Reduction of Glyoxalase 1 Expression Links Fetal Methylmercury Exposure to Autism Spectrum Disorder Pathogenesis
Bibliographic record
Abstract
Glyoxalase 1 (Glo1) is an essential enzyme to detoxify methylglyoxal (MGO), a cytotoxic byproduct of glycolysis. Accumulating studies have shown an important role of Glo1 in regulating cortical development and neurogenesis, potentially contributing to the pathogenesis of autism spectrum disorder (ASD) when impaired. We have previously shown that prenatal exposure to non-apoptotic low-dose methylmercury (MeHg), an environmental pollutant, induces premature cortical neurogenesis and ASD-like behaviors in a rodent model. In this study, we aimed to determine the underlying molecular mechanisms that mediate prenatal MeHg-induced premature neuronal differentiation and abnormal neurodevelopment. Using single-cell RNA sequencing (scRNA-seq) and real-time quantitative PCR (RT-qPCR), we found that prenatal MeHg exposure at a non-apoptotic dose significantly reduced Glo1 gene expression in embryonic cultured radial glia precursors (RGPs). In cultured RGPs, the knockdown of Glo1 expression increased neuronal production at the expense of the cultured RGPs population, while overexpression of Glo1 restored MeHg-induced neuronal differentiation back to normal levels. Furthermore, we found that co-treatment with both MeHg and multiple MGO scavengers or a CREB inhibitor (iCREB) mitigated MeHg-induced premature neuronal differentiation, reinforcing the role of Glo1 and CREB in mediating MeHg-induced neuronal differentiation. Our findings demonstrate a direct link between MeHg exposure and expression of an ASD risk gene Glo1 in cortical development, supporting the important role of gene–environment interaction in contributing to the etiology of neural developmental disorders, such as ASD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".