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Record W4392593329 · doi:10.1101/2024.03.04.583409

Eukaryotic Initiation Factor 3F (eIF3F) Regulates the IRES-Mediated Translation of Bcl-xL via Its Interaction with Programmed Cell Death 4 (PDCD4) Protein

2024· preprint· en· W4392593329 on OpenAlexafffund
Veda Hegde, Divya Sharma, Harshil Patel, Pavan Narasimha, Jason Luddu, Martin Holčı́k, Nehal Thakor

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsCarleton UniversityUniversity of CalgaryUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesMinistero dello Sviluppo Economico
KeywordsInternal ribosome entry siteCell biologyMessenger RNATranslation (biology)Untranslated regionBiologyEukaryotic initiation factorProtein biosynthesisProgrammed cell deathCell cultureMolecular biologyGeneApoptosisBiochemistryGenetics

Abstract

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Abstract Programmed cell death 4 (PDCD4) protein is a well-characterized tumor suppressor protein. PDCD4 inhibits mRNA translation by inhibiting the activity of an RNA helicase, eukaryotic initiation factor 4A (eIF4A). We have previously reported that PDCD4 interacts with the internal ribosome entry site (IRES) element that is found within the 5’ untranslated region (UTR) of mRNA encoding B-cell lymphoma extra-large (Bcl-xL) protein. PDCD4’s interaction with the Bcl-xL IRES element inhibits the IRES-mediated translation initiation on Bcl-xL mRNA. However, S6 kinase (S6K)-mediated phosphorylation of PDCD4 activates its degradation by proteasomal degradation pathway and derepress IRES-mediated translation initiation of Bcl-xL mRNA. Interestingly, eIF3F (one of the 13 subunits of eIF3) was reported to recruit S6K to phosphorylate eIF3. Therefore, we were intrigued by the possibility of co-regulation of PDCD4 and eIF3F by S6K and the regulation of IRES-mediated translation initiation by PDCD4-eIF3F. To this end, using co-immunoprecipitation (co-IP), we demonstrated that PDCD4 interacts with several subunits of eIF3. Reciprocal co-IP, endogenous IP, and in vitro pull-down assays demonstrated that eIF3F directly interacts with PDCD4 in an RNA-independent manner. In order to functionally characterize the PDCD4-eIF3F complex, we depleted PDCD4 from the glioblastoma (GBM) cells, which resulted in decreased levels of eIF3F. Also, depletion of eIF3F from GBM cells reduced the levels of PDCD4 protein. However, this was not observed in non-cancer cells. Overexpression of PDCD4 resulted in enhanced levels of eIF3F, and vice versa . We further confirmed that the interaction of eIF3F and PDCD4 proteins prevents each other’s proteasomal degradation. By performing RNA-IP, we showed that PDCD4 and eIF3F interact with Bcl-xL RNA independently. Moreover, our IRES-bi-cistronic reporter assay and polysome profiling experiments demonstrated that eIF3F regulates IRES-mediated translation of Bcl-xL mRNA, likely via its interaction with PDCD4. Significance This study uncovers the fundamental mechanism of the internal ribosome entry site (IRES)- mediated translation regulation of B-cell lymphoma extra-large (Bcl-xL) mRNA by programmed cell death 4 (PDCD4) protein, and the eukaryotic initiation factor 3F (eIF3F). Our results show that PDCD4 and eIF3F interact with each other directly and they also interact with Bcl-xL mRNA independently. We show that PDCD4 works via eIF3F to regulate Bcl-xL levels. We also show that the PDCD4-eIF3F-dependent mechanism of Bcl-xL mRNA translation is implicated in glioblastoma (GBM) cells, including the primary brain tumor stem cells (BTSCs), and would likely affect the GBM pathophysiology.

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 score0.001
Threshold uncertainty score0.005

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)0.0010.001

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 head0.017
GPT teacher head0.222
Teacher spread0.204 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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