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Activation of Non-Canonical Immune Signalling Pathways Drives Marrow Failure in a Murine Model of MDS

2014· article· en· W2286459480 on OpenAlexaff
Rawa Ibrahim, Joanna Węgrzyn, Linda Chang, Patricia Umlandt, Jeff Y. L. Lam, Megan Fuller, Aly Karsan

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsBiologyHaploinsufficiencyBone marrowCancer researchHaematopoiesisMyeloidMyeloid leukemiaImmunologyLeukemiaCell biologyPhenotypeStem cellGeneticsGene

Abstract

fetched live from OpenAlex

Abstract The Myelodysplastic Syndromes (MDS) are the most common hematological malignancies arising from stem/progenitor cells. MDS is characterized by ineffective hematopoiesis in one or more lineages of the bone marrow, resulting in peripheral cytopenias and the propensity to progress to either acute myeloid leukemia (AML) or bone marrow failure (BMF). The most common cytogenetic aberration associated with MDS is deletion of the long arm of chromosome 5. Many of the molecular events involved in the development of del(5q) MDS have been elucidated including haploinsufficiency of the gene encoding the ribosomal protein RPS14, responsible for the anemia observed, and haploinsufficency of the miRNAs miR-145 and miR-146a, which together target the innate immune signaling pathway, specifically, the Toll-like receptor-4 (TLR-4)signalling pathway. It has been demonstrated that overexpression of a target of miR-146a,TRAF6, in mouse bone marrow can recapitulate the phenotype of del(5q) MDS including the cytopenias and progression to BMF or AML. However, enforced expression of TIRAP, a miR-145 target gene, results in rapid BMF independent of TRAF6. The molecular and cellular mechanisms responsible for the differential outcome of overexpression of two genes that act within the same signalling pathway remain to be fully understood. We have identified several differentially expressed cytokines, including interferon gamma (IFNγ) and interleukin-10 (IL-10), following TIRAP overexpression compared with TRAF6 overexpression. Promoter methylation analysis has shown hypermethylation of key adaptors and signal transducers that lie between TIRAP and TRAF6 in the TLR-4 signalling pathway, suggesting activation of different pathways by TIRAP and TRAF6 overexpression. Indeed, blockade of TRAF6 and MyD88 did not inhibit TIRAP induced expression of these cytokines, suggesting that IFNγ and IL-10 production occurs in a TRAF6 and MyD88 independent manner. We identified IFNγ as the critical effector cytokine responsible for TIRAP mediated marrow failure. Gene set enrichment analysis has shown an enrichment of an IFNγ signature in MDS patients with a low risk of transformation to AML compared to healthy controls. Furthermore, interferon signatures were highly enriched in MDS patients compared to patients with AML, suggesting an important role for IFNγ signaling in driving MDS progression toward marrow failure as opposed to leukemic progression. IFNγ has been shown to inhibit components of the bone marrow niche by blocking RANK signalling in stromal cells such as osteoclast progenitors. Using coculture of TIRAP expressing bone marrow cells with the RAW264.7 monocyte cell line, a cell line that is capable of differentiation into osteoclasts, we found an inhibition in the ability of these cells to form osteoclasts compared to control. This provides the first line of evidence suggesting that immune signalling defects arising from genetic perturbations in the hematopoietic stem cell compartment can result in stem cell niche dysfunction leading to marrow failure. Disclosures No relevant conflicts of interest to declare.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.018
GPT teacher head0.259
Teacher spread0.240 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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