Sequencing and analysis of the gastrula transcriptome of the brittle star Ophiocoma wendtii

Abstract

Background: The gastrula stage represents the point in development at which the three primary germ layers diverge. At this point the gene regulatory networks that specify the germ layers are established and the genes that define the differentiated states of the tissues have begun to be activated. These networks have been well-characterized in sea urchins, but not in other echinoderms. Embryos of the brittle star Ophiocoma wendtii share a number of developmental features with sea urchin embryos, including the ingression of mesenchyme cells that give rise to an embryonic skeleton. Notable differences are that no micromeres are formed during cleavage divisions and no pigment cells are formed during development to the pluteus larval stage. More subtle changes in timing of developmental events also occur. To explore the molecular basis for the similarities and differences between these two echinoderms, we have sequenced and characterized the gastrula transcriptome of O. wendtii.

Methods: Development of Ophiocoma wendtii embryos was characterized and RNA was isolated from the gastrula stage. A transcriptome data base was generated from this RNA and was analyzed using a variety of methods to identify transcripts expressed and to compare those transcripts to those expressed at the gastrula stage in other organisms.

Results: Using existing databases, we identified brittle star transcripts that correspond to 3,385 genes, including 1,863 genes shared with the sea urchin Strongylocentrotus purpuratus gastrula transcriptome. We characterized the functional classes of genes present in the transcriptome and compared them to those found in this sea urchin. We then examined those members of the germ-layer specific gene regulatory networks (GRNs) of S. purpuratus that are expressed in the O. wendtii gastrula. Our results indicate that there is a shared 'genetic toolkit' central to the echinoderm gastrula, a key stage in embryonic development, though there are also differences that reflect changes in developmental processes.

Conclusions: The brittle star expresses genes representing all functional classes at the gastrula stage. Brittle stars and sea urchins have comparable numbers of each class of genes and share many of the genes expressed at gastrulation. Examination of the brittle star genes in which sea urchin orthologs are utilized in germ layer specification reveals a relatively higher level of conservation of key regulatory components compared to the overall transcriptome. We also identify genes that were either lost or whose temporal expression has diverged from that of sea urchins.

Figure 1

Phylogeny of echinoderms. All evidence indicates that crinoids are the most basal. The other four groups all diverged within a very short geological timeframe around 500 million years ago. Urchins and sea cucumbers are generally considered to form a clade of the most derived. It remains unclear whether the brittle stars group more closely with this clade or with starfish, due to conflicts between molecular, morphological, and embryological evidence [14-16].

Figure 2

Ophiocoma wendtii embryonic development. Stages (A) egg, (B) 16 cell (5 h), (C) hatched blastula (16 to 18 h), (D) mesenchyme blastula (24 h to 26 h), (E) early gastrula (28 to 30 h), (F) gastrula (38 to 40 h), (G) ventrolateral cluster with skeletal spicule (arrow) at 40 h, (H) pluteus (80 h).

Figure 3

Gene functional classes found in brittle star gastrula transcriptome. (A) Ophiocoma wendtii sequences were compared to the KEGG Orthology database by reciprocal best BLAST. Of 3,800 distinct KEGG animal gene clusters, 36% had significant matches to brittle star (blue), and 35% had matches to sea urchin (purple). Green shows the overlap between these two sets, indicating the KEGG clusters that match to both organisms (22%). (B) When sorted into functional classes, an average of 43%, 39%, and 28% of the KEGG clusters within each class had matches to brittle star, to sea urchin, or to both, respectively, with a majority of classes having similar representation in both organisms.

Figure 4

Pyrosequencing of brittle star transcriptome. (A) After cleaning and trimming, 354,586 reads totaled 75,031,136 bp. Approximately 3/4 had lengths between 200 and 300 bp. Less than one percent were longer than 300 bp. (B) A total of 14,261 contigs were assembled, with a combined length of 5,488,581 bp. Median length increased by 23% over that of the unassembled reads. Roughly two-thirds of the reads had lengths between 100 and 400 bp. Four percent were longer than 1,000 bp, creating a long right-hand tail to the distribution. (C) The number of times a given nucleotide position is present in the reads used to assemble the contigs ranged from 1x to 8549.4x. Eighty-one percent were represented one to five times, while less than one percent had more than 100× coverage.

Figure 5

BLAST identification of brittle star genes. Automated BLAST was used to align Ophiocoma wendtii cDNA sequences to both the genome and transcriptome of the sea urchin Strongylocentrotus purpuratus, as well as to the KEGG Orthology database. The areas of the smaller circles represent the number of significant reciprocal best BLAST hits to the indicated datasets. Overlaps indicate matches of the same brittle star sequences to more than one dataset, and in nearly all such cases the matches from the different datasets are mutually consistent. For reference, the large dashed border represents the size of the S. purpuratus genome (~23,300 genes).

Figure 6

Rarefaction curves for sea urchin and brittle star. The steeper initial slope for the sea urchin curve indicates matches to a significant number of KEGG clusters even with many fewer sequences. The brittle star curve rises more gradually, but becomes asymptotic at the right, indicating that the sequencing captured most of the genes present in the gastrula transcriptome. If the two organisms express roughly the same number of genes at equivalent developmental stages, then the rarefaction curves indicate that comparison of these two data sets is indeed meaningful.