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Record W4281757206 · doi:10.1016/j.molmet.2022.101524

DNA damage to β cells in culture recapitulates features of senescent β cells that accumulate in type 1 diabetes

2022· article· en· W4281757206 on OpenAlexafffund
Gabriel Brawerman, Jasmine Pipella, Peter J. Thompson

Bibliographic record

VenueMolecular Metabolism · 2022
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesMax Rady College of Medicine, University of ManitobaChildren's Hospital Research Institute of ManitobaUniversity of Alberta
KeywordsDNA damageDNACell biologyCell cultureType 2 diabetesType 1 diabetesDiabetes mellitusBiologyGeneticsEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: Type 1 Diabetes (T1D) is characterized by progressive loss of insulin-producing pancreatic β cells as a result of autoimmune destruction. In addition to β cell death, recent work has shown that subpopulations of β cells acquire dysfunction during T1D. We previously reported that β cells undergoing a DNA damage response (DDR) and senescence accumulate during the pathogenesis of T1D. However, the question of how senescence develops in β cells has not been investigated. METHODS: Here, we tested the hypothesis that unrepaired DNA damage in the context of genetic susceptibility triggers β cell senescence using culture models including the mouse NIT1 β cell line derived from the T1D-susceptible nonobese diabetic (NOD) strain, human donor islets and EndoC β cells. DNA damage was chemically induced using etoposide or bleomycin and cells or islets were analyzed by a combination of molecular assays for senescence phenotypes including Western blotting, qRT-PCR, Luminex assays, flow cytometry and histochemical staining. RNA-seq was carried out to profile global transcriptomic changes in human islets undergoing DDR and senescence. Insulin ELISAs were used to quantify glucose-stimulated insulin secretion from chemically-induced senescent human islets, EndoC β cells and mouse β cell lines in culture. RESULTS: Sub-lethal DNA damage in NIT1 cells led to several classical hallmarks of senescence including sustained DDR activation, growth arrest, enlarged flattened morphology and a senescence-associated secretory phenotype (SASP) resembling what occurs in primary β cells during T1D in NOD mice. These phenotypes differed between NIT1 cells and the MIN6 β cell line derived from a non-T1D susceptible mouse strain. RNA-seq analysis of DNA damage-induced senescence in human islets from two different donors revealed a p53 transcriptional program and upregulation of prosurvival and SASP genes, with inter-donor variability in this response. Inter-donor variability in human islets was also apparent in the extent of persistent DDR activation and SASP at the protein level. Notably, chemically induced DNA damage also led to DDR activation and senescent phenotypes in EndoC-βH5 human β cells, confirming that this response can occur directly in a human β cell line. Finally, DNA damage led to different effects on glucose-stimulated insulin secretion in mouse β cell lines as compared with human islets and EndoC β cells. CONCLUSIONS: Taken together, these findings suggest that some of the phenotypes of senescent β cells that accumulate during the development of T1D in the NOD mouse and humans can be modeled by chemically induced DNA damage to mouse β cell lines, human islets and EndoC β cells in culture. The differences between β cells from different mouse strains and different human islet donors and EndoC β cells highlights species differences and the role for genetic background in modifying the β cell response to DNA damage and its effects on insulin secretion. These culture models will be useful tools to understand some of the mechanisms of β cell senescence in T1D.

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 score0.004

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 integrity0.0000.001
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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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

Citations29
Published2022
Admission routes2
Has abstractyes

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