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Record W4230858391 · doi:10.3410/f.10954956.11905059

Faculty Opinions recommendation of Transcriptomic analysis of autistic brain reveals convergent molecular pathology.

2011· dataset· en· W4230858391 on OpenAlexfundaboutno aff
Andrew Singleton, José Brás

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2011
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchGenome CanadaOntario GenomicsNational Institute of Mental HealthOntario Genomics InstituteAutism Speaks
KeywordsAutismTranscriptomeGenome-wide association studyNeuroscienceNeurodevelopmental disorderAutism spectrum disorderBiologyAlternative splicingGeneticsGeneRNA splicingExonPsychologyGene expressionSingle-nucleotide polymorphismPsychiatryRNA

Abstract

fetched live from OpenAlex

Autism spectrum disorder (ASD) is a common, highly heritable neurodevelopmental condition characterized by marked genetic heterogeneity. Thus, a fundamental question is whether autism represents an aetiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain. Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1 (also known as FOX1), and a module enriched for immune genes and glial markers. Using high-throughput RNA sequencing we demonstrate dysregulated splicing of A2BP1-dependent alternative exons in the ASD brain. Moreover, using a published autism genome-wide association study (GWAS) data set, we show that the neuronal module is enriched for genetically associated variants, providing independent support for the causal involvement of these genes in autism. In contrast, the immune-glial module showed no enrichment for autism GWAS signals, indicating a non-genetic aetiology for this process. Collectively, our results provide strong evidence for convergent molecular abnormalities in ASD, and implicate transcriptional and splicing dysregulation as underlying mechanisms of neuronal dysfunction in this disorder. PMID: 21614001 Funding information This work was supported by: CIHR, Canada NIMH NIH HHS, United States Grant ID: R01 MH081754-02 NIMH NIH HHS, United States Grant ID: R01 MH094714 NIMH NIH HHS, United States Grant ID: R01 MH081754-04 NIMH NIH HHS, United States Grant ID: R37MH060233 NIMH NIH HHS, United States Grant ID: R01 MH081754 NIMH NIH HHS, United States Grant ID: R01 MH081754-05 NIMH NIH HHS, United States Grant ID: R37 MH060233-12 NIMH NIH HHS, United States Grant ID: R01 MH081754-03 NIMH NIH HHS, United States Grant ID: R37 MH060233 NIMH NIH HHS, United States Grant ID: R37 MH060233-11 NIMH NIH HHS, United States Grant ID: 5R01MH081754-03 NIMH NIH HHS, United States Grant ID: R01 MH081754-01 More Less keyboard_arrow_down

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.7150.472

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 head0.027
GPT teacher head0.340
Teacher spread0.312 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2011
Admission routes2
Has abstractyes

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