Aggression is an ethologically important social behavior, but excessive aggression can be detrimental to fitness. Social experiences among conspecific individuals reduce aggression in many species, the mechanism of which is largely unknown. We found that loss-of-function mutation of (), a homolog of vertebrate myeloid translocation genes (MTGs), increased aggressiveness only in socially experienced flies and that this could be reversed by neuronal expression of human MTGs. A subpopulation of octopaminergic/tyraminergic neurons labeled by was specifically required for such social experience-dependent suppression of aggression, in both males and females. Cell type-specific transcriptomic analysis of these neurons revealed aggression-controlling genes that are likely downstream of . Our results illustrate both genetic and neuronal mechanisms by which the nervous system suppresses aggression in a social experience-dependent manner, a poorly understood process that is considered important for maintaining the fitness of animals.

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the 'ground truth'). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.

For successful mating, a male animal must execute effective courtship behaviors toward a receptive target sex, which is female. Whether the courtship execution capability and upregulation of courtship toward females are specified through separable sex-determining genetic pathways remains uncharacterized. Here, we found that one of the two sex-determining genes, (), specifies a male-specific neuronal component that serves as an execution mechanism for courtship behavior, whereas () is required for enhancement of courtship behavior toward females. The -dependent courtship execution mechanism includes a specific subclass within a neuronal cluster that co-express and . This cluster contains at least another subclass that is specified cooperatively by both and . Although these neuronal populations can also promote aggressive behavior toward male flies, this capacity requires -dependent mechanisms. Our results uncover how sex-determining genes specify execution capability and female-specific enhancement of courtship behavior through separable yet cooperative neurogenetic mechanisms.

Inter-male aggressive behavior is a prominent sexually dimorphic behavior. Neural circuits that underlie aggressive behavior are therefore likely under the control of sex-determining genes. However, the neurogenetic mechanism that generates sex-specific aggressive behavior remains largely unknown. Here, we found that a neuronal class specified by one of the sex determining genes, (), belongs to the neural circuit that generates male-type aggressive behavior. This neuronal class can promote aggressive behavior independent of another sex determining gene, (), although is involved in ensuring that aggressive behavior is performed only toward males. We also found that three isoforms with different DNA binding domains show a division of labor on male aggressive behaviors. A dominant role of in specifying sex-specific aggressive behavior may underscore a genetic mechanism that allows male-type aggressive behavior to evolve at least partially independently from courtship behavior, which is under different selective pressures.

The importance of sex as a biological variable is being recognized by more and more researchers, including those using as a model organism. Differences between the two sexes are not confined to well-known reproductive behaviors, but include other behaviors and physiological characteristics that are considered "common" to both sexes. It is possible to categorize sexual dimorphisms into "qualitative" and "quantitative" differences, and this review focuses on recent advances in elucidating genetic and neurophysiological basis of both qualitative and quantitative sex differences in behavior. While sex-specific behaviors are often mediated by sexually dimorphic neural circuits, quantitative sexual dimorphism is caused by sex-specific modulation of a common neuronal substrate.

In this review, I discuss current knowledge and outstanding questions on the neuromodulators that influence aggressive behavior of the fruit fly Drosophila melanogaster. I first present evidence that Drosophila exchange information during an agonistic interaction and choose appropriate actions based on this information. I then discuss the influence of several biogenic amines and neuropeptides on aggressive behavior. One striking characteristic of neuromodulation is that it can configure a neural circuit dynamically, enabling one circuit to generate multiple outcomes. I suggest a consensus effect of each neuromodulatory molecule on Drosophila aggression, as well as effects of receptor proteins where relevant data are available. Lastly, I consider neuromodulation in the context of strategic action choices during agonistic interactions. Genetic components of neuromodulatory systems are highly conserved across animals, suggesting that molecular and cellular mechanisms controlling Drosophila aggression can shed light on neural principles governing action choice during social interactions.

Social interactions, such as an aggressive encounter between two conspecific males or a mating encounter between a male and a female, typically progress from an initial appetitive or motivational phase, to a final consummatory phase. This progression involves both changes in the intensity of the animals' internal state of arousal or motivation and sequential changes in their behavior. How are these internal states, and their escalating intensity, encoded in the brain? Does this escalation drive the progression from the appetitive/motivational to the consummatory phase of a social interaction and, if so, how are appropriate behaviors chosen during this progression? Recent work on social behaviors in flies and mice suggests possible ways in which changes in internal state intensity during a social encounter may be encoded and coupled to appropriate behavioral decisions at appropriate phases of the interaction. These studies may have relevance to understanding how emotion states influence cognitive behavioral decisions at higher levels of brain function.

Males of most species are more aggressive than females, but the neural mechanisms underlying this dimorphism are not clear. Here, we identify a neuron and a gene that control the higher level of aggression characteristic of Drosophila melanogaster males. Males, but not females, contain a small cluster of FruM(+) neurons that express the neuropeptide tachykinin (Tk). Activation and silencing of these neurons increased and decreased, respectively, intermale aggression without affecting male-female courtship behavior. Mutations in both Tk and a candidate receptor, Takr86C, suppressed the effect of neuronal activation, whereas overexpression of Tk potentiated it. Tk neuron activation overcame reduced aggressiveness caused by eliminating a variety of sensory or contextual cues, suggesting that it promotes aggressive arousal or motivation. Tachykinin/Substance P has been implicated in aggression in mammals, including humans. Thus, the higher aggressiveness of Drosophila males reflects the sexually dimorphic expression of a neuropeptide that controls agonistic behaviors across phylogeny.

Most odors are perceived to have the same quality over a large concentration range, but the neural mechanisms that permit concentration-invariant olfactory perception are unknown. In larvae of the vinegar fly Drosophila melanogaster, odors are sensed by an array of 25 odorant receptors expressed in 21 olfactory sensory neurons (OSNs). We investigated how subsets of larval OSNs with overlapping but distinct response properties cooperate to mediate perception of a given odorant across a range of concentrations.

Olfaction is generally assumed to be critical for survival because this sense allows animals to detect food and pheromonal cues. Although the ability to sense sex pheromones [1, 2, 3] is likely to be important for insects, the contribution of general odor detection to survival is unknown. We investigated the extent to which the olfactory system confers a survival advantage on Drosophila larvae foraging for food under conditions of limited resources and competition from other larvae.

Odorant receptors (ORs) are thought to act in a combinatorial fashion, in which odor identity is encoded by the activation of a subset of ORs and the olfactory sensory neurons (OSNs) that express them. The extent to which a single OR contributes to chemotaxis behavior is not known. We investigated this question in Drosophila larvae, which represent a powerful genetic system to analyze the contribution of individual OSNs to odor coding.