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G-CSF Induces Expression of Both Hepatocyte Growth Factor (HGF) and Its Receptor (c-Met) in Human Hematopoietic Stem/Progenitor Cells and Mature Myeloid Cells - Novel Evidence That during Mobilization the HGF-c-Met Axis Counterbalances G-CSF-Induced Attenuation of the SDF-1-CXCR4 Axis.

2007· article· en· W2575174870 on OpenAlexaff
Ali Jalili, Neeta Shirvaikar, Sara Ilnitsky, A. Robert Turner, Mariusz Z. Ratajczak, Anna Janowska‐Wieczorek

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsCanadian Blood ServicesUniversity of Alberta
Fundersnot available
KeywordsHepatocyte growth factorCD34HaematopoiesisStromal cellStem cellProgenitor cellBone marrowCXCR4Stem cell factorBiologyStromal cell-derived factor 1Cell biologyCancer researchMolecular biologyImmunologyReceptorChemokineInflammation

Abstract

fetched live from OpenAlex

Abstract The stromal-derived factor-1 (SDF-1)-CXCR4 axis plays an important role in stem cell trafficking, and G-CSF- induced mobilization decreases SDF-1 expression in the bone marrow (BM) microenvironment and CXCR4 expression by CD34+ hematopoietic stem/progenitor cells (HSPC). Alternatively, the tyrosine kinase c-Met receptor-HGF axis was recently postulated to play an important role in the trafficking of non-hematopoietic cells; however, our previous research demonstrated that while HGF is an important constituent of the BM microenvironment, c-Met is not expressed by BM-derived steady state HSPC (Br J Haem1997;99:228). Recently we observed that both c-Met and HGF are upregulated during tissue organ/injury in a hypoxia-inducible factor-1α-dependent manner (Circ Res2004;95:1191). To determine whether G-CSF-induced mobilization affects the c-Met-HGF axis in HSPC, we isolated human CD34+ cells from BM and mobilized peripheral blood (mPB), mature myeloid cells and stromal cells and evaluated expression of c-Met and HGF by hematopoietic and BM-derived stromal cells without and after exposure to G-CSF, and the chemotactic responses of steady state and mobilized hematopoietic cells to HGF. We confirmed using RT-PCR and FACS analysis that the c-Met receptor is not expressed by steady-state BM CD34+ cells and mature mononuclear cells (MNC), but to our surprise we found that c-Met is expressed in G-CSF-mobilized CD34+ cells and MNC obtained from leukapheresis products. Supporting this was our finding that mPB but not steady state CD34+ cells responded to HGF stimulation by phosphorylation of MAPKp42/44 and AKT. HGF was found to highly expressed in mPB CD34+ cells and MNC but in steady state BM MNC. Moreover, G-CSF stimulation induced HGF expression in steady state BM MNC and BM-derived fibroblastic and mesenchymal stem cells. Importantly, when we compared c-Met expression on circulating PB leukocytes from patients during early and late stages of G-CSF mobilization we found that it increases in the later stages. When we compared the expression of CXCR4 and c-Met on leukocytes from the leukapheresis product vs circulating PB from the same patient we found that CXCR4 expression is similar but expression of c-Met is higher in the leukapheresis product. HGF was also found to be a strong chemoattractant for mPB leukocytes, but not for steady state leukocytes, and the chemotactic activity of HGF was totally inhibited by a c-Met antagonist. Additionally, we demonstrated that c-Met is incorporated into lipid rafts as shown by confocal microscopy and that G-CSF stimulation increases the secretion of matrix metalloproteinase (MMP)-9 from BM MNC, which was inhibited by the c-Met antagonist. A combination of G-CSF and HGF also upregulated membrane-type (MT)-MMPs such as MT1-MMP and MT6-MMP on leukocytes. Thus we demonstrate for the first time that G-CSF induces expression of functional c-Met and HGF in HSPC and leukocytes, and that the HGF-c-Met axis could play an important role in their mobilization and maintenance of high expression of matrix-degrading enzymes, allowing egress of HSPC from the BM.

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)0.001
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 score0.137
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.266
Teacher spread0.233 · 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".

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Citations0
Published2007
Admission routes1
Has abstractyes

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