junctions of HPMECs following thrombin‐induced permeability. Conversely, treatment with MPs from Ca 2+ ionophore stimulated MSCs amplified the second phase of the [Ca 2+ ] i response to thrombin and prevented recovery of HPMEC barrier function. HPLC‐MS revealed MPs from unstimulated MSCs had lower ceramide and higher sphingosine‐1‐phosphate (S1P) content than MPs from Ca 2+ ionophore stimulated MSCs. S1P degradation by S1P lyase or blockade of the S1P receptor 1 attenuated the barrier‐protective effect of MPs from unstimulated MSCs, while ceramide degradation by neutral ceramidase improved barrier recovery following treatment with MPs from Ca 2+ ionophore stimulated MSCs. Conclusion MPs from unstimulated MSCs therapeutically enhance lung capillary barrier function, presumably by attenuating endothelial [Ca 2+ ] i responses by resetting the ceramide/S1P rheostat. These findings provide a mechanistic basis for development and optimization/enrichment of MSC derived MPs as a promising cell‐free cell therapy for ARDS. Support or Funding Information This work was supported by the Ontario Research Fund and Canadian Institutes of Health Research. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 head0.003
GPT teacher head0.186
Teacher spread0.182 · 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".

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Citations0
Published2019
Admission routes3
Has abstractyes

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