A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion

Abstract

Visible phenotypes based on locomotion and posture have played a critical role in understanding the molecular basis of behavior and development in Caenorhabditis elegans and other model organisms. However, it is not known whether these human-defined features capture the most important aspects of behavior for phenotypic comparison or whether they are sufficient to discover new behaviors. Here we show that four basic shapes, or eigenworms, previously described for wild-type worms, also capture mutant shapes, and that this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiased way. By measuring the distance between each individual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to compare mutants to wild type and to each other. This analysis has revealed phenotypes not previously detected by real-time observation and has allowed clustering of mutants into related groups. Behavioral motifs provide a compact and intuitive representation of behavioral phenotypes.

Fig. 1.

The same four basic shapes, or eigenworms, capture both wild-type and mutant postures. (A) Worms crawling freely on a bacterial lawn on an agar pad can be segmented and accurately skeletonized (outline and midline; color indicates curvature). The green dot indicates the worm’s head, and the red dot indicates the vulval side. (B) The four wild-type eigenworms are shown as thick red lines. Eigenworms derived from 307 mutant strains (gray lines) are similar to the wild type. (C) By projecting worm shapes onto the four-dimensional basis formed by the eigenworms, a sequence of behavior can be compactly represented as a four-channel time series. The images above the time series show the worm posture at the times indicated by the red vertical lines. The blue dots indicate the worm’s head. (D) The rmsd between the raw worm shape from the skeletonization and the worm shape reconstructed using just the four eigenworms for 7,008 individuals. As more eigenworms (1–4) are used, the fit improves. The rmsd distribution for the wild-type data alone is shown in purple on the left. The fit to all of the mutant data are comparable, as can be seen more clearly in the Inset where the distributions have been rescaled.

Fig. 2.

Unsupervised discovery of behavioral motifs. Repetitive subsequences are identified by discovering time-series motifs, which are the best-matching subsequences of a given length. In the sample time-series shown in A, the best-matching subsequences are shown in red and blue and overlaid on the Right. (B) Fourteen sample motifs ranging from 1.6 to 32 s (40–800 frames) representing diverse but repetitive behaviors. (C) A quantitative phenotypic profile is generated by finding the distance between movies and each element of the sample motif dictionary shown in B. Phenotypic profiles are shown for N2 wild type (green), a hyperactive mutant goa-1(sa734) (blue) (50, 51), and an uncoordinated mutant unc-63(ok1075) (1, 36). For each strain, the lines show the mean distance from each motif ± the SE for a population of worms. goa-1 is significantly closer to the two relatively high-frequency bouts of forward locomotion in motifs 8 and 11 than either N2 or unc-63, consistent with its hyperactivity; likewise, unc-63 is further from the flat posture of motif 2 because it is uncoordinated with a tendency to have higher body curvature.

Fig. 3.

Phenotypic association network. Nodes are mutant strains, and edges show phenotypic connections. Edge transparency indicates the frequency with which two strains cluster together after resampling from the data with replacement (frequently clustering strains are connected by dark edges). The network layout is determined using spring embedding with edge weights determined by the inverse phenotypic distance. Color-coding indicates either known phenotypic classes or molecular pathway families. (Inset) Network around N2 with increased transparency and smaller node labels for clarity. The DEG/ENaC mutants discussed in Fig. 5 are shown with a red rectangle.

Fig. 4.

Genes involved in monoamine pathways cluster together. In each panel, genes in the indicated class of monoamine signaling are highlighted in red. The mean ± SE of the shortest path connecting each pathway member is listed below the network. Cases where the intragroup distance is significantly smaller than the network overall based on a Wilcoxon rank-sum test are highlighted in red. In the case of serotonin, the results are also shown without cat-4 and bas-1 because they encode molecules required for both serotonin and dopamine biosynthesis. Included genes are as follows: dopamine and receptors: cat-2(e1112), dop-1(vs101), dop-1(vs100); dop-2(vs105), dop-1(vs100); dop-2(vs105); dop-3(vs106), dop-1(vs100); dop-3(vs106), dop-2(vs105), dop-2(vs105); dop-3(vs106), dop-3(vs106), dop-4(tm1392), bas-1(ad446), cat-4(e1141). Serotonin and receptors: bas-1(ad446), cat-4(e1141), ser-1(ok345), ser-4(ok512), ser-5(tm2654), ser-7(tm1325), tph-1(mg280). Tyramine and receptors: tdc-1(n3419), tyra-2(tm1846), tyra-3(ok325), ser-2(pk1357). Octopamine and receptors: octr-1(ok371), tdc-1(n3419), ser-6(tm2146), tbh-1(n3247).

Fig. 5.

Maximally distinguishing behavioral motifs. For each of the indicated comparisons (A–C) the two most-distinguishing behavioral motifs from the dictionary are found using mRMR. The plots on the left show the z-normalized distance between the compared strains and the motif (mean ± SE). The motif amplitudes and the corresponding worm postures are shown in gray. The colored lines show the mean-matching motifs from each of the compared strains. For example, acd-5(ok2657) matches the first motif in A more closely than N2 on average, and this is visible in the amplitude overlay.