Behaviors involving the interaction of multiple individuals are complex and frequently crucial for an animal’s survival. These interactions, ranging across sensory modalities, length scales, and time scales, are often subtle and difficult to characterize. Contextual effects on the frequency of behaviors become even more difficult to quantify when physical interaction between animals interferes with conventional data analysis, e.g. due to visual occlusion. We introduce a method for quantifying behavior in fruit fly interaction that combines high-throughput video acquisition and tracking of individuals with recent unsupervised methods for capturing an animal’s entire behavioral repertoire. We find behavioral differences between solitary flies and those paired with an individual of the opposite sex, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between two individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship.
Alternative pre-mRNA splicing (AS) is a critical regulatory mechanism that operates extensively in the nervous system to produce diverse protein isoforms. Fruitless AS isoforms have been shown to influence male courtship behavior, but the underlying mechanisms are unknown. Using genome-wide approaches and quantitative behavioral assays, we show that the P-element somatic inhibitor (PSI) and its interaction with the U1 small nuclear ribonucleoprotein complex (snRNP) control male courtship behavior. PSI mutants lacking the U1 snRNP-interacting domain (PSIΔAB mutant) exhibit extended but futile mating attempts. The PSIΔAB mutant results in significant changes in the AS patterns of ∼1,200 genes in the Drosophila brain, many of which have been implicated in the regulation of male courtship behavior. PSI directly regulates the AS of at least one-third of these transcripts, suggesting that PSI-U1 snRNP interactions coordinate the behavioral network underlying courtship behavior. Importantly, one of these direct targets is fruitless, the master regulator of courtship. Thus, PSI imposes a specific mode of regulatory control within the neuronal circuit controlling courtship, even though it is broadly expressed in the fly nervous system. This study reinforces the importance of AS in the control of gene activity in neurons and integrated neuronal circuits, and provides a surprising link between a pleiotropic pre-mRNA splicing pathway and the precise control of successful male mating behavior.PSI | U1 snRNP | alternative pre-mRNA splicing | male courtship behavior H ow gene regulation modulates neuronal activities leading to cognition and behavior is an important question in biology. Although many behavior-associated genes and neuronal cell types have been identified, a detailed understanding that links the molecular events of gene regulation to specific behaviors is still lacking. Alternative pre-mRNA splicing (AS) is a crucial gene regulatory mechanism that enables a single gene to generate functionally distinct messenger RNA transcripts (mRNAs) and protein products (1). The nervous system makes extensive use of AS to generate diverse and complex neural mRNA expression patterns that determine numerous neuronal cell types and functions (2). AS is regulated by the small nuclear ribonucleoprotein complexes (snRNPs) that compose the spliceosome for intron recognition and removal, as well as a large repertoire of non-snRNP RNA-binding proteins that affect decisions on splice site use (3). This dynamic and complex AS regulatory network modulates diverse neuronal functions, like synaptic transmission and signal processing, hence further impacting higher brain functions, such as cognition and behavioral control (4).The Drosophila KH-domain RNA binding splicing factor P-element somatic inhibitor (PSI) is best known for regulating tissue-specific AS of the Drosophila P-element transposon transcripts to restrict transposition activity to germ-line tissues (5, 6). PSI directly interacts with the U1 snRNP through a 70-aa tandem direct ...
Background Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. Methods We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.
Social behaviors are ubiquitous and crucial to an animal's survival and success. The behaviors an animal performs in a social setting are affected by internal factors, inputs from the environment, and interactions with others. To quantify social behaviors, we need to measure both the stochastic nature of the behavior of isolated individuals and how this behavioral repertoire changes as a function of the environment and interactions between individuals. We probed the behavior of male and female fruit flies in a circular arena as individuals and within all possible pairings. By combining measurements of the animals' position in the arena with an unsupervised analysis of their behaviors, we define the effects of position in the environment and the presence of a partner on locomotion, grooming, singing, and other behaviors that make up an animal's repertoire. We find that geometric context tunes behavioral preference, pairs of animals synchronize their behavioral preferences across shared trials, and paired individuals display signatures of behavioral mimicry.
Autism is noted for both its genotypic and phenotypic diversity. Repetitive action, resistance to environmental change, and motor disruptions vary from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we use advances in computer vision and deep learning to develop a framework for characterizing mouse behavior on multiple time scales using a single popular behavioral assay, the open field test. We observed male and female C57BL/6J mice to develop a dynamic baseline of adaptive behavior over multiple days. We then examined two rodent models of autism, a cerebellum-specific model, L7-Tsc1, and a whole-brain knockout model, Cntnap2. Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics.
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