Opsins are the primary proteins responsible for light detection in animals. Cnidarians (jellyfish, sea anemones, corals) have diverse visual systems that have evolved in parallel with bilaterians (squid, flies, fish) for hundreds of millions of years. Medusozoans (e.g. jellyfish, hydroids) have evolved eyes multiple times, each time independently incorporating distinct opsin orthologs. Anthozoans (e.g. corals, sea anemones,) have diverse light-mediated behaviors and, despite being eyeless, exhibit more extensive opsin duplications than medusozoans. To better understand the evolution of photosensitivity in animals without eyes we increased anthozoan representation in the phylogeny of animal opsins and investigated the large but poorly characterized opsin family in the sea anemone Nematostella vectensis. We analyzed genomic and transcriptomic data from 16 species of cnidarians to generate a large opsin phylogeny (708 sequences) with the largest sampling of anthozoan sequences to date. We identified 29 opsins from N. vectensis (NvOpsins) with high confidence, using transcriptomic and genomic datasets. We found that lineage-specific opsin duplications are common across Cnidaria, with anthozoan lineages exhibiting among the highest numbers of opsins in animals. To establish the putative photosensory function of NvOpsins, we identified canonically conserved protein domains and amino acid sequences essential for opsin function in other animal species. We show high sequence diversity among NvOpsins at sites important for photoreception and transduction, suggesting potentially diverse functions. We further examined the spatiotemporal expression of NvOpsins and found both dynamic expression of opsins during embryonic development and sexually dimorphic opsin expression in adults. These data show that lineage-specific duplication and divergence have led to expansive diversity of opsins in eyeless cnidarians, suggesting opsins from these animals may exhibit novel biochemical functions. The variable expression patterns of opsins in N. vectensis suggest opsin gene duplications allowed for a radiation of unique sensory cell types with tissue- and stage-specific functions. This diffuse network of distinct sensory cell types could be an adaptive solution for varied sensory tasks experienced in distinct life history stages in anthozoans.

Opsin Identification and Phylogenetics (References numbered as in the paper)

Initial N. vectensis opsin sequences were collected from three published datasets [5,6,47]. All potential N. vectensis sequences were aligned in Geneious Prime v.11.0.12, and identical matches and fragments were discarded. BLAST was used with the final set of opsins as bait to search against the Nematostella vectensis Embryogenesis and Regeneration Transcriptomics database (NvERTx) [67], our own reference transcriptome (generated for this study), and two publicly available chromosome level genomes: v2 genome hosted by the Stowers Institute [50,71], and the genome from the Darwin Tree of Life Programme at the Wellcome Sanger Institute (ENA submission accession: ERA9667479) ([51] no associated publication). Hits were added inclusively and combined with a modified alignment including non-redundant sequences from Vöcking et al. 2017, Picciani et al. 2018, and Gornik et al. 2021 opsin phylogenies [5,6,29]. We then added opsin sequences from new anthozoan transcriptome and genome data. We added opsins from 12 species with new transcriptomes, and 3 species from new genomic data (Additional File 3: Table S1). This was done using N. vectensis and Acropora digitifera sequences from ASOI, ASOII, and Cnidops groups as bait for BLAST searches. The top 100 BLAST hits were initially conservatively kept. All sequences were aligned in Geneious using MAFFT v7.450 with default settings [72]. FastTree was used in Geneious to check the tree, removing unannotated outgroups from the multiple cnidarian and N. vectensis BLAST searches. Trimmed (using TrimAl) and untrimmed alignments were generated, but untrimmed sequences generated the best-supported phylogeny and are reported here.

Final maximum likelihood gene trees were constructed using IQtree2 with the following command: iqtree2 -s <alignment.phy> -st AA -nt AUTO -v -m TEST -bb 1000 -alrt 1000 [73] on the Minnesota Supercomputing Institute’s (MSI) High Performance Computing system at the University of Minnesota. The LG+G4 model of protein evolution was auto-calculated from this command, and 1000 ultrafast bootstrap (UFboot) replicates and SH-aLRT tests were used for evidence of support. Clades were considered supported only with >80% SH-aLRT/>95% UFboot support. The tree was rooted based on known outgroups from previous phylogenies. 

Genomic and opsin sequence analysis

To identify distinct opsin paralogs, we used BLAST with transcripts as bait to identify genomic loci, resulting in the identification of 29 opsin genes. We manually identified intron/exon boundaries at all cnidopsins with introns by aligning coding sequences to the genome and translated these to identify their location and phase on the protein. We then aligned the cnidopsin amino acid sequences with a xenopsin and bovine c-opsin in Geneious and mapped exon boundaries manually onto the alignment. Opsin genomic coordinates for both new Nvec genomes [51,74], and previous Nvecv1 scaffold coordinates [75] are found in Additional File 3: Table S2. We used InterPro scan in Geneious to predict the seven transmembrane domains. Using bovine rhodopsin as a reference, we aligned our 29 opsins, Drosophila r-opsin and T. cystophora cnidopsin sequences to identify residues at canonically conserved positions.

A few tandem loci next to NvASOII-8a, NvASOII-8b, NvASOII-9a, NvASOII9b, NvASOII-7, and NvASOII-4 genes were found in the  v2 genome [50] whose nucleotide sequences were nearly identical (>97% similarity). We noticed adjacent non-coding intra- and intergenic regions were also near-identical, suggesting these may be haplotype differences and not real paralogs. One of these adjacent pairs also was split by a section of Ns in the genome (Additional File 4: Fig. S3), an indicator of difficulty with assembly in this region. We compared these trouble spots with the Wellcome-Sanger genome [51] and for each, found only one of the two near-identical pairs, further evidence that these copies are a result of assembly error, while other high-similarity but distinct loci were left in our final opsin count.

RNA-sequencing and analysis

Libraries were generated from two independent spawns each at blastula, gastrula, planula, primary polyp stages, two adult males and two adult females. Adult animals had been kept in normal laboratory conditions at 18°C with a 12-hour light/12-hour dark photoperiod and at least one week since they were last spawned. Live animals were placed directly into Trizol reagent and tissue was homogenized in a microcentrifuge tube with a pestle before performing RNA extraction. Total RNA was extracted from Trizol (Invitrogen), gDNA removed by gDNA cleanup kit (Qiagen), RNA was precipitated using isopropanol and then cleaned up using ethanol precipitation. Libraries were prepared using Kapa Stranded RNA Hyperprep (Illumina), quality control and size selection were performed by the Bauer Core at Harvard University and paired end, 150bp reads were sequenced on an Ilumina NovaSeq.

Raw sequencing libraries were processed for erroneous k-mers and unfixable reads using rCorrector [76]. Adapter sequences were trimmed using Trim Galore (Babraham Bioniformatics, UK), and rRNA reads were mapped to the N. vectensis mitochondrial genome and removed using Bowtie2 [77]. All libraries were combined to generate a single de novo transcriptome assembly using Trinity v2.12.0 [78]. Transcriptome quality was assessed by checking alignment statistics and quantifying BUSCO completeness [79]. We used EvidentialGene to combine gene and transcript evidence from multiple sources and reduce the number of genes in our transcriptome [80]. We used our own transcripts, and three other assemblies [50,67,81], and the coding sequences from v2 genome [50], as input. We used the EvidentialGene output for our final transcriptome. mRNA quantification was done using Kallisto for each of our 12 libraries using our de novo assembled transcriptome [82]. Stages were compared using the time course analysis in Sleuth run in R, and adult libraries were additionally pairwise compared using Sleuth [83]. The data for heatmaps were extracted from the Sleuth object using normalized transcripts per million (TPM) in either the timecourse analysis or male v. female comparison. Heatmaps were made with the heatmap command in gplots package [84] using R v4.2.3 run in RStudio v2022.12.0+353 [85].

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