‘The Tired Brain’, one of our former articles highlighted the urgency to tackle the issue of depression brought on by sleep deprivation. We, be it within the science community, or outside are becoming increasingly conscious of the prevalence of mental disorders. This has provoked many researchers to explain comprehensively, the underlying mechanisms of such disorders, in our fight to manage these conditions.
On that note, in a recent article in Cell, researchers have reported promising findings about serotonin – a neurotransmitter heavily associated with depression and many other mental disorders.
For some context, serotonin, or 5-HT is a neurotransmitter which plays an important role in regulating emotions and motor skills. It is often considered a natural mood stabiliser and influences patterns of sleep, eating and digestion. A dysregulation of serotonin is often experienced as symptoms of depression. These symptoms are attended using antidepressants (e.g., selective serotonin reuptake inhibitors – SSRIs) that target the regulation of this neurotransmitter within the central nervous system. On the downside, there has been some difficulty in fully assessing the dynamics of serotonin transmission as well as widespread inability to measure the activity of serotonin; in turn leading to severe impacts in the development of treatment for dysregulation. As regulation of serotonin is vital to maintain an individual’s wellbeing, effective treatment has become the the primary concern among researchers.
Funded by the NIH’s Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative, the purpose of this research was to offer an alternative understanding of the brain under both healthy and disease conditions. Using artificial intelligence technology, Unger et. al. were able to reconstruct a bacterial protein to facilitate real-time tracking of serotonin in the brain.
In this study, the researchers transformed a nutrient grabbing bacterial protein into a sensitive, selective sensor for serotonin which lights up in its presence. The researchers used artificial intelligence to redesign the protein OpuBC: previously used to track acetylcholine. Using machine learning algorithms, they came up with 250,000 new designs of the protein. After 3 rounds of testing, they settled on a final design. This new sensor was found to exclusively detect serotonin at different levels in the brain.
These findings were tested on mice brain slices and it was found that they successfully responded to serotonin signals in the synaptic gap – where communications between neurons take place. It was noted that the sensor was successful in detecting alterations in the levels of serotonin following the intake of serotonin altering drugs – such as cocaine, MDMA and antidepressants.
Another important finding was that the researchers were able to recognise the effects of serotonin during sleep through mice studies. They detected a decrease in levels of serotonin during sleep, compared to when they were awake. They also found a further drop when the mice entered R.E.M sleep (deep sleep).
These findings make us rather hopeful as they are the first to track the changes in serotonin levels with such precision. The researchers are of the view that the availability of this sensor will help us better understand the crucial role serotonin plays in informing mental disorders. They also wish to advance the development of effective treatments for serotonin dysregulation.
Original Source: Unger, E.K. et al., 2020. Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning. Cell, 183(7).
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Edited by Malavika Ramanand