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Record W7052177582

The roles of SH2-containing inositol 5'-phosphatase 1 (SHIP1) and signal transducer and activator of transcription 3 (STAT3) in interleukin-10 regulation of activated macrophages

2016· other· en· W7052177582 on OpenAlexfundno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2016
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersHealth Canada
KeywordsTranscription (linguistics)Transcription factorSignal transductionImmune systemActivator (genetics)STAT proteinCytokine
DOInot available

Abstract

fetched live from OpenAlex

Inflammation is a protective mechanism against infection, but it must be appropriately regulated to prevent pathological consequences including inflammatory diseases. Interleukin-10 (IL-10) is a key anti-inflammatory cytokine that inhibits the activation of many immune cell types, including the macrophage, to prevent exaggerated immune responses. Understanding the signalling pathway downstream of IL-10 and IL-10 receptor (IL-10R) is essential in developing therapeutics to treat inflammatory diseases. Canonically, IL-10R signalling is described as solely depending on the activation of the transcription factor STAT3 and the expression of STAT3-response genes. However, our laboratory has previously shown that IL-10 also activates the inositol 5'-phosphatase SHIP1 to mediate its anti-inflammatory responses, such as inhibiting the expression of pro-inflammatory cytokines. Moreover, we have now found that IL-10 is able to inhibit expression of miRNA-155 (miR-155) in macrophages activated by the bacterial product lipopolysaccharide (LPS). This inhibition by IL-10 requires both STAT3 and SHIP1, and occurs at the maturation step, but not at the transcription step, of miR-155. Consistent with SHIP1’s involvement in IL-10 function, a previously described SHIP1 activator, AQX-MN100, mimics IL-10 and inhibits LPS-induced miR-155. Next, we investigated the roles of STAT3 and SHIP1 in IL-10 regulation of global gene expression in activated macrophages. Among the genes identified as IL-10 regulated, we have found different subsets of genes potentially to be SHIP1-regulated, STAT3-regulated, and SHIP1-STAT3 regulated. Considering the importance of SHIP1 activation in IL-10 function, SHIP1 activators provide an alternate way to control inflammation. We have previously shown that SHIP1 activators bind to SHIP1’s C2 domain and regulate SHIP1 enzymatic activity allosterically. Using x-ray crystallography, we obtained the crystal structure of SHIP1’s phosphatase and C2 domains, the minimal region necessary for allosteric activation by SHIP1 activators. Analysis of the crystal structure revealed differences between SHIP1 and related phosphatases. Biochemical and biophysical methods have been employed to identify amino acid residues potentially interacting with SHIP1 activators. Together, this work strengthens the model that STAT3 and SHIP1 work together to mediate the anti-inflammatory function of IL-10. We also described the structure of SHIP1, providing the first step in rational drug design to generate better small molecule SHIP1 activators.

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 score0.003

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)0.0010.000

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 head0.193
Teacher spread0.188 · 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
Published2016
Admission routes1
Has abstractyes

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