Back

Dynamic Regulation of Cholesterol Metabolism Genes in Dopaminergic Neurons following Methamphetamine Treatment as Revealed by Single-Nucleus RNA Sequencing

Sun, W.; Zhang, Y.-J.; Roy, S.; Avram, S. W.; Sun, C.-Y.; Petros, T. J.; Amara, S. G.

2025-07-31 genomics
10.1101/2025.07.28.667272 bioRxiv
Show abstract

Methamphetamine profoundly alters the function of midbrain dopaminergic neurons, yet the molecular mechanisms underlying these effects are not fully understood. Using single-nucleus RNA sequencing, we found that acute methamphetamine exposure leads to a marked up-regulation of cholesterol metabolism genes in dopaminergic neurons of the ventral tegmental area and substantia nigra--a response that was much less pronounced in astrocytes and largely absent in other cell types. Further analysis using a ribosome tagging strategy combined with RNA sequencing revealed that amphetamine, a structurally similar psychostimulant, induced similar gene expression changes, whereas methylphenidate, a structurally distinct psychostimulant, did not, highlighting drug-specific transcriptional responses. Notably, repeated methamphetamine exposure resulted in down-regulation of cholesterol metabolism genes in dopaminergic neurons. Interestingly, we also observed that, compared to neighboring cell types, dopaminergic neurons are highly enriched in genes encoding cholesterol biosynthesis enzymes, including the rate-limiting enzyme Hmgcr, and key regulators Srebf2 and Insig1, challenging the prevailing view that neurons rely mainly on astrocyte-derived cholesterol. In summary, our study highlights dynamic changes in cholesterol metabolism in dopaminergic neurons in response to amphetamines and uncovers the potential importance of cholesterol homeostasis for dopaminergic neuron function.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.