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Record W4380359389 · doi:10.1093/neuonc/noad073.061

DIPG-14. PROTEOGENOMICS PROFILING REVEALS ENRICHED NON-HISTONE PROTEIN METHYLTRANSFERASES AS NEW THERAPEUTIC TARGETS IN DIPG

2023· article· en· W4380359389 on OpenAlexaff
Arun Kumaran Anguraj Vadivel, Sanja Pajovic, Palak Patel, Robert Siddaway, Sabrina Zhu, Lauren Phillips, Cynthia Hawkins

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsBiologyEpigeneticsHistoneProteomeHistone H3TranscriptomeProteomicsProteogenomicsMethyltransferaseDNA methylationMethylationGeneticsCancer researchGene expressionDNAGene

Abstract

fetched live from OpenAlex

Abstract Diffuse intrinsic pontine glioma (DIPG) is a devastating brain tumour arising in the brainstems of children. Despite advances in genomics and treatments, the survival rate remains zero, with a median of less than a year, making it the leading cause of death among children with brain tumours. A mutation in histone H3 protein (H3K27M) has been identified as a genetic initiation event and affects global K27 trimethylation on histone H3 proteins and DNA methylation in DIPG. The epigenetic changes caused by the H3K27M mutation suggest the existence of an H3K27M-specific transcriptome and proteome. To deepen our knowledge of DIPG, we conducted a proteogenomic analysis of DIPG tissues by using high-resolution mass spectrometry for comprehensive proteome profiling, including total proteome, phosphoproteome, and methylproteome. Integrating proteomics data with DNA methylation and transcriptomics (bulk RNAseq and single-cell RNAseq) provided new insights into DIPG tumorigenesis. Our multi-omics analyses reveal enriched translation machinery, negative regulation of apoptosis process, and non-histone protein methyltransferase proteins, suggesting their previously unknown roles in DIPG cell growth and survival. Furthermore, our findings indicate that DIPG tissues have lower global mean protein phosphorylation and higher global mean protein methylation compared to normal brains, implying that DIPG may use methyl-signaling rather than phospho-signaling for tumour growth. The translation elongation proteins EEF1A1 and EEF1A2 are the most highly methylated proteins in DIPG. Methylation modifications such as K55me2, K79me3, and K165me2 of EEF1A1 are significantly higher in DIPG tissues compared to normal brains. These enriched methylpeptides are substrates of the non-histone methyltransferases METTL13 and METTL21B, which are enriched in the multi-omics analysis. Knocking down these methyltransferases in DIPG cells significantly decreases their target protein methylation, reduces global protein synthesis, and inhibits cell growth in vitro. Thus, proteogenomic analysis of DIPG reveals tumour-enriched non-histone methyltransferases, METTL13 and METTL21B as new therapeutic targets.

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 score0.003

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)0.0010.000

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 head0.039
GPT teacher head0.345
Teacher spread0.306 · 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
Published2023
Admission routes1
Has abstractyes

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