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Record W2898295389 · doi:10.1093/molehr/gay044

Activation of latent transforming growth factor-β1, a conserved function for pregnancy-specific beta 1-glycoproteins

2018· article· en· W2898295389 on OpenAlexaff
James Warren, Michelle Im, Ángela Ballesteros, Cam T. Ha, Tom Moore, Fanny Lambert, Sophie Lucas, Boris Hinz, Gabriela Dveksler

Bibliographic record

VenueMolecular Human Reproduction · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteUniformed Services University of the Health SciencesU.S. Department of Defense
KeywordsBiologyTransforming growth factorJurkat cellsTransfectionMolecular biologyCell biologyReceptorBiochemistryImmune systemT cellImmunologyGene

Abstract

fetched live from OpenAlex

STUDY QUESTION: Do all 10 human pregnancy-specific beta 1-glycoproteins (PSGs) and murine PSG23 activate latent transforming growth factor-β1 (TGF-β1)? SUMMARY ANSWER: All human PSGs and murine PSG23 activated latent TGF-β1. WHAT IS KNOWN ALREADY: Two of the 10 members of the PSG1 family, PSG1 and PSG9, were previously shown to activate the soluble small latent complex of TGF-β1, a cytokine with potent immune suppressive functions. STUDY DESIGN, SIZE, DURATION: Recombinant PSGs were generated and tested for their ability to activate the small latent complex of TGF-β1 in a cell-free ELISA-based assay and in a bioassay. In addition, we tested the ability of PSG1 and PSG4 to activate latent TGF-β bound to the extracellular matrix (ECM) or on the membranes of the Jurkat human T-cell line. PARTICIPANTS/MATERIALS, SETTING, METHODS: Recombinant PSGs were generated by transient transfection and purified with a His-Trap column followed by gel filtration chromatography. The purified PSGs were compared to vehicle (PBS) used as control for their ability to activate the small latent complex of TGF-β1. The concentration of active TGF-β was measured in an ELISA using the TGF-β receptor II as capture and a bioassay using transformed mink epithelial cells that express luciferase in response to active TGF-β. The specificity of the signal was confirmed using a TGF-β receptor inhibitor. We also measured the binding kinetics of some human PSGs for the latent-associated peptide (LAP) of TGF-β using surface plasmon resonance and determined whether PSG1 and PSG4 could activate the large latent complex of TGF-β1 bound to the ECM and latent TGF-β1 bound to the cell membrane. All experiments were performed in triplicate wells and repeated three times. MAIN RESULTS AND THE ROLE OF CHANCE: All human PSGs activated the small latent complex of TGF-β1 (P < 0.05 vs. control) and showed similar affinities (KD) for LAP. Despite the lack of sequence conservation with its human counterparts, the ability to activate latent TGF-β1 was shared by a member of the murine PSG family. We found that PSG1 and PSG4 activated the latent TGF-β stored in the ECM (P < 0.01) but did not activate latent TGF-β1 bound to glycoprotein A repetitions predominant (GARP) on the surface of Jurkat T cells. LIMITATIONS, REASONS FOR CAUTION: The affinity of the interaction of LAP and PSGs was calculated using recombinant proteins, which may differ from the native proteins in their post-translational modifications. We also utilized a truncated form of murine PSG23 rather than the full-length protein. For the studies testing the ability of PSGs to activate membrane-bound TGF-β1, we utilized the T-cell line Jurkat and Jurkat cells expressing GARP rather than primary T regulatory cells. All the studies were performed in vitro. WIDER IMPLICATIONS OF THE FINDINGS: Here, we show that all human PSGs activate TGF-β1 and that this function is conserved in at least one member of the rodent PSG family. In vivo PSGs could potentially increase the availability of active TGF-β1 from the soluble and matrix-bound latent forms of the cytokine contributing to the establishment of a tolerogenic environment during pregnancy. LARGE-SCALE DATA: None. STUDY FUNDING/COMPETING INTEREST(S): The research was supported by a grant from the Collaborative Health Initiative Research Program (CHIRP). No conflicts of interests are declared by the authors.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.001

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 head0.027
GPT teacher head0.260
Teacher spread0.232 · 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

Citations49
Published2018
Admission routes1
Has abstractyes

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