Background & Summary

Octopus vulgaris is a benthic, neritic species belonging to the class Cephalopoda. It occurs from the coastline to the outer edge of the continental shelf, inhabiting various marine habitats at depths spanning from 0 to 200 m. O. vulgaris is one of the most widely distributed species belonging to the genus, and is an important commercially harvested resource for human consumption. It is found worldwide in temperate and tropical waters1,2,3. Throughout its distribution range, the animal undertakes limited seasonal migrations: mostly found in deep waters in winter and shallow waters in summer.

O. vulgaris is perhaps the most famous and best studied of all octopus species, largely due to the initiative of Professor John Z. Young4,5. Since the late 1940 s, Young carried out at the Stazione Zoologica Anton Dohrn of Napoli (Italy) a systematic analysis of the neural structures underlying behavioural plasticity in this animal6,7. Based on this contribution, the anatomy of O. vulgaris nervous system8 and its physiology and life history4,9,10,11 have been well characterized. It is the phenomenological proximity of behavioral traits and phylogenetic distance in respect to higher vertebrates that guaranteed the short, but wide success of cephalopods5,10. O. vulgaris in particular became a “model of the brain”12,13, and more recently a case for studying the evolution of cognition in invertebrates7,14,15,16,17,18,19. Researchers still use O. vulgaris as an organism to study behavioural and neural plasticity including learning and memory recall5,12,20, regeneration21,22,23,24 and sophisticated cognition7,14,15,16,17,25.

Currently available genomic resources for molluscs are scarce, considering the species abundance and the commercial value of the phylum Mollusca. Publicly available molluscan genomes include a dozen representatives from bivalves, gastropods26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42 and to-date only three cephalopods, namely the California two-spot octopus Octopus bimaculoides43 and, more recently, for Callistoctopus minor44 and Euprymna scolopes45.

Although the first step towards cephalopod genetics was made over 30 years ago46, cephalopod research is only slowly entering the genomics era10,47. Obtaining high quality cephalopod genomes has been impeded due to their large size (e.g., O. bimaculoides: 2.7 Gb; Gregory, 2018 - Animal Genome Size Database, http://www.genomesize.com), heterozygosity and high abundance of repeat regions43,47,48. However, several collaborative genome projects are currently underway for a variety of cephalopod species such as the nautilus, Sepia officinalis, Idiosepius paradoxus and Doryteuthis pealeii.

Cephalopods arose more than 500 Mya and diverged into over 800 current living species with highly diversified life styles and body plans48. Translocations, duplications, exon shuffling and gene conversions occurred within the cephalopod genome during evolution, which might explain the development of different morphological novelties, such as the prehensile arms, the unique jet propulsion system, the ink sac and sophisticated sensory and neural systems49. The analysis of O. bimaculoides genome revealed an extensive expansion of particular gene families, including protocadherins and the C2H2 superfamily of zinc-finger transcription factors43, as well as novel octopus-specific genes expressed in specialized structures such as suckers, skin and brain (for review see also Shigeno et al.18). These genome-level novelties are accompanied by other sophisticated innovations such as extensive RNA editing, particularly in the nervous system cells50,51,52. Furthermore, partial genome sequencing of several cephalopods showed that repeat elements, in particular transposable elements, are abundant53,54. Indeed, the genome of O. bimaculoides revealed that over 45% of the genome is comprised of repetitive elements43.

The study of cephalopod biological innovations10,18,43,55 is driven by the unique scientific value of these animals for evolutionary genomics, neuroscience and cognition7,10,18,25,43,55,56,57,58 which continues the heritage of the discovery of the action potential in the squid giant axon, a seminal contribution to neuroscience59. Furthermore, the phylogenetic relationships within the cephalopods have not yet been fully elucidated and biological research would benefit from more cephalopod genomes60,61.

In line with those previous and current efforts, and to promote data sharing among cephalopod researchers10,47, we present the sequence and draft assembly of the common octopus, Octopus vulgaris, genome. It is noteworthy to report that the two species (i.e., O. vulgaris and O. bimaculoides), although both belonging to the same genus, go through a substantially different life cycle since the paralarval stage is absent in O. bimaculoides62. Therefore, the two species represent different biological and physiological adaptations among closely related species. The genomic sequencing of both octopus species and our online platform to browse these data will allow for future comparative genomics studies, revealing key genomic innovations and facilitating the discovery of the molecular basis of intricate processes such as learning, regeneration and the evolution of complex brains.

Methods

Genomic DNA preparation

An adult male belonging to the species O. vulgaris Cuvier, 1797 (450 g body weight) was caught by fishermen from the Bay of Naples in 20111,2 and immediately humanely-killed63,64. Given the high rate of heterozygosity in marine organisms65,66, tissue from a single individual was used to extract the genomic DNA (to avoid contamination, spermatophores were used). Spermatophores in octopus are stored within the Needham’s sac, structure that was dissected following Chapko and coworkers67. Tissue (124 mg) was used to extract the genomic DNA following the recommended phenol-chloroform extraction protocol by the Beijing Genomics Institute (BGI)-Shenzhen. Briefly, tissue lysis occurred overnight at 56 °C after adding 3.0 ml of lysis buffer containing proteinase K (300 μg; Sigma-Aldrich, Saint Louis, Missouri, United States) and RNase A (100 μg; Sigma-Aldrich, Saint Louis, Missouri, United States). DNA was then extracted with phenol (2X), phenol:chloroform, chloroform and was subsequently precipitated. Genomic DNA was dissolved in TE buffer to reach a final concentration of 1 μg/μl.

Genome sequencing and quality control

A total of four genomic DNA libraries (with different insert sizes: 170, 250, 500 and 800 bp) were constructed following the Illumina library preparation protocols. Briefly, to construct the paired-end libraries DNA was fragmented by Adaptive Focused Acoustics technology (Covaris) and tested via gel-electrophotometry, the fragmented DNA combined with End Repair Mix (20 °C for 30 min). After purification, DNA ends were blunted and an A base was added to the 3′ ends. DNA adaptors with a single T-base 3′-end overhang were ligated to the above products. Ligation products were purified on 2% agarose gels to recover the target fragments and were purified from the gels (Qiagen Gel Extraction kit, 28704). Several rounds of PCR amplification with PCR Primer Cocktail and PCR Master Mix were performed to enrich the Adapter-ligated DNA fragments. Then the PCR products selected by running another 2% agarose gel to recover the target fragments and the gel purified (QIAquick Gel Extraction kit, QUIAGEN). The final library was quantified by assessing the average molecule length (Agilent 2100 Bioanalyzer), and by Real-Time qRT-PCR. A total of 277 Gb of raw data were generated by Illumina Hiseq 2000 at BGI.

All libraries were sequenced in a paired-end mode with read lengths of 100 bp or 150 bp. Reads were filtered and trimmed (100 bp to 95 bp, 150 bp to 145 bp) using SOAPnuke software (https://github.com/BGI-flexlab/SOAPnuke)68 which yielded 250 Gb of data. Low-quality reads, reads with adaptor sequences and duplicated reads were filtered, and if the quality of bases at the head or tail of the reads was low, we directly trimmed them from 100 bp to 95 bp (PE100) or form 150 bp to 145 bp (PE150). The remaining high-quality data were used in the further analysis. SGA PreQC v0.10.1469 modules were run per library and on the combined libraries to estimate various genome parameters (Table 1 and Table 2).

Table 1 Main statistics from O.
Full size table
Table 2 k-mer = 17 raw read statistics for Octopus vulgaris genome data.
Full size table

Draft genome assembly

We applied Assembly By Short Sequencing 2.0.2 (ABySS70,71) for both k-mer sizes that were suggested by SGA PreQC. The quality of assemblies (ABySS kmer41 and ABySS kmer81) was evaluated by QUAST 4. 372. A summary of various statistics is shown in Table 3. Based on the QUAST analysis the optimal kmer size for the ABySS assembly was estimated to be 81. Since a higher heterozygosity rate of the genome was predicted based on these initial results, the Redundans 0.13 c73 tool was used to reduce the number of ABySS contigs from the initial assemblies. Redundans reduces contigs by removing highly similar contigs. These highly similar contigs are originally the different alleles of the same genomic position, but are too different for the De Brujin graph method to be assembled into the same contig (too much variation inside one kmer). Redundans collapses and scaffolds these reduced contigs into single genomic locations. Redundans reduced the number of scaffolds of the draft genome over seven (7) times, while improving assembly statistics (see Table 3).

Table 3 Assembly statistics for Octopus vulgaris.
Full size table

Reference Assisted Scaffolding

Given the availability of a relatively good reference genome of a related species (O. bimaculoides)43, a reference assisted scaffolding tool was used to optimize the genome. The reduced scaffolds were aligned to the O. bimaculoides genome using blastn74 of the blast+ toolkit 2.8.0-alpha. These alignments were used by chromosomer 0.1.3 (https://github.com/gtamazian/Chromosomer) to scaffold the reduced scaffolds according to the given genome.

Assessment of draft genomes

An assessment of the draft genomes (ABySS, Redundans and chromosomer) was performed by looking for the highly conserved genes using BUSCO 3.0. 275. The Metazoa odb9 database was used, supplying 978 orthologs. The number of complete orthologs increased with each improvement of the assembly (Table 3), confirming the gain in assembly quality of the final chromosomer version. The final genome build has over 50% complete BUSCOs, and 10% fragmented BUSCOs (orthologs found, but scattered over multiple scaffolds).

Data Records

The draft genome(s) of O. vulgaris as shown in Table 3 has been made publicly available on the genome browser and data repository of the Association for Cephalopod Research that initiated this work (http://www.cephalopodresearch.org/ceph_gdatab/) in collaboration with the Department of Molecular Evolution and Development, University of Vienna. This web resource is based on the browser originally designed by University of California, Santa Cruz (UCSC)76 and will be maintained and curated to keep track of all present and upcoming octopus genomes. It includes comparative genomics tracks such as read mapping and whole genome alignment between the two octopus species. Raw reads have also been deposited to the NCBI SRA77. The reference-guided assembly has been deposited at GenBank78 and its original version is also provided in the associated FigShare record (chromosomer.fa) together with its annotation (gene_models.chromosomer.gff), and other assemblies listed in Table 3 (Octopus vulgaris genome assemblies79. Table 2 and Table 3 summarize statistics about O. vulgaris genome as deduced from our current sequencing data and Fig. 1 shows the kmer (17mer) distribution determining the overall sequencing depth (Table 1 and 2).

Fig. 1
figure 1

Sequencing depth and genome repetitiveness estimation from 17mer counts in the raw read data. (a) 17mer depth analysis using raw data showing elevated levels of heterozygosity. (b) Cumulative proportion of 17mers as a function of their depth showing that at least half of the genome occurs at depth 10 or more.

Full size image

Technical Validation

Quality control

The quantity and integrity of the genomic DNA was analysed via agarose gel electrophoresis and with a NanoDrop spectrophotometer (Thermo Fisher Scientific; concentration of 1 μg/μl, A260/A280 = 1.84 and A230/A260 = 2.2). DNA integrity was analysed with Agilent Bioanalyzer 2100.

Quality control DNA library

To assess the quality of Illumina reads FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc) was performed on all raw data. Trimmomatic v0.3680 was was not able to identify any significant adaptor sequence contamination within the raw data. The data were mapped to the PhiX control library (Illumina, Inc) using Bowtie2 v2.3.481 and no matches were found.

Sequencing depth assessment

We used jellyfish 2.2. 1082 on the raw read data using kmer size of 17 bp. This resulted in a depth of sequencing histogram (Fig. 1) showing sequencing depth peak of around 76x. Using the kmer depth curve and the cumulative read depth (Fig. 1), repetitiveness, and heterozygosity was conducted independent of the genome assemblies (see Tables 2 and 3). The genome was estimated to be around 2.4 Gb in length with a relatively high heterozygosity rate (>1.1%) and large repetitiveness (>50%).

Genome properties and future steps

To gain information on the genetic distance between the two closely related species O. vulgaris and O. bimaculoides, we mapped all the available raw sequence data from O. vulgaris against the genome of O. bimaculoides83 and found that 74–84% of the data aligned, but that a high percentage (20–50%) was able to align multiple times. The significant proportion of multiple mapping reads suggests that, similar to the O. bimaculoides genome, O. vulgaris genome has a large number (at least 50%) of repetitive elements, confirmed by the cumulative read depth analysis (Fig. 1). Ab initio repeat analysis using dnaPipeTE84 revealed similar classes of octopus specific short interspersed nuclear elements (SINE) to be over-represented (Fig. 2), yet the proportions were strikingly different, despite the close phylogenetic distance. This indicates high activity of repetitive elements in the common octopus genome.

Fig. 2
figure 2

Proportions of the most abundant repetitive element classes in Octopus vulgaris compared to Octopus bimaculoides based on the ab initio reconstruction of repetitive elements using the DNAPipeTE pipeline. (a) Repeat propotions in the Octopus vulgaris genome. (b) Repeat propotions in the Octopus bimaculoides genome. In both genomes, SINE elements are the most abundant repeat classes. While the total number of repeats is similar in both genomes, differences in the proportions can be attributed to individual expansions of repeat elements that occurred independently in both lineages.

Full size image

Profiling O. bimaculoides regions with read coverage from O. vulgaris, we found that 23,509 O. bimaculoides genes were covered at 90% or more of their coding sequence length by O. vulgaris reads (Fig. 3). Approximately 50% of those genes had a Pfam annotation, including gene families previously reported to have undergone major expansions in the O. bimaculoides genome, such as zinc fingers and protocadherins. This is in strong contrast to only 1,570 O. bimaculoides genes with no O. vulgaris read coverage, with just 14% of those having a Pfam annotation. Those candidates represent very recent novel or highly diverged genes and their number indicates a relatively high rate of novel gene formation in octopus genomes. To investigate non-coding evolution among cephalopods, we furthermore compared the mapping rates to non-repetitive non-coding regions of 100 bp and longer. Again, we found the majority of those loci are covered at 90% length or higher. However, the relative proportion of O. bimaculoides regions not covered by any reads was higher than for the genes, indicating a higher turnover rate for the non-coding, potentially regulatory, sequences (Fig. 3).

Fig. 3
figure 3

Comparison of coding and non-coding region conservation between Octopus bimaculoides and Octopus vulgaris. (a) Alignment coverage in the coding genomic regions. (b) Alignment coverage in the non-coding, non-repetitive genomic regions. Coverage shows the proportion of nucleotides that are covered in O. bimaculoides assembly with O. vulgaris read mapping in both coding and non-coding non-repetitive regions of at least 100 bp. The main peak at 1 (100% coverage) indicates the presence of a complete region in O. vulgaris genome at very low sequence divergence, whereas the secondary peak at 0 indicates regions of O. bimaculoides genome that are not matching in O. vulgaris read data (see text for analysis).

Full size image

To evaluate the completeness of our assemblies, raw reads were mapped using Bowtie2 v2.3.4 against both ABySS kmer81 and kmer41 assemblies. For ABySS kmer 41, at least 99.94% of all the reads were mapped while the percentage of uniquely mapped reads was only around 33–50%. For the ABySS kmer81 assembly, percentages were at least 98% and between 31 and 57%, respectively.

We used our assemblies to estimate whole-genome divergences between the available octopod genomes. Mapping of the scaffolds of 10 kb and longer against the O. bimaculoides genome using MEGABLAST resulted in the overall sequence similarity of 92.4% in the aligned regions of 1 kb and above (Fig. 4). This divergence of around 8% between the two species is higher than the estimated heterozygosity rate of 1.1% in O. vulgaris and lower than the divergence between O. bimaculoides83 and the recently released data of C. minor (82.4% similarity) (Fig. 4, and ref.85) from a different genus, providing for the first whole-genome divergence estimates within this clade.

Fig. 4
figure 4

Comparison of whole genome alignments using MEGABLAST among the available octopod genomes. Only the longest scoring alignment between any given pair of two scaffolds or contigs was considered. Red: percentage nucleotide identity between Callistoctopus minor to Octopus bimaculoides. Blue: percentage nucleotide identity between Octopus vulgaris to O. bimaculoides.

Full size image

Our assemblies confirm that abundant repeat regions make it difficult to improve the genome based on the currently available sequence data. Future steps will include long read sequencing technology such as proximity-ligation based assemblies (e.g., Dovetail, PhaseGenomics) or longer read technologies (e.g., PacBio) to optimize the current assemblies.