Characterization of the Conus bullatus genome and its venom-duct transcriptome
© Hu et al; licensee BioMed Central Ltd. 2011
Received: 23 August 2010
Accepted: 25 January 2011
Published: 25 January 2011
The venomous marine gastropods, cone snails (genus Conus), inject prey with a lethal cocktail of conopeptides, small cysteine-rich peptides, each with a high affinity for its molecular target, generally an ion channel, receptor or transporter. Over the last decade, conopeptides have proven indispensable reagents for the study of vertebrate neurotransmission. Conus bullatus belongs to a clade of Conus species called Textilia, whose pharmacology is still poorly characterized. Thus the genomics analyses presented here provide the first step toward a better understanding the enigmatic Textilia clade.
We have carried out a sequencing survey of the Conus bullatus genome and venom-duct transcriptome. We find that conopeptides are highly expressed within the venom-duct, and describe an in silico pipeline for their discovery and characterization using RNA-seq data. We have also carried out low-coverage shotgun sequencing of the genome, and have used these data to determine its size, genome-wide base composition, simple repeat, and mobile element densities.
Our results provide the first global view of venom-duct transcription in any cone snail. A notable feature of Conus bullatus venoms is the breadth of A-superfamily peptides expressed in the venom duct, which are unprecedented in their structural diversity. We also find SNP rates within conopeptides are higher compared to the remainder of C. bullatus transcriptome, consistent with the hypothesis that conopeptides are under diversifying selection.
Next-generation sequencing techniques have opened up new opportunities for genomics studies of new model organisms . Many of these organisms are not amenable to classical genetic techniques; thus their sequenced and annotated genomes are the central resource for experimental studies. The popularity of the Planarian Schmidtea mediterranea, which can regenerate complete animals from fragments of its body, with stem-cell researchers is one example . The Cone snail is another.
The cone snails (genus Conus) belong to the superfamily Conoidea which probably includes over 10,000 venomous gastropods . The venom from each of the species of cone snails includes a mixture of small cysteine-rich peptides, which are used to immobilize their prey. These small peptides (~15 to 40 amino acids in length) have exquisite specificity for different isoforms of ion channels, receptors and transporters . Their disulfide scaffold restricts the conformational space available to a peptide. However, the combination of variable intervening amino acids and their posttranslational modifications enable a spectrum of specific interactions with their target molecules. A typical conopeptide precursor is comprised of three regions: an N-terminal signal peptide, a pro-region, and a mature peptide region. The N-terminal sequence is usually much more conserved than the mature peptide, possibly due to the diversifying selection on the latter . Conopeptides are classified into super-families, mainly based on the conserved signal peptide and different cysteine patterns observed within the mature peptide.
Conopeptides serve as specific neurobiological tools for addressing specific receptors and channels, and are also valuable lead compounds for therapeutic evaluation. A conopeptide, ω-MVIIA (commercially known as Prialt, ziconotide) isolated from Conus magus, has been approved by FDA for the treatment of chronic pain [6, 7]. In addition, other conopeptides are also being evaluated for the treatment of pain and epilepsy [8–11]. It is estimated that the venom of a single species of Conus may contain as many as 200 different venom peptides [4, 12]. This raises the possibility that the 500-700 species of cone snails may provide upwards of 100,000 compounds of potential pharmacological interest, perhaps more when all the members of superfamily Conoidea are considered.
We have carried out a sequencing survey of the Conus bullatus genome and venom-duct transcriptome. Conus bullatus is a fish-hunting cone snail that together with C. cervus and C. dusaveli are members of the subgenus Textilia (Swainson, 1840). This is probably the least understood group of fish-hunting Conus. All are from the Indo-Pacific region (Pacific and Indian oceans from Hawaii through South Africa). Conus bullatus is the only accessible member of this clade of species; all others are rare and from deep water. C. bullatus is found from the intertidal zone to about 240 m, most commonly from slightly subtidal to 50 m, C. cervus between 180-400 m and C. dusaveli 50-288 m .
Venom studies in Conus bullatus have already yielded results of exceptional pharmacological interest. The best characterized bullatus venom component, alpha-conotoxin BuIA is a small peptide antagonist of nicotinic receptors that has become the standard pharmacological tool for differentiating between nicotinic receptors that carry two closely related subunits, β2 and β4. These receptors are of considerable interest in Parkinson's disease . More recently, the μ-conotoxins, peptides with 3 disulfide bonds that are antagonists of voltage-gated Na channels have also been characterized from Conus bullatus. These peptides appear to have novel subtype selectivity for the different molecular isoforms of voltage-gated Na channels . Thus, they provide a promising neuropharmacological lead to developing an entirely new pathway to differentiate between different voltage-gated Na channel subtypes. Clearly, better cone snail genomics resources would aid these studies; however, few such resources exist as yet for Conus studies, and none for C. bullatus.
The cone snails are being extensively investigated as a source of peptidic pharmacological agents (ligands) with exquisite specificity for different subtypes of receptors in the central nervous system. In keeping with this main goal it is not surprising that most of the available nucleic acid sequences from Conus are a catalogue of these compounds present in the venom. In addition, partial sequences of a few mitochondrial (ribosomal RNA and COI) and nuclear genes [18–23] have also been determined to ascertain the phylogenetic relationship among cone snails.
Previous work has used traditional molecular biology approaches to clone genes encoding members of specific conopeptide super-families [20, 24–26], and EST sequencing in another Conus snail has identified conopeptides [27, 28]. However, to date, no high-throughput sequencing approach on the whole mRNA reservoir of a Conus venom-duct has been attempted.
We have used RNA-seq  to identify and profile the expression of conopeptides and post-translational modification enzymes implicated in venom production. Our results provide the first global view of venom-duct transcription. Our shotgun genomic survey complements our RNA-seq data, and is also the first reported for a cone snail. Knowledge of several marine gastropod genomes will provide a first step toward the molecular understanding of numerous traits unique to these species. Accordingly, we have used these data to determine the suitability of the genome for sequencing and assembly with 2nd generation technologies, determining genome-wide base composition, sequence heterozygosity, simple repeat, and mobile element densities within the C. bullatus genome.
As we show, our RNA-seq and genomic datasets can be combined to enable analyses not possible with either dataset alone. For example, the transcriptome assembly has allowed us to explicitly test the hypothesis that conopeptides are under diversifying selection . We have also developed a novel method for estimating genome size using RNA-seq and genomic shotgun sequences, which we present here. The approach is accurate, and should prove useful for any researcher seeking to determine the size of an emerging model organism  genome using 2nd generation sequencing data.
We generated 96,379,716 Illumina paired 59-mers and 55,699,572 paired 60-mers for the genome. The average insert size of the paired-end library is 200nt. We also isolated venom-duct poly-A mRNA and sequenced it using both Illumina and Roche technologies. On the Illumina platform, we generated 102,278,116 paired 79-mers with a median insert size of 340bp. The Roche 454 platform generated 848,394 reads with average read length of 248bp. Many cDNA reads from the Illumina platform have low-quality 3' ends, which could be due to either to the small amounts of mRNA used in our experiments, or instrument error during sequencing or processing. We removed 3'end sequences from the reads with phred quality values of 2.
Genome-wide GC content
We randomly selected 30 million genomic reads using the process described in the Methods section (see section Simulated Read Sets) and determined their GC content. This procedure gives an estimated GC content for the C. bullatus genome of 42.88%. To validate this method, we also simulated 1 million randomly sampled 60-mers from the D. melanogaster genome and performed the same experiment, which gives 41.87%, an estimate in good agreement with the actual GC content (41.74%.) of the D. melanogaster genome.
Genome-wide Repeat Content
RepeatMasker  and RepBase  lack extensive libraries of repeats for mollusks, which will compromise the ability of RepeatMasker to identify interspersed repeats in the two mollusk datasets. Although, this fact does not complicate direct comparison of C. bullatus and Aplysia californica, with regards to the relative numbers of conserved interspersed repeats, it does complicate absolute measurements and comparisons to the other genomes (Figure 2). The ability of RepeatMasker to identify simple repeats, however, is less impacted by the lack of well-characterized repeat libraries for mollusks. This fact together with comparison with J. Flatley's genomic reads (Figure 2) suggests that C. bullatus is significantly enriched for simple repeats relative to the other invertebrates, and slightly so (1.44 fold) compared to human.
We also used RECON  to identify novel, high-copy genomic sequences that may be interspersed repeats in the C. bullatus genome. For this analysis we used our C. bullatus de novo genomic assembly (see Methods). In total, we found 115 genomic contigs present in 10 or more copies, with an average length of 544bp. Among these genomic sequences, 5 are homologous with known LINE members that were not detected by RepeatMasker in first repeat analysis. Of the remaining contigs, 9 have significant homology with G-protein receptors; 2 have significant homology with lipoprotein receptors; 1 has a leucine-rich repeat structure. These are probably high-copy number genomic regions but are not interspersed repeats. The remaining contigs have little homology with known interspersed repeats, however, a significant fraction of them have either strong homology to nuclease proteins or weak homology with rRNA and tRNA genes-both common motifs in LINE elements. Running RepClass  over these 115 genomic contigs confirmed that 20 contigs have LINE-like structures or are significantly homologous to known LINEs. Including this set would increase the percentage of the C. bullatus genome with LINE homology from 0.24% to 0.56%.
This analysis reveals much about the repeat content of Conus compared to that of the Human genome. First, the Conus genome has a larger proportion of high copy-number sequences (presumably interspersed repeats) compared to human. This is shown by the fact that 23% of Conus reads (compared to 16% in human) have numbers greater than 50. By looking into this group of human reads, we confirmed that 91% of these are homologous to known interspersed repeats. Second, the human dataset (and hence the human genome as compared to the Conus genome) has 3× as many genomic sequences with a copy number above 10,000 compared to Conus (6.9% versus 2.4%). These sequences are mostly non-LTR elements that exists in extremely high copy number; running Repeatmasker over these human genome reads showed that 75% of these genomic regions are SINEs and another 20% are LINEs, supporting this hypothesis. Taken together, our results show that although the Conus genome is enriched for interspersed repeats compared to human, it has far fewer non-LTR repetitive elements.
A partial genome assembly
A previous estimate based upon cytology, placed the Conus bullatus genome at around 3 billion base pairs . If true, our 60 bp paired-end Illumina dataset would provide 3× coverage. Although this is insufficient to produce anything near a complete genome assembly, a partial genome assembly is still desirable for some analyses. We used ABySS to produce a partial assembly 201 million base pairs in length with an N50 value of 182 bp (See Methods for details). This accounts for ~7% of the total length of the C. bullatus genome. To estimate the quality of our genome assembly, we simulated 8.7 million 60bp-long Illumina reads from the D. melanogaster genome (3× coverage), with the same base-calling accuracy distribution as in our Conus genomic reads. To do so we used the procedure described in the Methods section. This process gives 3× coverage over the Drosophila genome with the same error rates as our C. bullatus reads. Assembling these reads with ABySS with the same parameters produced a Drosophila assembly with an N50 of 143 bp and total sequence length of 16 MB, which accounts for roughly 10% of the fly genome. Thus the two assemblies are of comparable quality.
Assembly of the venom-duct transcriptome
We assembled our Illumina RNA-seq reads from the C. bullatus venom-duct with ABYSS (see methods for details). This produced 525,537 contigs of 60bp or greater in length and having a total length of 57 MB. We chose 60bp as minimum contig size because conopeptides can be as short as 20 amino acids. The 454 reads were generated and assembled by Roche.
Annotation of transcriptome
To determine the percentage of the total C. bullatus proteome sampled one or more times in our Illumina and Roche transcriptome datasets, we took the core eukaryotic protein set from CEGMA , which is comprised of 248 core proteins that generally lack paralogs in the eukaryotes [38, 39], and asked what percentage of these proteins are found in the combined Illumina or Roche assemblies. Using BLASTX, 211 out of 248 proteins (85%) are found (E < = 1e-7).
To annotate the transcriptome assembly we ran WU-BLASTX on the ABySS Illumina assembly against UniProtKB database . 7,691 unique UniProtKB proteins have significant homology with one or more transcriptome contigs. We also mapped those contigs no shorter than 200bp to GO  terms for biological process, molecular function and cellular component. As a control, we applied the same approach to the annotated C. elegans, D. melanogaster and H. sapiens transcriptomes and compared the proportion of genes assigned to each GO term in these organisms to our transcriptome assembly results (Additional File 1). Note that is not a comparison of expression levels, but rather a comparison using GO of which genes were represented in our transcriptome assembly. In other words, the relative proportions of all GO gene categories associated with our C. bullatus contigs was found to be similar to the relative proportions of genes assigned to the same GO categories for C. elegans, D. melanogaster and H. sapiens transcriptomes. We found that the resulting GO profiles are highly similar for all four organisms. This finding, together with our observation that 85% of CEGMA proteins are represented in the assembly, suggests that we have sampled a wide swath of the C. bullatus transcriptome.
Identification of Conopeptides in RNA-seq data
We searched our combined Illumina and Roche transcriptome assemblies for significant homology to a set of known conopeptides collected from ConoServer , using the procedure described in the Methods section. We find that, as might be expected, conopeptides are transcribed at high levels in the venom duct; the depth of coverage of the putative conopeptides is 102× versus 33× for the remainder of the transcriptome.
Superfamilies of C. bullatus conopeptides identified by RNA-seq.
Conopeptide Super Family
C. bullatus RNA-seq data
Conoserver reference sequences
Translated transcripts containing putative toxin sequences.
MKLTCVAIVAVLLLTACQLITAEDSRGTQLHRALRKTTKLSVS TR C KGPGAK C LKTMYD CC KYS C SRGR C
MKLTCVLIIAVLFLTAITADDSRDKQVYRAVGLIDKMRR IR ASEG C RKKGDR C GTHL CC PGLR C GSGRAGGA C RPPYN
MKLMCVLIVSVLVLTACQLSTADDTRDKQKDRLVRLFRKKRDSSDSGLL PR T C VMFGSM C DKEEHSI CC YE C DYKKGI C V
MKLTCVVIVAVLLLTACQLIIAEDSRGTQLHRALRKATKLSVS TR T C VMFGSM C DKEEHSI CC YE C DYKKGI C V
MKLTCVLIVAVLFLTACQLATAENSREEQGYSAVRSSDQIQDSDLKL TK S C TDDFEP C EAGFEN CC SKS C FEFEDVYV C* GVSIDYYDSR
MKLICVFIVAVLLLTACQLNAADDSRDTQKHRALRSTTKLSMS KK DS C VPDGDS C LFSRIP CC GT C SSRSKS C V*G
MKLTCMMIVTVLFLTAWTFVTADDSTYGLKNLLPKARHEMMNPEAPKLNKK DE C SAPGAF C LIRPGL CC SEF C FFA C F
AEDSRGTQLHRALRKATKLSES TR C KRKGSS C RRTSYD CC TGS C RNGK C* G
AVLLLTACQLITAEDSRDTQKHRALRSDTKLSMLT LR C ATYGKP C GIQND CC NI C DPARRT C T
DSRGTQLHRALRKATILSVS AR C KLSGYR C KRPKQ CC NLS C GNYM C* G
ACQLITAEDSRGTQLHRALRSTSKVSK STS C VEAGSY C RPNVKL CC GF C SPYSKI C MNFPKN
TAEDSRGTQLHRALRKATKLPVS TR C ITPGTR C KVPSQ CC RGP C KNGR C TPSPSEW
AEDSRGTQLHRALRKTTKLSLS IR C KGPGAS C IRIAYN CC KYS C RNGK C S
AACQLGTAASFARDKQDYPAVRSDGRQDSKDSTLDRIA KR C SEGGDF C SKNSE CC DKK C QDEGEGRGV C LIVPQNVILLH
MLKMGVLLFTFLVLFPLATLQLDADQPVERYADNKQDLNPD ER MIFLFGG CC RMSS C QPPPV C N CC AKQDLNPDER
DQPADRPAERMQDDISSEQNPLLEKR VGER CC KNGKRG C GRW C RDHSR CC* GRR
GLY CC QPKPNGQMM C NRW C EINSR CC* GRR
A-superfamily: CC-C-C; CC-C-C-C-C
MGMRMMFTVFLLIVLATTVVSFSTDDESDGSNEEPSADQTARSSMNR APG CC NNPA C VKHR C* G
MGMRMVFTVFLLVVLATTVVSFTSDRASDGRNAAANDKASDLAALA VR G CC HDIF C KHNNPDI C* G
MGMRMRMMFTVFLLVVLANTVVSFPSDRDSDGADAEASDEPVEFER DENG CC WNPS C PRPR C T*GRR
DGANAEATDNKPGVFER DE KK CC WNRA C TRLVP C SK
SDRASDGRNAAANDRASDLVALT VR G CC TYPP C AVLSPL C D
MGMRMMVTVFLLGVLATTVVSLRSNRASDGRRGIVNK LNDLVPQYWTECC GRIGPH C SR C I C PEVV C PKN*G
MGMRMMVTVFLLVVLATTVVSLRSNRASDGRRGIVNKLNDLVPK YWTECC GRIGPH C SR C I C PEVA C PKN*G
MGMRMMVTVFPLVVLATTVVSLRSNRASDGRRGIVNKLNDLVPK YWTECC GRIGPH C SR C I C PGVV C PKR*G
LVVLATTVVSFRSNRASDGRKIAVNKRRRELVVPPG K LRE CC GRVGPM C PK C M C PPRR C
ASDGRNAVVH ER APELVVTATTT CC GYDPMTI C PP C M C THS C PPKRKP*GRRND
MTSVQSATCCCLLWLVLCVQLVTPDSPATAQLSRHLTAR VPVGPALAYA C SVM C AKGYDTVV C T C TRRRG*VVSSSI
MEGRRFAAVLILPICMLAPGAVAS KR WTRPSV C NLPAESGTGTQSLKRFYYNSDKMQ C RTFIYKGNGGNDNNFPRTYD C QKK C LYRP*G
Sequence diversity and classification of A-superfamily conopeptides from Conus bullatus
Subclasses of A-superfamily peptides (Mature toxin sequences)
APGCC NNPAC VKHRC*
DENGCC WNPSC PRPRC T*
CC WNRAC TRLVPC SK
GCC TYPPC AVLSPLC D
GCC HDIFC KHNNPDIC*
APELVVTATTTCC GYDPMTIC PPC MC THSC PPKRKP*
LNDLVPQYWTECC GRIGPHC SRC IC PEVVC PKN*
YWTECC GRIGPHC SRC IC PEVAC PKN*
YWTECC GRIGPHC SRC IC PGVVC PKR*
LRECC GRVGPMC PKC MC PPRRC
SNP rates in conopeptides
We also compared the single nucleotide heterozygosity level within the transcripts encoding conopeptides to the rest of the transcriptome. To reduce false negative rates, we restricted our analysis to transcriptome contigs having coverage depths of 10× or more. Our rationale being that SNPs within low-coverage contigs might be missed, leading us to underestimate the actual SNP rate. For the transcriptome as a whole, the SNP rate is 0.0035 (102,955 SNPs in 29.5 MB of high-coverage contigs). By contrast, the single nucleotide polymorphism rate within conotoxin contigs is 0.011 (1146 SNPs in 105,259bp of high-coverage conotoxin contigs; this is 64% of all conotoxin contigs by length). The 3.1-fold higher SNP rate within conopeptides contigs is consistent with the hypothesis that conopeptides are under diversifying selection.
Candidate post-translational processing enzymes
Conopeptides contain post-translationally modified amino acids. These modifications play an important role in conferring target specificity. The most ubiquitous modification is the formation of disulfides leading to proper conotoxin folding; this mediated by disulfide isomerases, chaperones and enzymes involved in redox biochemistry. From an examination of transcriptome sequences we have identified partial and complete sequences of several chaperones and thiol-disulfide oxidoreductases that are likely to be involved in the redox biochemistry of conotoxin folding (Additional File 2).
We identified some of the enzymes that are presumed to catalyze correct disulfide connectivity within conopeptides [43–46]. These include members of the QSOX family of sulfhydryl oxidases, Ero oxidases and protein disulfide isomerases (PDIs). PDIs also have chaperone-like activity and prevent protein aggregation. We have identified three isoforms of protein disulfide isomerase (PDI) and four members belonging to different subfamilies of PDIs. Two of these are members of the P5 subfamily. We also identified a transcript related to human PDIRs, which carry out oxidation-isomerization functions similar to PDI, but are less active. We also identified a transcript encoding a second redox inactive TRX domain b' belong to Ep72 and Ep57 subfamily. In addition, transcriptome contigs with homology to several Chaperones, including 78kDa glucose regulated protein, Hsp70, Hsp60, Hsp90, glucose regulated protein 94, different subunits of the T-complex protein 1, DNA J (Hsp40), calnexin, calreticulin, chaperonin 10kDa subunit, prefoldin superfamily and activator of Hsp90 ATPase I were also identified.
The other enzymes we have identified include a proline hydroxylase related to the enzyme involved in collagen biosynthesis. (Unrelated to the posttranslational modification of peptides, we have also identified the egl nine homolog-also a prolyl hydroxylase). We have identified both FK506 binding protein type peptidyl prolyl cis-trans isomerase and the cyclophilin peptidyl prolyl cis-trans isomerase. The latter type has been shown to enhance the rate of correct folding of conopeptides containing proline residues . Other enzymes identified include lysyl hydroxylase, vitamin K dependent γ-glutamyl carboxylase [48, 49], vitamin K epoxide reductase and peptidyl glycine alpha amidating monooxygenase.
A large number of hormones and neuro-active peptides require C-terminal amidation for full activity [50–52]; conopeptides are no exception. C-terminal amidation is a two-step process. Peptidylglycine α-hydroxylating monooxygenase (PHM) catalyzes the hydroxylation of the α-carbon of glycine and a second enzyme, peptidyl-α-hydroxy glycine α-amidating lyase (PAL) catalyzes the formation of the amidated product and glyoxylate. In Drosophila these two activities are carried out by separate polypeptides, whereas in other organisms (C.elegans, Xenopus laevis, human and rat) a single polypeptide carries out both activities. We discovered a single transcriptome contig encoding both PHM and PAL domains, thus C-terminal amidation of conopeptides is likely carried out by a single enzyme in C. bullatus.
A unique posttranslational modification first identified in Conus was the presence of 6-Br tryptophan in conopeptides, e.g. bromocontryphan , bromosleeper  and light sleeper . Subsequently the modification was also characterized in a peptide isolated from mammalian brain [55–57]. The enzyme responsible for this modification has not been characterized. However, four different classes of haloperoxidases are known , which are enzymes that use heme iron/H2O2, vanadium/H2O2, FADH2/O2, and non-heme iron/O2/α-ketoglutarate. In the present analyses we have not identified any of the above classes of enzymes.
Another posttranslational modification is the isomerization of L-amino acids in peptides to the D-conformation . The enzyme has been isolated from the funnel web spider venom . At present we have not identified any transcript possibly encoding the isomerase.
A novel method for estimating genome size
2nd generation sequencing technologies now make it possible to probe new and emerging model organism genomes in a cost effective manner. This means that genomes and transcriptomes can be rapidly trawled for specific contents, and at the same time the organism can be evaluated for suitability of whole-genome de novo assembly. We have tried to accomplish both these tasks in the work reported here.
Our transcriptome analyses provide the first global view of gene expression within a Conus venom-duct. Several lines of evidence suggest that our dataset provides a relatively comprehensive view of this pharmacologically important tissue. First, the relative proportion of C. bullatus genes (as discovered by annotating out transcriptome data) assigned to different GO terms resemble those of other well annotated transcriptomes. Second, 85% of CEGMA's universally conserved eukaryotic genes are represented by one or more contigs, providing an independent estimate of the degree of completeness of the assembly. One caveat to this conclusion is that highly expressed basic house keeping genes are over represented in the CEGMA set; thus a more precise statement is that 85% of highly expressed genes are present in the RNA-seq data.
Our RNA-seq data are highly enriched for reads with conopeptide homology. The average read depth of contigs homologous to conopeptides is 102× as opposed to 33X for the remaining contigs. Interestingly, their super-family frequency spectrum roughly approximates that of the Conoserver reference collection in general , although some rare classes are missing.
Overall, the distribution and frequencies of GO functions, processes and locations of annotated transcriptome data closely parallel those of various carefully annotated model organism transcriptomes (Additional File 1); this fact suggests that overall, the venom-duct transcriptome is diverse, despite the highly specialized nature of this tissue. Although, as our recovery of numerous conopeptides and post-translational modification (PTM)-enzymes makes clear, its transcription is also clearly geared toward venom production. Our success at characterizing the conopeptide and candidate PTM-enzymes demonstrates the power of the RNA-Seq approach for conopeptide discovery. The conopeptide and PTM-enzymes we have discovered present new avenues for future research, as it is now possible to express these proteins in heterologous cells in order to explore interactions PTM-enzymes and their conopeptide targets [47, 48, 61, 62].
Our genomic shotgun survey data have allowed us to characterize the C. bullatus genome. Our analyses indicate that it is enriched for simple repeats relative to the human genome. Characterization of its interspersed repeat populations is complicated by the lack of an adequate repeat library for RepeatMasker. To circumvent this obstacle, we developed a novel analysis method, comparing the inter-read similarity frequency spectrum of our C. bullatus genome reads to the inter-read similarity frequency spectrum of matched human dataset. Based upon this analysis we conclude that C. bullatus has higher repeat content, yet contains fewer extremely high-copy repeat species. Because this method requires no assembly or prior knowledge of a genome's repeat content, it should prove useful to others seeking to characterize the repeat contents of new and emerging model genomes.
We have carried out the first transcriptome and genomic survey of a Textilian, Conus bullatus. Our RNA-seq analyses provide the first global view of transcription within a Conus venom duct, and demonstrate the feasibility of trawling these data for rapid discovery of new conopeptides and PTM-enzymes. We find that numerous A-superfamily peptides are expressed in the venom duct. These conopeptides are unprecedented in their structural diversity, suggesting that Conus bullatus, and potentially the Textilia clade in general, has explored novel evolutionary pathways in generating its complement of A-gene super-family peptides. Our data also provide support for the long-standing hypothesis that conopeptides are under diversifying selection. Our genomic analyses have revealed that the C. bullatus genome has higher content of interspersed repeats, yet fewer extremely high-copy-number repeats compared to human.
Preparation of RNA samples
Specimens of Conus bullatus were collected in the Phillippines. Each specimen was dissected to isolate the venom duct and the duct was immediately suspended in 1.0 mL RNAlater solution (Ambion, Austin, TX) at ambient temperatures, and then stored at -20 degrees Centigrade until used. Total RNA was isolated using mir Vana® miRNA isolation kit (Ambion, Applied Biosystems CA USA) according to the manufacturer's recommendation Tissue homogenization was carried out using a tissue tearor (Model 985370, Dremel, WI, USA).
Simulated read sets
To produce the matching sets of reads from other genomes with which to compare our C. bullatus reads, we randomly sampled some number of read pairs from our Conus dataset. Next we randomly selected substrings from an assembled target genome (e.g. human, D. melanogaster, etc.) having the same length and pair distances as our Conus reads. This matched dataset mimics the Conus data precisely as regards number of reads, distance between pairs, read lengths, and importantly base quality. This last feature is accomplished by mutating the simulated reads from the target genome using the base quality values of the selected Conus reads. These matched datasets enable many useful analyses. For example, a set of 1,000,000 randomly selected Conus genomic reads can be passed through RepeatMasker and the results directly compared to that produced from its matched human counterpart.
Partial genome assembly
We generated a total of 152 million Illumina genomic reads, with read lengths of either 59bp or 60bp depnding upon run. The reads are paired-end, and have a average insertion size of 200bp. We used the 'qualityTrimmer' algorithm in the EULER-SR software package  to remove bad reads and trim low-quality region from reads. We then used ABySS 1.0.15  for assembly, with the following parameters: c = 0, e = 2, n = 2. The k-mer size is an important factor for the quality of assembly, and in order to make an informed decision about the k-mer size, we assembled the C. bullatus genome with k = 25, 30, 35, 40, 45 and 50. The k-mer size of 25 generate an assembly with the best total length (201MB) and N50 (182bp). The assembly was filtered so that contigs/scaffolds with lengths less than 100 bp were removed. When aligning the genomic reads back to the de novo assembly, 3.6 million reads aligned.
Assembly of the transcriptome
102 million paired-ended RNA-seq reads were generated using the Illumina sequencing platform. The read lengths for these runs were 79bp, with an average insertion size of 340bp. These reads were first filtered with EULER-SR's 'qualityTrimmer' algorithm as above, then assembled by ABySS 1.0.15 using the following parameters: c = 0, e = 2, E = 0. k-mer size of 25, 30, 35, 40, 45, 50 were tested, and the assembly at k = 35 were chosen in consideration for the total assembly size as well as N50. The assembly was filtered so that contigs/scaffolds with lengths less than 60 bp were removed.
To assess the quality of the transcriptome assembly, we aligned the RNA-seq reads back to the assembly with Bowtie. Out of 102 million reads, 31million aligned to the transcriptome under single-end alignment mode. A much smaller portion (3.2 million) of reads were aligned under paired-end mode. This is expected because our library should be enriched for short conopeptide sequences, thus many fragments should be shorter than 340bp, which will produce overlapping paired-reads that won't align under paired-end mode of Bowtie.
Characterization of repeat content in the genomic assembly
We randomly selected 1 million Illumina reads for the genome of Conus bullatus. As a control, we used the reference genomes of Aplysia californica, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens from NCBI database. For each of the control genomes, 1 million Illumina reads with the same length and base-calling accuracy distribution were simulated. We also used a second control consisting of 100,000 real Illumina genomic reads randomly sampled from the Flatley genome . We ran RepeatMasker with the '-species all' option in order to characterize all known families of interspersed repeats. These data are shown in Figure 2.
Novel repeat families with Conus bullatus genome were identified by running RECON over the longest genomic contigs with a total length of 30MB (masked by RepeatMasker beforehand). We then perfromed an all-by-all BLASTN of the contigs against themselves, using an E-value threshold of 1e-8. The blastn reports were converted into MSP files and fed to RECON to identiy genomic sequences present in no less than 10 copies in the 30MB sample sequence. 115 high-copy-number sequences were identified, and any of them that have significant homology (1e-5) with a UniprotKB or Repbase entry were removed from the novel interspersed repeats collection.
Estimation of the proportion of repetitve regions
1 million genomic reads from the conus genome were randomly selected; 1 million human genomic reads were then simulated with the same length and base-calling accuracy. We aligned each set of reads to themselves with BLASTN to look for significant similarity (M = 1 N = -3 Q = 3 R = 3 W = 15 WINK = 5 filter = seg lcmask V = 1000000 B = 1000000 E = 1e-5 Z = 3000000000). The percentage of reads having each number of BLAST hit were then tallied.
To convert the number of BLAST hits to the copy-number of their corresponding genomic sequence, we simulated a genome with the same size as the human genome and the following features: 38% of this genome are comprised of unique sequence; 20% are sequences with 2 copies; 10% of the genome have 5 copies, 10 copies, 100 copies and 1000 copies each; 1% of the genome have 10,000 and 100,000 copies each. Then we simulated 1 million reads from this genome with the same length and base-calling accuracy as the Conus genomic reads and performed an all-to-all blast approach as described above. For each read generated, we tracked the copy number of the genomic region that it is extracted from. Then we calculated the average number of read partners for reads from different copy-number region. As Additional File 3 shows, the average number of read partners is correlated extremely well with the copy-number of the genomic region the read was drawn from. The equation in Additional File 3 allows us to profile the proportion of genomic regions with different copy-numbers, as shown in Figure 3.
To estimate SNP rates within our transcriptome assembly, Illumina reads were aligned to contigs no shorter than 60bp in the transcriptome assembly, using Bowtie  with default parameters. With the samtools package, the resulting Bowtie report was converted into SAM files , then used to estimate the SNP ratio with samtools. We used stringent criteria to call SNPs, requiring that: 1) the SNP phred score was higher than 20; and 2) that each SNP variant was supported by at least two reads. The SNP rates within conopeptides were estimated using a same approach. We also calculated the proportion of triallelic SNPs, which is 15%, indicative of the upper bound of the false-positive rate due to mis-alignment.
BLAST searches for conopeptides
We ran BLASTX on our transcriptomal assembly against the combined database of UniProtKB  and conotoxins from ConoServer , using the following parameters: W = 4 T = 20 filter = seg lcfilter. Contigs that hit a conopeptide as its best hit were collected as the low-stringency conopeptide set, and subsequently translated into peptides according to the reading frame identified by BLASTX. We then ran BLASTP on the low-stringency conopeptides against the combined database, using the following parameters: hitdist = 40 wordmask = seg postsw matrix = BLOSUM80. The results are filtered with E < = 3e-5.
Assignment of putative conotoxins to superfamilies
We first translated each putative conotoxin conteg into peptide sequence, using the reading-frame predicted from BLASTing the RNA-seq assembly to ConoServer's collection of conopeptides. Each translated putative-conopeptide was then aligned with BLASTP to conotoxin signal peptides sequences, downloaded from ConoServer. We required all aligments to have Expect < = 1e-4, and to have at least 7 identical amino acids aligned. The best hit for each putative conopeptide is used to predict its superfamily. Overall, we were able to assign 543 putative conopeptides to a superfamily. As a control, we downloaded previously reported conopeptides from ConoServer, and randomly sheared these sequences into short oligos with the same N50 as our putative conopeptide contigs. We applied the same approach to assign these to superfamiles. Out of 3274 oligos, we were able to assign 449 to a superfamily, of which 443 (98.7%) were correct. Thus, we believe our assignment method is reasonably accurate.
Genome size estimation
We ran WU-BLASTN over all transcriptomal contigs longer than 300bp against 73,898,732 59-mer genomic reads, with the following parameters: M = 1 N = -3 Q = 3 R = 1 wordmask seg lcmask. The coverage depth for each transcript was calculated from dividing total length of reads mapped to this transcript by its transcript length. Then the frequency distribution is shown in Figure 4. The estimated coverage depth for the genome is determined as the coverage depth with the highest frequency, which is 1.70×. The estimated genome size for Conus bullatus is thus 73,898,732*59/1.70 = 2.56×109 bp.
Significance of conopeptide BLAST hits
The short reads and base quality issues combine with the short lengths of conopeptides to make identification of conopeptides in RNA-seq data difficult. Because many conopeptide transcript species are represented by only one or a few reads, the base-quality of the resulting contig is often low, especially as regards indels. All of these facts combine to make the detection of even highly conserved conopeptides problematic, because BLASTX is unable to take into account indel induced frameshifts in the contigs when calculating the significance of a hit , thus many real hits are not detected. Also problematic is the cysteine-rich nature of conopeptides, leading to spuriously significant hits against other non-homologous but cysteine-rich proteins, and protein domains. To control for these issues we performed a simulation to help us determine the best E-value threshold for a conopeptide hits in RNA-seq data. We first ran WU-BLASTP  on our transcriptome assembly against the combined database of UniProtKB  and conopeptides from ConoServer . In total, 6,677 peptides were found to have a known conopeptide as its best hit. We then plotted the E-value distribution of the BLAST results for the best HSPs (Additional File 4). Next, we randomly permuted the sequences of each of our 6,677 C. bullatus contigs with conopeptide hits using a Fisher-Yates shuffle . We then ran BLASTP using the permuted peptides against the combined UniProtKB and conotoxins database, and plotted the E-value distribution for all hits. Presumably, the latter plot should represent the background distribution of insignificant BLAST hits. We found that only 5% of the hits in the permuted peptide set have an E-value of lower than 3e-5, while in the putative conopeptide set, the percentage is 48%. Thus we used E < = 3e-5 as the E-value threshold for our BLASTP searches for conopeptides.
Data and software availability
The read-simulation tool and data (transcriptome assembly, genomic assembly, putative conotoxin sequences and post-translational modification enzymes) can be downloaded at http://derringer.genetics.utah.edu/conus/. The software is open source.
We thank Roche 454 sequencing for helping to make this work possible by contributing transcriptome data to the project; especially C. Kodira for help and support. We also acknowledge Cofactor Genomics for their help and support in obtaining the Illumina RNA-seq and Genomic data. This work was supported in part by NHGRI NIH grant 1R01HG004694 to MY, GM48677 (BMO, PB) and a University of Utah seed grant to MY and PB.
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