A male-specific QTL for social interaction behavior in mice mapped with automated pattern detection by a hidden Markov model incorporated into newly developed freeware

Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages

Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual's behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11263-024-02118-3.

Social Relationship as a Factor for the Development of Stress Incubation in Adult Mice

While stress reactions can emerge long after the triggering event, it remains elusive how they emerge after a protracted, seemingly stress-free period during which stress incubates. Here, we study the behavioral development in mice isolated after observing an aggressive encounter inflicted upon their pair-housed partners. We developed a spatially resolved fine-scale behavioral analysis and applied it to standard behavioral tests. It reveals that the seemingly sudden behavioral changes developed gradually. These behavioral changes were not observed if the aggressive encounter happened to a stranger mouse, suggesting that social bonding is a prerequisite for stress incubation in this paradigm. This finding was corroborated by hemisphere-specific morphological changes in cortex regions centering at the anterior cingulate cortex, a cognitive and emotional center. Our non-invasive analytical methods to capture informative behavioral details may have applications beyond laboratory animals.

A wild-derived inbred mouse strain, MSM/Ms, provides insights into novel skin tumor susceptibility genes

Cancer is one of the most catastrophic human genetic diseases. Experimental animal cancer models are essential for gaining insights into the complex interactions of different cells and genes in tumor initiation, promotion, and progression. Mouse models have been extensively used to analyze the genetic basis of cancer susceptibility. They have led to the identification of multiple loci that confer, either alone or in specific combinations, an increased susceptibility to cancer, some of which have direct translatability to human cancer. Additionally, wild-derived inbred mouse strains are an advantageous reservoir of novel genetic polymorphisms of cancer susceptibility genes, because of the evolutionary divergence between wild and classical inbred strains. Here, we review mapped Stmm (skintumor modifier of MSM) loci using a Japanese wild-derived inbred mouse strain, MSM/Ms, and describe recent advances in our knowledge of the genes responsible for Stmm loci in the 7,12-dimethylbenz(a)anthracene (DMBA)/12-O-tetradecanoylphorbol-13-acetate (TPA) two-stage skin carcinogenesis model.

Social avoidance behavior in male tree shrews and prosocial behavior in male mice toward unfamiliar conspecifics in the laboratory

Adult male tree shrews vigorously defend against intruding male conspecifics. However, the characteristics of social behavior have not been entirely explored in these males. In this study, male wild-type tree shrews (Tupaia belangeri chinensis) and C57BL/6J mice were first allowed to familiarize themselves with an open-field apparatus. The tree shrews exhibited a short duration of movement (moving) in the novel environment, whereas the mice exhibited a long duration of movement. In the 30 min social preference-avoidance test, target animals significantly decreased the time spent by the experimental tree shrews in the social interaction (SI) zone, whereas experimental male mice exhibited the opposite. In addition, experimental tree shrews displayed a significantly longer latency to enter the SI zone in the second 15 min session (target-present) than in the first 15 min session (target-absent), which was different from that found in mice. Distinct behavioral patterns in response to a conspecific male were also observed in male tree shrews and mice in the first, second, and third 5 min periods. Thus, social behaviors in tree shrews and mice appeared to be time dependent. In summary, our study provides results of a modified social preference-avoidance test designed for the assessment of social behavior in tree shrews. Our findings demonstrate the existence of social avoidance behavior in male tree shrews and prosocial behavior in male mice toward unfamiliar conspecifics. The tree shrew may be a new animal model, which differs from mice, for the study of social avoidance and prosocial behaviors.

Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.

Pcdhβ deficiency affects hippocampal CA1 ensemble activity and contextual fear discrimination

Clustered protocadherins (Pcdhs), a large group of adhesion molecules, are important for axonal projections and dendritic spread, but little is known about how they influence neuronal activity. The Pcdhβ cluster is strongly expressed in the hippocampus, and in vivo Ca²⁺ imaging in Pcdhβ-deficient mice revealed altered activity of neuronal ensembles but not of individual cells in this region in freely moving animals. Specifically, Pcdhβ deficiency increased the number of large-size neuronal ensembles and the proportion of cells shared between ensembles. Furthermore, Pcdhβ-deficient mice exhibited reduced repetitive neuronal population activity during exploration of a novel context and were less able to discriminate contexts in a contextual fear conditioning paradigm. These results suggest that one function of Pcdhβs is to modulate neural ensemble activity in the hippocampus to promote context discrimination.

Autism Spectrum Disorder-Related Syndromes: Modeling with Drosophila and Rodents

Whole exome analyses have identified a number of genes associated with autism spectrum disorder (ASD) and ASD-related syndromes. These genes encode key regulators of synaptogenesis, synaptic plasticity, cytoskeleton dynamics, protein synthesis and degradation, chromatin remodeling, transcription, and lipid homeostasis. Furthermore, in silico studies suggest complex regulatory networks among these genes. Drosophila is a useful genetic model system for studies of ASD and ASD-related syndromes to clarify the in vivo roles of ASD-associated genes and the complex gene regulatory networks operating in the pathogenesis of ASD and ASD-related syndromes. In this review, we discuss what we have learned from studies with vertebrate models, mostly mouse models. We then highlight studies with Drosophila models. We also discuss future developments in the related field.

Pergola: Boosting Visualization and Analysis of Longitudinal Data by Unlocking Genomic Analysis Tools

The growing appetite of behavioral neuroscience for automated data production is prompting the need for new computational standards allowing improved interoperability, reproducibility, and shareability. We show here how these issues can be solved by repurposing existing genomic formats whose structure perfectly supports the handling of time series. This allows existing genomic analysis and visualization tools to be deployed onto behavioral data. As a proof of principle, we implemented the conversion procedure in Pergola, an open source software, and used genomics tools to reproduce results obtained in mouse, fly, and worm. We also show how common genomics techniques such as principal component analysis, hidden Markov modeling, and volcano plots can be deployed on the reformatted behavioral data. These analyses are easy to share because they depend on the scripting of public software. They are also easy to reproduce thanks to their integration within Nextflow, a workflow manager using containerized software.

Measuring Active and Passive Tameness Separately in Mice

Domesticated animals such as dogs and laboratory mice show a high level of tameness, which is important for humans to handle them easily. Tameness has two behavioral components: a reluctance to avoid humans (passive tameness) and a motivation to approach humans (active tameness). To quantify these components in mice, we previously developed behavioral tests for active tameness, passive tameness, and the willingness to stay on a human hand, each designed to be completed within 3 min. The data obtained were used for selective breeding, with a large number of mice analyzed per generation. The active tameness test measures the movement of the mouse toward a human hand and the contact it engages in. The passive tameness test measures the duration of time that a mouse tolerates human touch. In the stay-on-hand test, a mouse is placed on a human hand and touched slowly using the thumb of that hand; the duration of time that the animal remains on the hand is measured. Here, we describe the test set-up and apparatus, explain the procedures, and discuss the appropriate data analysis. Finally, we explain how to interpret the results.

Decrease in endogenous brain allopregnanolone induces autism spectrum disorder (ASD)-like behavior in mice: A novel animal model of ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with core symptoms of social impairments and restrictive repetitive behaviors. Recent evidence has implicated a dysfunction in the GABAergic system in the pathophysiology of ASD. We investigated the role of endogenous allopregnanolone (ALLO), a neurosteroidal positive allosteric modulator of GABA_A receptors, in the regulation of ASD-like behavior in male mice using SKF105111 (SKF), an inhibitor of type I and type II 5α-reductase, a rate-limiting enzyme of ALLO biosynthesis. SKF impaired sociability-related performance, as analyzed by three different tests; i.e., the 3-chamber test and social interaction in the open field and resident-intruder tests, without affecting olfactory function elucidated by the buried food test. SKF also induced repetitive grooming behavior without affecting anxiety-like behavior. SKF had no effect on short-term spatial working memory or long-term fear memory, but enhanced latent learning ability in male mice. SKF-induced ASD-like behavior in male mice was abolished by the systemic administration of ALLO (1mg/kg, i.p.) and methylphenidate (MPH: 2.5mg/kg, i.p.), a dopamine transporter inhibitor. The effects of SKF on brain ALLO contents in male mice were reversed by ALLO, but not MPH. On the other hand, SKF failed to induce ASD-like behavior or a decline in brain ALLO contents in female mice. These results suggest that ALLO regulates episodes of ASD-like behavior by positively modulating the function of GABA_A receptors linked to the dopaminergic system. Moreover, a sex-dependently induced decrease in brain ALLO contents may provide an animal model to study the main features of ASD.

Introduction to Hidden Markov Models and Its Applications in Biology

A number of real-world systems have common underlying patterns among them and deducing these patterns is important for us in order to understand the environment around us. These patterns in some instances are apparent upon observation while in many others especially those found in nature are well hidden. Moreover, the inherent stochasticity in these systems introduces sufficient noise that we need models capable to handling it in order to decipher the underlying pattern. Hidden Markov model (HMM) is a probabilistic model that is frequently used for studying the hidden patterns in an observed sequence or sets of observed sequences. Since its conception in the late 1960s it has been extensively applied in biology to capture patterns in various disciplines ranging from small DNA and protein molecules, their structure and architecture that forms the basis of life to multicellular levels such as movement analysis in humans. This chapter aims at a gentle introduction to the theory of HMM, the statistical problems usually associated with HMMs and their uses in biology.

Automated Estimation of Mouse Social Behaviors Based on a Hidden Markov Model

Recent innovations in sensing and Information and Communication Technology (ICT) have enabled researchers in animal behavior to collect an enormous amount of data. Consequently, the development of an automated system to substitute for some of the observations and analyses that are performed currently by expert researchers is becoming a crucial issue so that the vast amount of accumulated data can be processed efficiently. For this purpose, we introduce a process for the automated classification of the social interactive status of two mice in a square field on the basis of a Hidden Markov model (HMM). We developed two models: one for the classification of two states, namely, indifference and interaction, and the other for three states, namely, indifference, sniffing, and following. The HMM was trained with data from 50 pairs of mice as provided by expert human observers. We measured the performance of the HMM by determining its rate of concordance with human observation. We found that sniffing behavior was segmented well by the HMM; however, following behavior was not segmented well by the HMM in terms of percentage concordance. We also developed software called DuoMouse, an automated system for the classification of social interactive behavior of mice, that was based on the HMM. Finally, we compared two implementations of the HMM that were based on a histogram and a Gaussian mixture model.

Video tracking analysis of behavioral patterns during estrus in goats

Here, we report a new method for measuring behavioral patterns during estrus in goats based on video tracking analysis. Data were collected from cycling goats, which were in estrus (n = 8) or not in estrus (n = 8). An observation pen (2.5 m × 2.5 m) was set up in the corner of the female paddock with one side adjacent to a male paddock. The positions and movements of goats were tracked every 0.5 sec for 10 min by using a video tracking software, and the trajectory data were used for the analysis. There were no significant differences in the durations of standing and walking or the total length of movement. However, the number of approaches to a male and the duration of staying near the male were higher in goats in estrus than in goats not in estrus. The proposed evaluation method may be suitable for detailed monitoring of behavioral changes during estrus in goats.

Toward a science of computational ethology

The new field of “Computational Ethology” is made possible by advances in technology, mathematics, and engineering that allow scientists to automate the measurement and the analysis of animal behavior. We explore the opportunities and long-term directions of research in this area.