Pattern- and contrast-dependent visual response in the box jellyfish Tripedalia cystophora

Unique horizontal gaze control in the box jellyfish, Tripedalia cystophora

All known cubozoans, box jellyfish, have a similar visual system. They possess four sensory structures called rhopalia, which carry-six eyes each. Two of these six eyes are true image-forming camera type eyes in several ways similar to vertebrate eyes. The rhopalia hang by a thin flexible stalk and in the distal end, there is a high-density crystal. In an earlier study of the Caribbean species Tripedalia cystophora, we showed that the crystals act as weights ensuring that the rhopalia are always upright no matter the orientation of the medusa and the vertical part of the visual field of the eyes thus kept relatively constant. Here we have examined the horizontal part of the visual field under different experimental conditions including different visual environments. We find that the horizontal gaze direction is largely controlled by the anatomy of the rhopalium and rhopalial stalk, similar to what has previously been shown for the vertical gaze direction. In a vertically oriented medusa, the rhopalia are kept with a 90° angle between them with the lower lens eyes (LLE) pointing inwards. This 90° shift is kept in horizontally swimming medusa, resulting in the left LLE gazing right, the right gazing left, the bottom gazing orally (backwards compared to swimming direction), and the top LLE gazing aborally (forwards compared to swimming direction). The light environment was manipulated to test if the visual input influences this seemingly strict horizontal gaze direction but even in complete darkness there is tight mechanistic control.

Neuropeptide expression in the box jellyfish Tripedalia cystophora-New insights into the complexity of a "simple" nervous system

Box jellyfish have an elaborate visual system and perform advanced visually guided behaviors. However, the rhopalial nervous system (RNS), believed to be the main visual processing center, only has 1000 neurons in each of the four eye carrying rhopalia. We have examined the detailed structure of the RNS of the box jellyfish Tripedalia cystophora, using immunolabeling with antibodies raised against four putative neuropeptides (T. cystophora RFamide, VWamide, RAamide, and FRamide). In the RNS, T. cystophora RF-, VW-, and RAamide antibodies stain sensory neurons, the pit eyes, the neuropil, and peptide-specific subpopulations of stalk-associated neurons and giant neurons. Furthermore, RFamide ir+ neurites are seen in the epidermal stalk nerve, whereas VWamide antibodies stain the gastrodermal stalk nerve. RFamide has the most widespread expression including in the ring and radial nerves, the pedalium nerve plexus, and the tentacular nerve net. RAamide is the putative neurotransmitter in the motor neurons of the subumbrellar nerve net, and VWamide is a potential marker for neuronal differentiation as it is found in subpopulations of undifferentiated cells both in the rhopalia and in the bell. The results from the FRamide antibodies were not included as only few cells were stained, and in an unreproducible way. Our studies show hitherto-unseen details of the nervous system of T. cystophora and allowed us to identify specific functional groups of neurons. This identification is important for understanding visual processing in the RNS and enables experimental work, directly addressing the role of the different neuropeptides in vision.

Visual Perception and the Emergence of Minimal Representation

There is a long-lasting quest of demarcating a minimally representational behavior. Based on neurophysiologically-informed behavioral studies, we argue in detail that one of the simplest cases of organismic behavior based on low-resolution spatial vision–the visually-guided obstacle avoidance in the cubozoan medusa Tripedalia cystophora–implies already a minimal form of representation. We further argue that the characteristics and properties of this form of constancy-employing structural representation distinguish it substantially from putative representational states associated with mere sensory indicators, and we reply to some possible objections from the liberal representationalists camp by defending and qualitatively demarcating the minimal nature of our case. Finally, we briefly discuss the implications of our thesis within a naturalistic framework.

De novo transcriptome assembly of the cubomedusa Tripedalia cystophora, including the analysis of a set of genes involved in peptidergic neurotransmission

BACKGROUND

The phyla Cnidaria, Placozoa, Ctenophora, and Porifera emerged before the split of proto- and deuterostome animals, about 600 million years ago. These early metazoans are interesting, because they can give us important information on the evolution of various tissues and organs, such as eyes and the nervous system. Generally, cnidarians have simple nervous systems, which use neuropeptides for their neurotransmission, but some cnidarian medusae belonging to the class Cubozoa (box jellyfishes) have advanced image-forming eyes, probably associated with a complex innervation. Here, we describe a new transcriptome database from the cubomedusa Tripedalia cystophora.

RESULTS

Based on the combined use of the Illumina and PacBio sequencing technologies, we produced a highly contiguous transcriptome database from T. cystophora. We then developed a software program to discover neuropeptide preprohormones in this database. This script enabled us to annotate seven novel T. cystophora neuropeptide preprohormone cDNAs: One coding for 19 copies of a peptide with the structure pQWLRGRFamide; one coding for six copies of a different RFamide peptide; one coding for six copies of pQPPGVWamide; one coding for eight different neuropeptide copies with the C-terminal LWamide sequence; one coding for thirteen copies of a peptide with the RPRAamide C-terminus; one coding for four copies of a peptide with the C-terminal GRYamide sequence; and one coding for seven copies of a cyclic peptide, of which the most frequent one has the sequence CTGQMCWFRamide. We could also identify orthologs of these seven preprohormones in the cubozoans Alatina alata, Carybdea xaymacana, Chironex fleckeri, and Chiropsalmus quadrumanus. Furthermore, using TBLASTN screening, we could annotate four bursicon-like glycoprotein hormone subunits, five opsins, and 52 other family-A G protein-coupled receptors (GPCRs), which also included two leucine-rich repeats containing G protein-coupled receptors (LGRs) in T. cystophora. The two LGRs are potential receptors for the glycoprotein hormones, while the other GPCRs are candidate receptors for the above-mentioned neuropeptides.

CONCLUSIONS

By combining Illumina and PacBio sequencing technologies, we have produced a new high-quality de novo transcriptome assembly from T. cystophora that should be a valuable resource for identifying the neuronal components that are involved in vision and other behaviors in cubomedusae.

Vision Made Easy: Cubozoans Can Advance Our Understanding of Systems-Level Visual Information Processing

Animals relying on vision as their main sensory modality reserve a large part of their central nervous system to appropriately navigate their environment. In general, neural involvement correlates to the complexity of the visual system and behavioural repertoire. In humans, one third of the available neural capacity supports our single-chambered general-purpose eyes, whereas animals with less elaborate visual systems need less computational power, and generally have smaller brains, and thereby lack in visual behaviour. As a consequence, both traditional model animals (mice, zebrafish, and flies) and more experimentally tractable animals (Hydra, Planaria, and C. elegans) cannot contribute to our understanding of systems-level visual information processing-a Goldilocks case of too big and too small.However, one animal, the box jellyfish Tripedalia cystophora, possesses a rather complex visual system, displays multiple visual behaviours, yet processes visual information by means of a relatively simple central nervous system. This-just right-model system could not only provide information on how visual stimuli are processed through distinct combinations of neural circuitry but also provide a processing algorithm for extracting specific information from a complex visual scene.