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Evaluating the Mitochondrial Activity and Inflammatory State of Dimethyl Sulfoxide Differentiated PLB‐985 Cells

2019· article· en· W3176343370 on OpenAlexafffund
Jean‐Luc Jougleux, Jacob L. Léger, Patrick Roy, Marie‐Ange Djeungoue Petga, Étienne Hébert Chatelain, Luc H. Boudreau

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicChemokine receptors and signaling
Canadian institutionsUniversité de Moncton
FundersCanadian Institutes of Health ResearchNew Brunswick Innovation Foundation
KeywordsFlow cytometryInflammationCell biologyImmunologyApoptosisDimethyl sulfoxideCell cultureNeutrophil extracellular trapsChemistryBiologyCancer researchBiochemistry

Abstract

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INTRODUCTION Neutrophils play a key role as the first line of defense against pathogens. Their ability to generate and release inflammatory mediators (eicosanoids or cytokines) helps promote the inflammatory response and consequently restore the hemostasis. While active participants in several steps of the normal inflammatory response, neutrophils are also involved in chronic inflammatory diseases such as asthma, atherosclerosis and arthritis. Given their dual role in the modulation of inflammation, regulating the inflammatory response of neutrophils has been an important therapeutic approach pursued by numerous researchers. The neutrophil's lifespan is relatively short (8–12h), which can be problematic for some in vitro experiments. To address this issue, researchers used the human monomyelocyte cell line PLB‐985 as an alternative for some of their in vitro experiments. PLB‐985 cells can be differentiated into a more neutrophil‐like phenotype upon exposure to several agonists, including dimethyl sulfoxide (DMSO). Whether this differentiation of PLB‐985 affects important features related to neutrophil's normal functions ( i.e . mitochondrial activity, eicosanoid production) remains elusive and characterizing these changes will be the focal point of this project. OBJECTIVES We assessed and compared the mitochondrial activity and inflammatory state of PLB‐985 cells following differentiation. METHODS Differentiation of PLB‐985 cells was performed using 1% DMSO for 6 days and was confirmed by immunofluorescence microscopy and flow cytometry using an anti‐CD11b‐PE cell surface antibody. Proliferation and apoptosis assays were performed by flow cytometry using the eFluor 670 and 7‐aminoactinomycin D labelers respectively. The mitochondrial respiratory assay was determined by high‐resolution O 2 respirometry (Oroboros Instrument). The inflammatory state of the cells was evaluated by transcriptomic, proteomic and lipidomic approaches. RESULTS Differentiation affected the proliferation of PLB‐985 cells ( P <0.0001), without inducing apoptosis. A significant decrease in the mitochondrial respiration was observed in differentiated PLB‐985 cells ( P <0.0005). However, the overall mitochondria content was not affected. Immunoblotting with mitochondrial antibodies revealed a strong modulation of the succinate dehydrogenase A ( P <0.0001), superoxide dismutase 2 ( P <0.0001), ubiquinol‐cytochrome c reductase core protein 2 ( P <0.05) and ATP synthase subunit α ( P <0.01) in differentiated PLB‐985 cells. Finally, eicosanoids (leukotriene B 4 , 12‐hydroxyheptadecatrienoic and 15‐hydroxyeicosatetraenoic acids) production was significantly increased in differentiated cells ( P <0.01). CONCLUSIONS Our data demonstrate that the differentiation process of PLB‐985 cells does not impact their viability despite a reduced respiratory state of the cells. This process is also accompanied by modulations of the inflammatory state of the cell. Finally, our data suggests that PLB‐985 cells could be a suitable in vitro candidate to study mitochondrial‐related dysfunctions in inflammatory diseases. Support or Funding Information CIHR, NBHRF, NBIF This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.294
Teacher spread0.270 · 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 routes2
Has abstractyes

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