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Record W7036031932

Analysis of notch-driven regulation of dendritic cell function

2015· dissertation· en· W7036031932 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNotch signaling pathwayImmune systemInnate immune systemSignal transductionDendritic cellReceptorEffectorLigand (biochemistry)Hes3 signaling axis
DOInot available

Abstract

fetched live from OpenAlex

An effective immune response against pathogens or cancer requires proper guidance of the immune system. Being the first responders in innate immunity, Dendritic Cells (DCs) provide essential and differentiation cues to T cells, guiding them down specific effector lineages. Cell-cell communication through Notch signaling is initiated by trans-binding of receptor and ligand on neighboring cells to guide various cell fate decisions. However, recently it has been shown that cis-interaction of Notch receptor and Delta ligand in mammalian cells can result in inhibition of Notch signaling. DCs are known to express both Notch ligands and receptors. We have previously shown that DCs differentially express Notch ligands in response to stimuli associated with specific TH lineages. These findings suggest that relative abundance of Notch ligands to receptors may lead the cell to adopt a phenotype associated with signaling through either Notch ligand or receptor. Thereby, we hypothesize that the Notch signaling directly regulates DC function and therefore indirectly modulates T-cell polarization. To observe Notch signaling associated with different states of DC activation, we measured expression of components of Notch signal transduction in response to the Toll-like receptor (TLR) ligand lipopolysaccharide (LPS) and Leishmania parasites. Activation of DCs with TH1-directing LPS maintains the low Notch1 levels, but increases Notch2 expression. DCs infected with Leishmania remain immature, even if exposed to LPS subsequently, show higher Notch1 levels. Analysis of Notch1-deficient DCs, illustrated that Notch1 expression negatively regulates DC activation. Similarly, DCs stimulated with TH1-inducing agents maintain low Notch1 levels but have increased Delta4 expression. On the other hand, Notch2 expression positively regulates DC activation. Induced Notch2 expression leads to increased expression of co-stimulatory molecule CD86 and proinflammatory cytokine Interleukin 12 (IL-12). The increased Notch2 expression in DCs stimulated with LPS is, however, accompanied by a reduction in Jagged2 expression. Accordingly, Jagged2 is shown to negatively regulate IL-12 secretion. Such pattern of expression among Notch ligands and receptors (i.e. mutually exclusive expression of Notch1/Delta4 or Notch2/Jagged2) supports the evidence for cis-inhibition, a mechanism by which Notch receptors and ligands expressed on the same cell reciprocally inhibit one another. Such cis-interaction results in removal of the receptors and ligands from the cell surface and their subsequent proteolysis. All in all, our results propose that relative levels of Notch receptor and ligands can directly alter DC phenotype.

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.066
GPT teacher head0.326
Teacher spread0.260 · 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
Published2015
Admission routes1
Has abstractyes

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