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Record W4323350987 · doi:10.1093/jcag/gwac036.027

A27 DNA HYPOMETHYLATION INDUCED BY 5-AZA-CDR OR LOSS OF DNMT1 INHIBITS COLITIS-ASSOCIATED COLORECTAL CANCER

2023· article· en· W4323350987 on OpenAlexaffabout
Finn Ole Larsen, Hayley Good, Alice E. Shin, Mathieu Derouet, L Zhang, Samuel Asfaha

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsWestern University
Fundersnot available
KeywordsDNA methylationColorectal cancerCarcinogenesisColitisCancer researchAdenomatous polyposis coliEpigeneticsCancerUlcerative colitisDNA methyltransferaseMethyltransferaseMethylationInflammatory bowel diseaseDNMT1MedicineBiologyMolecular biologyGene expressionImmunologyInternal medicineDNAGeneDiseaseGenetics

Abstract

fetched live from OpenAlex

Abstract Background Colorectal cancer is the second leading cause of cancer death in Canada. A major risk factor for the development of colorectal cancer is chronic inflammation leading to colitis-associated cancer (CAC). We previously described a CAC mouse model in which tumors arise from DCLK1+ tuft cells following loss of the tumor suppressor adenomatous polyposis coli (APC) and induction of colitis. Interestingly, both colitis and CAC display epigenetic changes that modulate gene expression. However, the impact of DNA methylation changes on colonic tumorigenesis is not known. Thus, we hypothesize that inhibition of DNA methylation in DCLK1+ tuft cells reduces colonic tumorigenesis. Purpose In this study, we aim to investigate the role of DNA methylation in CAC by inhibiting DNA methylation using genetic and pharmacologic means. Method Using a publicly available dataset (GSE75214) of gene expression data analyzed by microarray from colonic biopsies of patients with ulcerative colitis and Crohn’s Disease with active disease, we examined the expression of DNA methyltransferases (DNMTs). Expression of DNMTs in mice with colitis was additionally examined by RT-qPCR and global DNA methylation levels measured by 5-mC ELISA. In separate experiments, Dclk1-CreERT2/Apcf/f mice were crossed to DNMT1f/f mice to knock-out the DNA methyltransferase DNMT1 in DCLK1+ tuft cells. Dclk1/Apcf/f and Dclk1/Apcf/f/DNMT1f/f mice were then administered three doses of tamoxifen followed by 2.5% dextran sodium sulfate (DSS) for five days to induce colitis. Fourteen weeks later, we assessed colonic tumor number and size. In a separate cohort of Dclk1/Apcf/f mice, we induced colitis and treated the mice with six doses of the DNA de-methylating drug 5-AZA-2’-deoxycytidine (5-AZA) or vehicle, and assessed colonic tumor number. To examine DNA methylation changes, we then treated WT mice with 5-AZA and DSS and isolated intestinal epithelial cells. From the intestinal epithelial cell, we isolated DNA and ran the Infinium MouseMethylation BeadChip Array. Result(s) Patients with IBD were found to have increased expression of DNMT1 compared to healthy controls. Mice treated with DSS similarly had increased DNMT1 expression, as well as, global methylation levels compared to controls. Deletion of DNMT1 in DCLK1+ cells significantly inhibited the number and size of colonic tumors. Treatment of mice with 5-AZA decreased global and gene specific DNA methylation levels, and significantly reduced both the number of mice with tumors, and the average colonic tumor number and size per mouse. Conclusion(s) Our findings demonstrate that colitis in both patients and mice is associated with DNA methylation. Furthermore, DNA hypomethylation by 5-AZA treatment or loss of DNMT1 reduces CAC formation suggesting that altered DNA methylation plays a critical role in colonic tumorigenesis. Disclosure of Interest None Declared

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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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 routes2
Has abstractyes

Explore more

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