Genetic modifications associated with ketogenic diet treatment in the BTBR<sup>T+Tf/J</sup> mouse model of autism spectrum disorder
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (ASD) is a prevalent and heterogeneous neurodevelopmental disorder characterized by hallmark behavioral features. The spectrum of disorders that fall within the ASD umbrella encompass a distinct but overlapping symptom complex that likely results from an array of molecular and genetic aberrations rather than a single genetic mutation. The ketogenic diet (KD) is a high-fat low-carbohydrate anti-seizure and neuroprotective diet that has demonstrated efficacy in the treatment of ASD-like behaviors in animal and human studies. METHODS: We investigated changes in mRNA and gene expression in the BTBR mouse model of ASD that may contribute to the behavioral phenotype. In addition, we sought to examine changes in gene expression following KD treatment in BTBR mice. RESULTS: Despite significant behavioral abnormalities, expression changes in BTBR mice did not differ substantially from controls; only 33 genes were differentially expressed in the temporal cortex, and 48 in the hippocampus. Examination of these differentially expressed genes suggested deficits in the stress response and in neuronal signaling/communication. After treatment with the KD, both brain regions demonstrated improvements in ASD deficits associated with myelin formation and white matter development. CONCLUSIONS: Although our study supports many of the previously known impairments associated with ASD, such as excessive myelin formation and impaired GABAergic transmission, the RNAseq data and pathway analysis utilized here identified new therapeutic targets for analysis, such as Vitamin D pathways and cAMP signaling. Autism Res 2017, 10: 456-471. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".