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Record W4376872045 · doi:10.2147/jir.s401566

FLI1 Regulates Histamine Decarboxylase Expression to Control Inflammation Signaling and Leukemia Progression

2023· article· en· W4376872045 on OpenAlexaff
Jifen Hu, Jian Gao, Chunlin Wang, Wuling Liu, Anling Hu, Xiao Xiao, Yi Kuang, Kunlin Yu, Babu Gajendran, Eldad Zacksenhaus, Weidong Pan, Yaacov Ben‐David

Bibliographic record

VenueJournal of Inflammation Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersGuizhou Medical UniversityNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsHistidine decarboxylaseCancer researchHistamineAllergic inflammationJurkat cellsBiologyChromatin immunoprecipitationInflammationGene knockdownMolecular biologyChemistryPromoterImmunologyGene expressionApoptosisT cellGeneImmune systemBiochemistryEnzymePharmacology

Abstract

fetched live from OpenAlex

Aim: Histamine decarboxylase (HDC) catalyzes decarboxylation of histidine to generate histamine. This enzyme affects several biological processes including inflammation, allergy, asthma, and cancer, although the underlying mechanism is not fully understood. The present study provides a novel insight into the relationship between the transcription factor FLI1 and its downstream target HDC, and their effects on inflammation and leukemia progression. Methods: in leukemic cells. Western blotting and RT-qPCR were used to determine expression of HDC and allergy response genes, and lentivirus shRNA was used to knock-down target genes. Proliferation, cell cycle, apoptosis assays and molecular docking were used to determine the effect of HDC inhibitors in culture. An animal model of leukemia was employed to test the effect of HDC inhibitory compounds in vivo. Results: by direct binding to its promoter. Using genetic and pharmacological inhibition of HDC, or the addition of histamine, the enzymatic product of HDC, we show neither have a discernable effect on leukemic cell proliferation in culture. However, HDC controls several inflammatory genes including IL1B and CXCR2 that may influence leukemia progression in vivo through the tumor microenvironment. Indeed, diacerein, an IL1B inhibitor, strongly blocked Fli-1-induced leukemia in mice. In addition to allergy, FLI1 is shown to regulate genes associated with asthma such as IL1B, CPA3 and CXCR2. Toward treatment of these inflammatory conditions, epigallocatechin (EGC), a tea polyphenolic compound, is found strongly inhibit HDC independently of FLI1 and its downstream effector GATA2. Moreover, the HDC inhibitor, tetrandrine, suppressed HDC transcription by directly binding to and inhibiting the FLI1 DNA binding domain, and like other FLI1 inhibitors, tetrandrine strongly suppressed cell proliferation in culture and leukemia progression in vivo. Conclusion: These results suggest a role for the transcription factor FLI1 in inflammation signaling and leukemia progression through HDC and point to the HDC pathway as potential therapeutics for FLI1-driven leukemia.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.356
Teacher spread0.332 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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