A quantitative description of animal social behaviour is informative for behavioural biologists and clinicians developing drugs to treat social disorders. Social interaction in a group of animals has been difficult to measure because behaviour develops over long periods of time and requires tedious manual scoring, which is subjective and often non-reproducible. Computer-vision systems with the ability to measure complex social behaviour automatically would have a transformative impact on biology. Here, we present a method for tracking group-housed mice individually as they freely interact over multiple days. Each mouse is bleach-marked with a unique fur pattern. The patterns are automatically learned by the tracking software and used to infer identities. Trajectories are analysed to measure behaviour as it develops over days, beyond the range of acute experiments. We demonstrate how our system may be used to study the development of place preferences, associations and social relationships by tracking four mice continuously for five days. Our system enables accurate and reproducible characterisation of wild-type mouse social behaviour and paves the way for high-throughput long-term observation of the effects of genetic, pharmacological and environmental manipulations.

Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single steroid-responsive gene and follow dynamic synthesis of RNA from the activated locus. DOI:http://dx.doi.org/10.7554/eLife.00750.001.

An often-overlooked aspect of neural plasticity is the plasticity of neuronal composition, in which the numbers of neurons of particular classes are altered in response to environment and experience. The Drosophila brain features several well-characterized lineages in which a single neuroblast gives rise to multiple neuronal classes in a stereotyped sequence during development [1]. We find that in the intrinsic mushroom body neuron lineage, the numbers for each class are highly plastic, depending on the timing of temporal fate transitions and the rate of neuroblast proliferation. For example, mushroom body neuroblast cycling can continue under starvation conditions, uncoupled from temporal fate transitions that depend on extrinsic cues reflecting organismal growth and development. In contrast, the proliferation rates of antennal lobe lineages are closely associated with organismal development, and their temporal fate changes appear to be cell cycle-dependent, such that the same numbers and types of uniglomerular projection neurons innervate the antennal lobe following various perturbations. We propose that this surprising difference in plasticity for these brain lineages is adaptive, given their respective roles as parallel processors versus discrete carriers of olfactory information.

Secretion systems require high-fidelity mechanisms to discriminate substrates among the vast cytoplasmic pool of proteins. Factors mediating substrate recognition by the type VI secretion system (T6SS) of Gram-negative bacteria, a widespread pathway that translocates effector proteins into target bacterial cells, have not been defined. We report that haemolysin coregulated protein (Hcp), a ring-shaped hexamer secreted by all characterized T6SSs, binds specifically to cognate effector molecules. Electron microscopy analysis of an Hcp-effector complex from Pseudomonas aeruginosa revealed the effector bound to the inner surface of Hcp. Further studies demonstrated that interaction with the Hcp pore is a general requirement for secretion of diverse effectors encompassing several enzymatic classes. Though previous models depict Hcp as a static conduit, our data indicate it is a chaperone and receptor of substrates. These unique functions of a secreted protein highlight fundamental differences between the export mechanism of T6 and other characterized secretory pathways.

SUMMARY: Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family members in sequence databases and to create potentially large multiple sequence alignments. Version 1.1 of Infernal introduces a new filter pipeline for RNA homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods. This enables \~{}100-fold acceleration over the previous version and \~{}10 000-fold acceleration over exhaustive non-filtered CM searches. AVAILABILITY: Source code, documentation and the benchmark are downloadable from http://infernal.janelia.org. Infernal is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Documentation includes a user’s guide with a tutorial, a discussion of file formats and user options and additional details on methods implemented in the software. CONTACT: nawrockie@janelia.hhmi.org.

Understanding how glucose transporter isoform 4 (GLUT4) redistributes to the plasma membrane during insulin stimulation is a major goal of glucose transporter research. GLUT4 molecules normally reside in numerous intracellular compartments, including specialized storage vesicles and early/recycling endosomes. It is unclear how these diverse compartments respond to insulin stimulation to deliver GLUT4 molecules to the plasma membrane. For example, do they fuse with each other first or remain as separate compartments with different trafficking characteristics? Our recent live cell imaging studies are helping to clarify these issues. Using Rab proteins as specific markers to distinguish between storage vesicles and endosomes containing GLUT4, we demonstrate that it is primarily internal GLUT4 storage vesicles (GSVs) marked by Rab10 that approach and fuse at the plasma membrane and GSVs don't interact with endosomes on their way to the plasma membrane. These new findings add strong support to the model that GSV release from intracellular retention plays a major role in supplying GLUT4 molecules onto the PM under insulin stimulation.

We describe an implementation of maximum likelihood classification for single particle electron cryo-microscopy that is based on the FREALIGN software. Particle alignment parameters are determined by maximizing a joint likelihood that can include hierarchical priors, while classification is performed by expectation maximization of a marginal likelihood. We test the FREALIGN implementation using a simulated dataset containing computer-generated projection images of three different 70S ribosome structures, as well as a publicly available dataset of 70S ribosomes. The results show that the mixed strategy of the new FREALIGN algorithm yields performance on par with other maximum likelihood implementations, while remaining computationally efficient.

In vivo imaging applications typically require carefully balancing conflicting parameters. Often it is necessary to achieve high imaging speed, low photo-bleaching, and photo-toxicity, good three-dimensional resolution, high signal-to-noise ratio, and excellent physical coverage at the same time. Light-sheet microscopy provides good performance in all of these categories, and is thus emerging as a particularly powerful live imaging method for the life sciences. We see an outstanding potential for applying light-sheet microscopy to the study of development and function of the early nervous system in vertebrates and higher invertebrates. Here, we review state-of-the-art approaches to live imaging of early development, and show how the unique capabilities of light-sheet microscopy can further advance our understanding of the development and function of the nervous system. We discuss key considerations in the design of light-sheet microscopy experiments, including sample preparation and fluorescent marker strategies, and provide an outlook for future directions in the field.

BACKGROUND: Drosophila melanogaster adult males perform an elaborate courtship ritual to entice females to mate. fruitless (fru), a gene that is one of the key regulators of male courtship behavior, encodes multiple male-specific isoforms (Fru(M)). These isoforms vary in their carboxy-terminal zinc finger domains, which are predicted to facilitate DNA binding. RESULTS: By over-expressing individual Fru(M) isoforms in fru-expressing neurons in either males or females and assaying the global transcriptional response by RNA-sequencing, we show that three Fru(M) isoforms have different regulatory activities that depend on the sex of the fly. We identified several sets of genes regulated downstream of Fru(M) isoforms, including many annotated with neuronal functions. By determining the binding sites of individual Fru(M) isoforms using SELEX we demonstrate that the distinct zinc finger domain of each Fru(M) isoforms confers different DNA binding specificities. A genome-wide search for these binding site sequences finds that the gene sets identified as induced by over-expression of Fru(M) isoforms in males are enriched for genes that contain the binding sites. An analysis of the chromosomal distribution of genes downstream of Fru(M) shows that those that are induced and repressed in males are highly enriched and depleted on the X chromosome, respectively. CONCLUSIONS: This study elucidates the different regulatory and DNA binding activities of three Fru(M) isoforms on a genome-wide scale and identifies genes regulated by these isoforms. These results add to our understanding of sex chromosome biology and further support the hypothesis that in some cell-types genes with male-biased expression are enriched on the X chromosome.

Animals learn both whether and when a reward will occur. Neural models of timing posit that animals learn the mean time until reward perturbed by a fixed relative uncertainty. Nonetheless, animals can learn to perform actions for reward even in highly variable natural environments. Optimal inference in the presence of variable information requires probabilistic models, yet it is unclear whether animals can infer such models for reward timing. Here, we develop a behavioral paradigm in which optimal performance required knowledge of the distribution from which reward delays were chosen. We found that mice were able to accurately adjust their behavior to the SD of the reward delay distribution. Importantly, mice were able to flexibly adjust the amount of prior information used for inference according to the moment-by-moment demands of the task. The ability to infer probabilistic models for timing may allow mice to adapt to complex and dynamic natural environments.