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Abstract Inhibitors of enzymes that inactivate amine neurotransmitters (dopamine, serotonin), such as catechol-O-methyltransferase (COMT) and monoamine oxidase (MAO), are thought to increase neurotransmitter levels and are widely used to treat Parkinson's disease and psychiatric disorders, yet the role of these enzymes in regulating behavior remains unclear. Here, we investigated the genetic loss of a similar enzyme in the model organismDrosophila melanogaster. Because the enzyme Ebony modifies and inactivates amine neurotransmitters, its loss is assumed to increase neurotransmitter levels, increasing behaviors such as aggression and courtship and decreasing sleep. Indeed,ebonymutants have been described since 1960 as aggressive mutants, though this behavior has not been quantified. Using automated machine learning-based analyses, we quantitatively confirmed thatebonymutants exhibited increased aggressive behaviors such as boxing but also decreased courtship behaviors and increased sleep. Through tissue-specific knockdown, we found thatebony’s role in these behaviors was specific to glia. Unexpectedly, direct measurement of amine neurotransmitters inebonybrains revealed that their levels were not increased but reduced. Thus, increased aggression is the anomalous behavior for this neurotransmitter profile. We further found thatebonymutants exhibited increased aggression only when fighting each other, not when fighting wild-type controls. Moreover, fights betweenebonymutants were less likely to end with a clear winner than fights between controls or fights betweenebonymutants and controls. Inebonyvs. control fights,ebonymutants were more likely to win. Together, these results suggest thatebonymutants exhibit prolonged aggressive behavior only in a specific context, with an equally dominant opponent. 

From birds that preen their feathers to dogs that lick their fur, many animals groom themselves. They do so to stay clean, but routine grooming also has a range of other uses, such as social communication or controlling body temperature. Despite its importance, grooming remains poorly understood; it is especially unclear how this behavior is regulated. Fruit flies could be a good model to study grooming because they are often used in laboratories to look into the genetic and brain mechanisms that control behavior. Flies clean themselves by sweeping their legs over their wings and body, but little is known about how the insects groom ‘naturally’ over long periods of time. This is partly because scientists have had to recognize and classify grooming behavior by eye, which is highly time-consuming. Here, Qiao, Li et al. have created a system to automatically detect grooming behavior in fruit flies over time. First, a camera records the movement of an individual insect. A computer then analyzes the images and picks out general features of the fly’s movement that can help work out what the insect is doing. For example, if a fly is moving its limbs, but not the main part of its body, it is probably grooming itself. Qiao, Li et al. then borrowed an algorithm from an area of computer science known as ‘machine learning’ to teach the computer how to classify each fly’s behavior automatically. The new system successfully recognized grooming behavior in over 90% of cases, and it revealed that fruit flies spend about 13% of their waking life grooming. It also showed that grooming seems to be controlled by two potentially independent internal programs. One program is tied to the internal body clock of the fly, and regulates when the insect grooms during the day. The other commands how long the fly cleans itself, and balances the amount of time spent on grooming with other behaviors. Cleaning oneself is not just important for animals to stay disease-free: it also reflects the general health state of an individual. For example, a loss of grooming is associated with sickness, old age, and, in humans, with mental illness. If scientists can understand how grooming is controlled at the brain and molecular levels, this may give an insight into how these mechanisms relate to diseases. The system created by Qiao, Li et al. could help to make such studies possible.