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Record W2144855932 · doi:10.1074/jbc.m113.494542

Mechanism and Function of Monoclonal Antibodies Targeting Siglec-15 for Therapeutic Inhibition of Osteoclastic Bone Resorption

2014· article· en· W2144855932 on OpenAlexaff
Matthew Stuible, Anna N. Moraitis, Annie Fortin, Stefan Saragosa, Aïda Kalbakji, Mario Filion, Gilles B. Tremblay

Bibliographic record

VenueJournal of Biological Chemistry · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsAlethia Biotherapeutics (Canada)
Fundersnot available
KeywordsSIGLECOsteoclastRANKLMonoclonal antibodyRANK LigandBone resorptionCell biologyChemistryReceptorAntibodyCancer researchBiologyImmunologyActivator (genetics)BiochemistryEndocrinology

Abstract

fetched live from OpenAlex

The use of monoclonal antibodies to target functionally important cell-surface proteins on bone-resorbing osteoclasts represents a promising approach for treatment of cancer-associated bone loss and other skeletal pathologies. Previously, we identified Siglec-15, a little studied sialic acid-binding receptor, as a candidate target that is highly up-regulated during osteoclast differentiation induced by the cytokine receptor activator of NF-κB ligand (RANKL). In this report, we confirm that Siglec-15 is localized to the plasma membrane where it can be targeted by monoclonal antibodies to inhibit differentiation of functional osteoclasts in vitro. Furthermore, we found that treatment of mice with these antibodies led to a marked increase in bone mineral density, consistent with inhibition of osteoclast activity. Interestingly, osteoblast numbers were maintained despite the anti-resorptive activity. At the molecular level, Siglec-15 interacts with the adapter protein DAP12 and can induce Akt activation when clustered on the osteoclast cell surface, which likely represents its normal signaling function. Importantly, we discovered that monoclonal antibodies induce rapid internalization, lysosomal targeting, and degradation of Siglec-15 by inducing receptor dimerization. This study defines a key regulatory node that controls osteoclast differentiation and activity downstream of RANKL and supports further development of Siglec-15 antibodies as a novel class of bone loss therapeutics. The use of monoclonal antibodies to target functionally important cell-surface proteins on bone-resorbing osteoclasts represents a promising approach for treatment of cancer-associated bone loss and other skeletal pathologies. Previously, we identified Siglec-15, a little studied sialic acid-binding receptor, as a candidate target that is highly up-regulated during osteoclast differentiation induced by the cytokine receptor activator of NF-κB ligand (RANKL). In this report, we confirm that Siglec-15 is localized to the plasma membrane where it can be targeted by monoclonal antibodies to inhibit differentiation of functional osteoclasts in vitro. Furthermore, we found that treatment of mice with these antibodies led to a marked increase in bone mineral density, consistent with inhibition of osteoclast activity. Interestingly, osteoblast numbers were maintained despite the anti-resorptive activity. At the molecular level, Siglec-15 interacts with the adapter protein DAP12 and can induce Akt activation when clustered on the osteoclast cell surface, which likely represents its normal signaling function. Importantly, we discovered that monoclonal antibodies induce rapid internalization, lysosomal targeting, and degradation of Siglec-15 by inducing receptor dimerization. This study defines a key regulatory node that controls osteoclast differentiation and activity downstream of RANKL and supports further development of Siglec-15 antibodies as a novel class of bone loss therapeutics.

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 score0.017
Threshold uncertainty score0.286

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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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

Citations71
Published2014
Admission routes1
Has abstractyes

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