MétaCan
Menu
Back to cohort

Human Regulatory T Cell Potential for Tissue Repair Via IL-33/ST2 and Amphiregulin

2018· article· en· W2883563483 on OpenAlexaff
Avery J. Lam, Haiming Huang, James Pan, Sachdev S. Sidhu, Guy Charron, Sabine Ivison, John D. Rioux, Megan K. Levings

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsUniversité de MontréalAmgen (Canada)University of TorontoMontreal BiodomeUniversity of British Columbia
Fundersnot available
KeywordsAmphiregulinInterleukin-7 receptorIL-2 receptorImmunologyBiologyRegulatory T cellIonomycinFlow cytometryT cellImmune systemCell biologyCancer researchReceptorGrowth factor

Abstract

fetched live from OpenAlex

Regulatory T cell (Treg)-based therapy is a promising curative approach for allograft rejection. Beyond their effects on immune cells, emerging evidence suggests that Tregs have direct effects on tissue repair. Specifically, Tregs in mice promote tissue repair after infection or injury by secreting the EGF family member amphiregulin (AREG) under the control of alarmin IL-33 and its receptor ST2. We investigated the potential of human blood Tregs to mediate tissue repair via the IL-33/ST2 axis and AREG production. AREG expression was measured by flow cytometry in blood Tregs (flow-sorted as CD4+CD25+CD127-) stimulated with PMA and ionomycin. Human Tregs could produce AREG ex vivo, upregulated by TCR activation, but at a lower proportion than their Tconv counterparts (flow-sorted as CD4+CD25-CD127+). AREG expression was enriched in non-effector Tregs (CD39-, CCR4-, TIGIT-), a phenotype maintained after TCR activation. Moreover, AREG production potential was lost upon Treg proliferation and differentiation, suggesting that AREG production may comprise a distinct modality of human Tregs. In contrast to reports from mouse Tregs, IL-33 did not affect human blood Treg production of AREG. However, ST2 was undetectable in blood Tregs ex vivo and after activation. To more accurately measure human ST2 expression, we used phage display to generate a series of anti-ST2 antibodies. Experiments in transfected and endogenous ST2+ cells revealed several candidate antibodies that were superior to commercially available options for flow cytometric detection of human ST2. Investigations to define the tissue localization and biology of human ST2+ Tregs are in progress. Meanwhile, because of the importance of IL-33 signalling in promoting both the function and maintenance of mouse ST2+ Tregs in tissues, we sought to generate a plentiful source of human ST2+ Tregs to evaluate their potential as a cell therapy. Human blood naïve Tregs (flow-sorted as CD4+CD25+CD127-CD45RA+) were engineered to overexpress ST2 and expanded for 12 days with artificial antigen-presenting cells, anti-CD3, and IL-2. ST2 overexpression conferred IL-33 responsiveness, as determined by signal transduction and increased proliferation. IL-33 upregulated AREG in a TCR-independent manner on ST2-engineered Tregs, suggesting that the tissue repair capacity of human Tregs may be controlled innately. Overall, human Treg expression of AREG is associated with an innate-like program, potentially regulated by IL-33 and uncoupled from classical TCR-dependent Treg effector functions. Knowledge of the mechanisms by which human Tregs mediate tissue repair and the signals controlling this process will help delineate their therapeutic potential when used as a cell therapy. Thus, future investigations will focus on the tissue repair capacity of human Treg-derived AREG in vitro, as well as the in vivo functions of ST2-engineered Tregs in a humanized allotransplant model. CIHR Doctoral Research Award. CIHR Foundation Grant.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.054
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 teacher head, 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueTransplantationSame topicIL-33, ST2, and ILC PathwaysFrench-language works237,207