Serotonin is a neurochemical that plays a vital role in the way the brain controls our thoughts and emotions. For example, many antidepressants are designed to alter serotonin signals transmitted between neurons. In an article in Cell, Researchers from the National Institute of Health Funders explained how they used advanced genetic engineering methods to convert bacterial proteins to a new research tool that could help monitor serotonin release with greater fidelity than conventional methods.
Preclinical experiments, particularly in mice, have shown that the sensor can detect small, real-time changes in brain serotonin levels during sleep, fear, and social interactions, as well as the effectiveness of new psychoactive drugs. confirmation. The study was funded, in part, by the NIH Brain Research Initiative through the advancement of Innovative Neurotechnologies (BRAIN) that aims to restore our understanding of the brain under healthy conditions and disease.
The study was led by researchers in the laboratory of Lin Tian, Ph.D., principal investigator at the Davis School of Medicine at the University of California. Conventional methods can only detect broad changes in serotonin identification. In this study, the researchers transformed a nutrient-shaped bacterial protein, the Venus flytrap shape into a highly sensitive sensor that ignites into a flower when it captures serotonin.
Previously, scientists in the laboratory of Loren L. Looger, Ph.D., Janelia Research Campus of the Howard Hughes Medical Institute, Ashburn, Virginia, used traditional genetic engineering methods to convert the bacterial protein into a sensor of the neurotransmitter acetylcholine. The protein, called OpuBC, usually secretes the nutrient choline, which has an acetylcholine-like shape. For this study, the Tian lab worked with the team of Dr. Looger and the lab of Viviana Gradinaru, Ph.D., Caltech, Pasadena, California, to show that they needed the additional help of artificial intelligence to completely remodel OpuBC as a serotonin receptor.
The researchers used machine learning algorithms to help a computer come up with 250,000 new designs. After three rounds of experimentation, the scientists decided on one. Initial tests suggested that the new sensor would reliably detect serotonin at different levels in the brain while receiving little response to other neurotransmitters or drugs of the same form. Tests in mouse brain chips showed that the sensor responded to serotonin signals transmitted between neurons at synaptic contact points. At the same time, tests on cells in petri vessels suggested that the sensor could effectively monitor changes in these symptoms caused by drugs, including cocaine, MDMA (also known as ecstasy also) and several commonly used antidepressants.
Finally, experiments in mice showed that the sensor could help scientists study serotonin neurotransmission under more natural conditions. For example, the researchers saw an expected increase in serotonin levels when mice were awake and a fall when mice fell asleep. They also saw a greater fall when the mice entered the deepest REM sleep states.
Traditional serotonin monitoring methods would have missed these changes. In addition, the experts observed serotonin levels rise differently in two separate brain fear cycles when mice were warned of foot shock with a ringing bell. In one cycle – the medial prefrontal cortex – the bell stimulated serotonin levels rapidly and high but in the other – the basolateral amygdala – the circulatory system went up to slightly lower levels. In the spirit of the BRAIN Initiative, the researchers expect the sensor to be readily available to other scientists. They hope it will help researchers better understand the vital role that serotonin plays in our daily lives and in many psychological situations.
Source:
NIH / National Institute of Neurological Disorders and Stroke
Magazine Reference:
Unger, EK, et al. (2020) Guided Effect on Selective and Sensitive Serotonin Sensor through Machine Learning. Cell. doi.org/10.1016/j.cell.2020.11.040.