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ABSTRACT Over the course of hundreds of millions of years, biomineralization has evolved independently many times across all kingdoms of life. Among animals, the phylum Mollusca displays a remarkable diversity in biomineral structures, particularly the molluscan shell, which varies greatly in shape, size, pigmentation, and patterning. Shell matrix proteins (SMPs) are key components of these shells, and are thought to drive the precipitation of calcium carbonate minerals and influence shell morphology. However, this structure‐function relationship has rarely been studied directly because tools for knocking out genes did not exist in molluscs until recently. In this study, we report the first successful use of CRISPR/Cas9 gene editing to target an SMP in gastropod molluscs. Using the emerging model gastropodCrepidula atrasolea, we generated knockouts of theSMP1gene. Successful gene editing was confirmed by Sanger and MiSeq sequencing, and loss ofSMP1expression was validated through high‐content imaging of crispant embryos. This study establishesC. atrasoleaas a valuable model for investigating the genetic basis of shell formation and provides a framework for applying CRISPR/Cas9 technology in other molluscan species. Our approach will enable future studies to thoroughly test the role of SMPs in shaping the diverse array of molluscan shell structures. 

Behavioral phenotyping of model organisms has played an important role in unravelling the complexities of animal behavior. Techniques for classifying behavior often rely on easily identified changes in posture and motion. However, such approaches are likely to miss complex behaviors that cannot be readily distinguished by eye (e.g., behaviors produced by high dimensional dynamics). To explore this issue, we focus on the model organism Caenorhabditis elegans , where behaviors have been extensively recorded and classified. Using a dynamical systems lens, we identify high dimensional, nonlinear causal relationships between four basic shapes that describe worm motion (eigenmodes, also called “eigenworms”). We find relationships between all pairs of eigenmodes, but the timescales of the interactions vary between pairs and across individuals. Using these varying timescales, we create “interaction profiles” to represent an individual’s behavioral dynamics. As desired, these profiles are able to distinguish well-known behavioral states: i.e., the profiles for foraging individuals are distinct from those of individuals exhibiting an escape response. More importantly, we find that interaction profiles can distinguish high dimensional behaviors among divergent mutant strains that were previously classified as phenotypically similar. Specifically, we find it is able to detect phenotypic behavioral differences not previously identified in strains related to dysfunction of hermaphrodite-specific neurons. 

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans , body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded ‘attractor’ of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach—defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error—may be of broad interest and relevance to behavioural researchers working with video-derived time series.