Transcriptional Response of SK-N-AS Cells to Methamidophos
This work provides insights into cellular mechanisms affected by acetylcholine esterase inhibitors, with incremental methodological improvements in transcriptomics analysis.
The study analyzed the transcriptional response of SK-N-AS cells to methamidophos exposure using machine learning, identifying upregulated processes like unfolded protein response and inferring causal networks among key transcripts.
Transcriptomics response of SK-N-AS cells to methamidophos (an acetylcholine esterase inhibitor) exposure was measured at 10 time points between 0.5 and 48 h. The data was analyzed using a combination of traditional statistical methods and novel machine learning algorithms for detecting anomalous behavior and infer causal relations between time profiles. We identified several processes that appeared to be upregulated in cells treated with methamidophos including: unfolded protein response, response to cAMP, calcium ion response, and cell-cell signaling. The data confirmed the expected consequence of acetylcholine buildup. In addition, transcripts with potentially key roles were identified and causal networks relating these transcripts were inferred using two different computational methods: Siamese convolutional networks and time warp causal inference. Two types of anomaly detection algorithms, one based on Autoencoders and the other one based on Generative Adversarial Networks (GANs), were applied to narrow down the set of relevant transcripts.