MétaCan
Menu
← Back to cohort

Unraveling Intron Retention: Differential Expression Pattern in Brain and Kidney Cells After Hypoxia or Ischemia

2020· article· en· W3017216604 on OpenAlexaffabout
Victoria Northrup, Lester J. Pérez, Brittany A. Edgett, Jeremy A. Simpson, Keith R. Brunt

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of GuelphDalhousie University
Fundersnot available
KeywordsIntronBiologyErythropoietinRNA splicingExonAlternative splicingPrimer (cosmetics)Gene expressionCell biologyGeneMolecular biologyRNAGeneticsChemistry

Abstract

fetched live from OpenAlex

Introduction The inclusion of introns in the mature mRNA (intron retention), has been recently uncovered as a mechanism for the regulation of genetic expression in vertebrates. Intron retention is implicated in tissue specific protein diversity, splicing regulation and control of gene expression. We have recently identified intron retention as a mechanism involved in the regulation of erythropoietin (EPO) . EPO is the key regulator of erythropoiesis and is transcriptionally regulated by hypoxia inducible factor 2 (HIF2). The ability to easily detect intron retention could add to our ability to better understand genetic regulation of our genomes in stress conditions, such as ischemic diseases (ex/ myocardial infarction (MI) or stroke). Objective To evaluate the role of intron retention mechanism in EPO expression level to gain a better understanding of its regulation in response to hypoxia and ischemia, conditions which result in EPO expression. Methods HTB16 (neuroblastoma), CHP212 (glioblastoma), hCMEC/D3 (blood brain barrier) and HEK293 (kidney) were placed in hypoxic (1% oxygen), ischemic (1% oxygen, no serum or glucose) or normoxic conditions (20% oxygen) for 24 hours prior to RNA extraction. Quantitative polymerase chain reaction (qPCR) was performed with two sets of primers for EPO, the first set was targeting intron inclusion with each primer in a separate exon (intron primers) and the second set was targeting a region that spans two exons, which would not amplify if introns were present (non‐introns). Relative quantification for the level of expression was determined by qPCR using β‐actin and YWHAZ as reference genes for normalization purposes. Statistical analysis of transcript expression and the ratio between intron and non‐intron detecting primer sets were determined using analysis of variance (ANOVA). Results EPO mRNA level was increased in HTB16, CHP212 and HEK293, but not in hCMEC/D3 cell lines in response to ischemia, but not hypoxia. However, when the ration of intron vs. non‐intron expression level was determined a trend towards more intron retaining transcripts in HTB16, CHP212 and hCMEC/D3 but not in HEK293 was observed, suggesting an induction for the expression of this gene under this ischemic condition. Conclusions Intron retention mechanism in EPO is taking place in brain‐derived cell lines, but not in kidney‐derived cells. These results indicate a tissue specificity of intron retention mechanism that could regulate differential expression level of EPO under different physiological and stressful conditions. These findings highlight the complexity of EPO regulation. Hence, intron retention must be considered when examining the expression of a gene to better understand expression in ischemic disease models, such as MI or stroke. Support or Funding Information Natural Sciences and Engineering Research Council of Canada (NSERC)

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicRNA Research and Splicing→French-language works237,207→