Serotonin has been implicated in the regulation of a wide range of brain functions involving alternative behavioral states, including the control of mood, aggression, sex, and sleep. Here, we report that in the nematode Caenorhabditis elegans, serotonin controls a switch between two distinct, on/off states of egg-laying behavior. Through quantitative analysis of the temporal pattern of egg-laying events, we determined that egg laying can be modeled as a novel random process, in which animals fluctuate between discrete behavioral states: an active state, during which eggs are laid in clusters, and an inactive state, during which eggs are retained. Single-cell ablation experiments indicate that two pairs of motor neurons, HSNL/HSNR and VC4/VC5, can induce the active phase by releasing serotonin. These neurons also release acetylcholine, which appears to trigger individual egg-laying events within the active phase. Genetic experiments suggest that determination of the behavioral states observed for C. elegans egg laying may be mediated through protein kinase C-dependent (PKC-dependent) modulation of voltage-gated calcium channels.

Chronic exposure to nicotine leads to long-term changes in both the abundance and activity of nicotinic acetylcholine receptors, processes thought to contribute to nicotine addiction. We have found that in Caenorhabditis elegans, prolonged nicotine treatment results in a long-lasting decrease in the abundance of nicotinic receptors that control egg-laying. In naive animals, acute exposure to cholinergic agonists led to the efficient stimulation of egg-laying, a response mediated by a nicotinic receptor functionally expressed in the vulval muscle cells. Overnight exposure to nicotine led to a specific and long-lasting change in egg-laying behavior, which rendered the nicotine-adapted animals insensitive to simulation of egg-laying by the nicotinic agonist and was accompanied by a promoter-independent reduction in receptor protein levels. Mutants defective in the gene tpa-1, which encodes a homolog of protein kinase C (PKC), failed to undergo adaptation to nicotine; after chronic nicotine exposure they remained sensitive to cholinergic agonists and retained high levels of receptor protein in the vulval muscles. These results suggest that PKC-dependent signaling pathways may promote nicotine adaptation via regulation of nicotinic receptor synthesis or degradation.

A method of hue measurement, in which an absolute color-naming procedure is utilized, has been applied to spectral stimuli delivered as flashes at 0 degrees, 20 degrees, and 40 degrees eccentricity in an otherwise dark field. The method yields very reliable measures, especially at 0 degrees. Color-naming at 0 degrees differs little from that at 20 degrees, but a marked deterioration of performance occurs between 20 and 40 degrees. This is reflected by a reduction in red and especially green responses, and a lower reliability of the measurements. Additional estimates were also obtained which showed a decrease in measured saturation but increasing reliability of the saturation measurements with increasing eccentricity.

Animal behavior is increasingly being recorded in systematic imaging studies that generate large data sets. To maximize the usefulness of these data there is a need for improved resources for analyzing and sharing behavior data that will encourage re-analysis and method development by computational scientists 1 . However, unlike genomic or protein structural data, there are no widely used standards for behavior data. It is therefore desirable to make the data available in a relatively raw form so that different investigators can use their own representations and derive their own features. For computational ethology to approach the level of maturity of other areas of bioinformatics, we need to address at least three challenges: storing and accessing video files, defining flexible data formats to facilitate data sharing, and making software to read, write, browse, and analyze the data. We have developed an open resource to begin addressing these challenges using worm tracking as a model.To store video files and the associated feature and metadata, we use a Zenodo.org community (an open-access repository for data) that provides durable storage, citability, and supports contributions from other groups. We have also developed a web interface that enables filtering based on feature histograms that can return, for example, fast and curved worms in addition to more standard searches for particular strains or genotypes ( Fig. 1 and http://movement.openworm.org/). The database consists of 14,874 single-worm tracking experiments representing 386 genotypes (building on 9,203 experiments and 305 genotypes in a previous publication 2 ) and includes data from several larval stages as well as ageing data consisting of over 2,700 videos of animals tracked daily from the L4 stage to death. Full resolution videos are available in HDF5 containers that include gzip-compressed video frames, timestamps, worm outline and midline, feature data, and experiment metadata. HDF5 files are compatible with multiple languages including MATLAB, R, Python, and C. We have also developed an HDF5 video reader that allows video playback with adjustable speed and zoom (important when reviewing high-resolution, multi-worm tracking data), as well as toggling of worm segmentation over the original video to verify segmentation accuracy during playback.Secondly, we have defined an interchange format named Worm tracker Commons Object Notation (WCON), to facilitate data sharing and software reuse among groups working on worm behavior. WCON uses the widely supported JSON format to store tracking data as text that is both human and machine readable. It is compatible with single and multi-worm 3 data, at any resolution: from a single point representing worm position over time 4 , to many points representing the high-resolution skeleton of a moving worm 2 . Importantly, it also supports custom feature additions so that individual labs can store their own specific data sets alongside the universal set of basic worm data. WCON readers are available for Python, MATL...

Acid sensing ion channels (ASICs) are members of the diverse family of degenerin/epithelial sodium channels (DEG/ENaCs). They perform a wide range of physiological roles in healthy organisms, including in gut function and synaptic transmission, but also play important roles in disease, as acidosis is a hallmark of painful inflammatory and ischaemic conditions. We performed a screen for acid-sensitivity on all 30 subunits of the C. elegans DEG/ENaC family using Two-Electrode Voltage Clamp (TEVC) in Xenopus oocytes. We found two groups of acidsensing DEG/ENaCs characterised by being inhibited or activated by increasing proton concentrations. Three of these acid-sensitive C. elegans DEG/ENaCs were activated by acidic pH, making them functionally similar to the vertebrate ASICs. We also identified four new members of the acid-inhibited DEG/ENaC group, giving a total of seven additional acidsensitive channels. We observed sensitivity to the anti-hypertensive drug amiloride as well as modulation by the trace element zinc. Acid-sensitive DEG/ENaCs were found to be expressed in both neurons and non-neuronal tissue, highlighting the likely functional diversity of these channels. Our findings provide a framework to exploit the C. elegans channels as models to .