Ankrd11 is a chromatin regulator involved in autism that is essential for neural development
Bibliographic record
Abstract
SpringerPlus 2015, 4(Suppl 1):L1 MicroRNAs (miRNAs) are short, 22-25 nucleotide long transcripts that may suppress entire signaling pathways by interacting with the 3'-untranslated region (3'-UTR) of coding mRNA targets, interrupting translation and inducing degradation of these targets.The long 3'-UTRs of brain transcripts compared to other tissues predict important roles for brain miRNAs.Supporting this notion, we found that brain miRNAs co-evolved with their target transcripts, that non-coding pseudogenes with miRNA recognition elements compete with brain coding mRNAs on their miRNA interactions, and that Single Nucleotide Polymorphisms (SNPs) on such pseudogenes are enriched in mental diseases including autism and schizophrenia, but not Alzheimer's disease (AD).Focusing on evolutionarily conserved and primate-specifi c miRNA controllers of cholinergic signaling ('CholinomiRs'), we fi nd modifi ed CholinomiR levels in the brain and/or nucleated blood cells of patients with AD and Parkinson's disease, with treatment-related diff erences in their levels and prominent impact on the cognitive and anti-infl ammatory consequences of cholinergic signals.Examples include the acetylcholinesterase (AChE)-targeted evolutionarily conserved miR-132, whose levels decline drastically in the AD brain.Furthermore, we found that interruption of AChE mRNA's interaction with the primatespecifi c CholinomiR-608 in carriers of a SNP in the AChE's miR-608 binding site induces domino-like eff ects that reduce the levels of many other miR-608 targets.Young, healthy carriers of this SNP express 40% higher brain AChE activity than others, potentially aff ecting the responsiveness to AD's anti-AChE therapeutics, and show elevated trait anxiety, infl ammation and hypertension.Non-coding regions aff ecting miRNA-target interactions in neurodegenerative brains thus merit special attention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".