Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Genomics

Open Access

Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica

  • Selene L Fernandez-Valverde1,
  • Andrew D Calcino1 and
  • Bernard M Degnan1Email author
BMC Genomics201516:387

https://doi.org/10.1186/s12864-015-1588-z

Received: 23 November 2014

Accepted: 27 April 2015

Published: 15 May 2015

Abstract

Background

The demosponge Amphimedon queenslandica is amongst the few early-branching metazoans with an assembled and annotated draft genome, making it an important species in the study of the origin and early evolution of animals. Current gene models in this species are largely based on in silico predictions and low coverage expressed sequence tag (EST) evidence.

Results

Amphimedon queenslandica protein-coding gene models are improved using deep RNA-Seq data from four developmental stages and CEL-Seq data from 82 developmental samples. Over 86% of previously predicted genes are retained in the new gene models, although 24% have additional exons; there is also a marked increase in the total number of annotated 3’ and 5’ untranslated regions (UTRs). Importantly, these new developmental transcriptome data reveal numerous previously unannotated protein-coding genes in the Amphimedon genome, increasing the total gene number by 25%, from 30,060 to 40,122. In general, Amphimedon genes have introns that are markedly smaller than those in other animals and most of the alternatively spliced genes in Amphimedon undergo intron-retention; exon-skipping is the least common mode of alternative splicing. Finally, in addition to canonical polyadenylation signal sequences, Amphimedon genes are enriched in a number of unique AT-rich motifs in their 3’ UTRs.

Conclusions

The inclusion of developmental transcriptome data has substantially improved the structure and composition of protein-coding gene models in Amphimedon queenslandica, providing a more accurate and comprehensive set of genes for functional and comparative studies. These improvements reveal the Amphimedon genome is comprised of a remarkably high number of tightly packed genes. These genes have small introns and there is pervasive intron retention amongst alternatively spliced transcripts. These aspects of the sponge genome are more similar unicellular opisthokont genomes than to other animal genomes.

Keywords

TranscriptomeTranscription terminationAlternative splicingMetazoan evolution

Background

The origin of the fundamental rules governing metazoan multicellularity and morphological complexity can be gleaned through the analysis of the genomes of early branching animals (e.g. sponges, cnidarians, ctenophores and placozoans) [1-4] and their closely related unicellular holozoans (e.g. choanoflagellates and filastereans) [5-7]. Comparative analysis of these genomes has shed light into the evolution of protein-coding gene families. For instance, transcription factor and signalling pathway gene families that are essential to the development of complex bilaterians (e.g. vertebrates, insects, worms and their allies) largely evolved in the Precambrian, before the lineage leading to these animals diverged from early branching animal phyla [1,8-11].

Obtaining a more complete picture of the origin and early evolution of metazoan multicellularity and development also requires the analysis of the mechanisms that regulate gene expression. This demands (i) a more precise view of genome organisation and composition, and gene structure, (ii) detailed expression profiles from multiple cell types, and developmental and physiological contexts, and (iii) the capacity to experimentally manipulate gene function. Thus, increasing the accuracy and completeness of the draft genomes of early branching metazoans is an important step in improving their utility for future evolutionary and functional studies aimed at unravelling the origin of animal multicellularity.

The genome of the demosponge Amphimedon queenslandica was published in 2010 [1] and is currently the only published genome from phylum Porifera. The sponge body plan is amongst the simplest in the animal kingdom. It lacks nerve and muscle cells and a centralised gut (reviewed in [1,12-14]). Porifera is traditionally regarded as the oldest surviving phyletic lineage of animals. However, as recent molecular phylogenomic and phylogenetic analyses both support [1,15] and reject [3,4,16,17] this traditional view, it remains unclear as to whether sponges or ctenophores are the sister group to all other animals and whether poriferans are monophyletic. Thus, interpretations of the sponge body plan in the context of metazoan evolution range from it representing a state similar to the last common ancestor of modern animals to it being derived from a morphologically more complex ancestor that possessed a gut, nerves and muscles.

Here we have improved the gene annotations in the draft genome of Amphimedon by combining deep transcriptome data from four developmental stages with previously generated developmental ESTs and CEL-Seq – a single cell RNA-Seq method [18] - evidence across 82 sponge developmental samples, from early cleavage through metamorphosis [19]. The inclusion of these transcriptomes markedly improves the current Amphimedon protein-coding gene models, which were primarily based on ab initio predictions and low-throughput EST evidence, and increases the total number of protein-coding genes in the genome by 25%. Furthermore, analysis of transcripts across sponge development has for the first time revealed alternative splicing patterns in a sponge, which are more similar to those reported in yeast than to those described in eumetazoans.

Results

Evidence-based protein-coding gene annotation

We sequenced and assembled de novo A. queenslandica polyadenylated RNAs present in adult, juvenile, competent and pre-competent larval stages in a strand-specific manner using Trinity [20]. To help detect low-abundance transcripts we also sequenced an adult sponge sample at high-depth in an unstranded manner and assembled it de novo with Trinity [20] (Table 1, see Methods). All strand-specific transcripts were combined with 8,880 previously assembled EST contigs from larval stages [1] using PASA [21]. The best open reading frames (ORFs) were predicted from the representative transcripts generated by PASA (Figure 1A). To better resolve A. queenslandica gene families characterized by complex and highly repetitive regions that Trinity might assemble incorrectly (e.g. the Nucleotide-binding domain and Leucine-rich Repeat- containing (NLR) gene family [22]), an independent genome-guided assembly for each developmental stage was generated using Cufflinks [23]. Only Cufflinks transcripts found in at least two developmental stages were used as additional evidence for gene annotation (Figure 1A).
Table 1

Transcriptome sequencing statistics

 

Precompetent larvae

Competent larvae

Juvenile

Adult

Adult deep

Reads

42,273,865

43,699,007

41,677,487

43,098,690

125,880,671

Quality trimmed reads

39,234,338

40,546,526

38,587,047

39,976,627

116,384,700

Assembled transcripts

107,725

122,306

101,432

112,187

230,181

Average transcript length

816

738

706

716

1,278

Longest transcript

14,660

12,052

9,354

16,961

29,513

Mapped Transcripts

63,681

64,826

61,149

65,377

76,045

% Mapped/Assembled

59.11%

53.00%

60.29%

58.28%

33.04%

Average mapped transcript

738

729

673

686

918

Longest mapped transcript

14,393

12,052

24,959

16,954

29,166

Total transcripts mapped to genome

63,681

64,826

61,149

65,377

76,045

Total coverage (% genome)

28.32%

27.92%

29.24%

30.31%

21.62%

Used for PASA

Yes

Yes

Yes

Yes

No

Figure 1

Reannotation strategy and comparison of the new gene models (Aqu2) of the Amphimedon queenslandica genome with previous gene models (Aqu1, ab initio and NCBI). A) Diagram of de novo transcriptome assembly and annotation strategy. Boxes represent sets of data while arrows denote specific computational steps in the annotation pipeline. Steps involving Trinity have been omitted for brevity. B) Venn diagram showing overlap of Aqu2 models with previous annotations including ab initio (Augustus, SNAP and GenomeScan), NCBI and Aqu1 at 80% similarity to account for missing UTR regions in previous annotations. Intersections were done in a hierarchical fashion with the following order of precedence: Aqu2; Aqu1; ab initio; and NCBI.

De novo and genome-based assembled transcripts, predicted ORFs and the previously generated ab initio gene models [1] were combined using EVM [24] to predict protein-coding gene models. Untranslated regions (UTRs) were added to these EVM gene models by two successive rounds of PASA using all developmental stranded Trinity transcripts and ESTs (Figure 1A).

The completed set of Amphimedon genes - Aqu2 - contains a total of 47,895 transcripts, which includes alternatively spliced gene isoforms expressed in different developmental stages (see below). To reduce isoform redundancy we identified each gene’s isoform with the longest ORF (Figure 1A), resulting in 40,122 protein-coding loci in the final Aqu2 gene annotation. Finally, deep 3’ end-biased CEL-Seq [18] expression data spanning 82 A. queenslandica developmental samples [19] were used to refine the 3’ ends of Aqu2 gene models, resulting in the extension of the 3’ ends of 10,925 genes (Figure 1A).

Comparison with previous annotations

Currently, the main Amphimedon gene annotation resource available to the community is Aqu1. Aqu1 has 30,060 genes and was released along with the original report of the Amphimedon genome [1] (Table 2). Additionally, NCBI generated a limited set of predicted genes via their automated pipeline upon genome submission, resulting in 9,975 protein-coding gene predictions. To assess the gene annotation improvements, Aqu2 was compared with these gene annotations and previously generated ab initio gene model predictions [1] (Figure 1B).
Table 2

Aqu1 and Aqu2 gene annotation comparison

 

Aqu1

Aqu2

Total gene number

30,060

40,122

Total length (% genome)

69.3 Mb (47.82%)

93.5 Mb (64.52%)

Average gene length *

2,426 bp

2,521 bp

Smallest gene *

81 bp

149 bp

Longest gene *

86,995 bp

103,992 bp

Total isoform number

30,060

47,895

Total exon number

171,753

206,984

Average exon length

218 bp

225 bp

Average exons per gene

5.7

5.2

Max exons per gene

97

103

Total intron number

140,027

166,862

Average intron length

253 bp

327 bp

ORF

  

Total length

35.6 Mb

42.6 Mb

Average size

1,184 bp

1,062 bp

Longest

47,676 bp

56,682 bp

5’ UTRs

  

Total number

4,457

9,873

Total length

0.6 Mb

1.13 Mb

Average size

130 bp

114 bp

Longest

2,318 bp

4,952 bp

3' UTRs

  

Total number

6,021

14,892

Total length

1. 3 Mb

2.8 Mb

Average size

211 bp

188 bp

Longest

2,268 bp

2,814 bp

*Includes introns and exons.

21,921 (54.6%) Aqu2 models share at least 80% identity with Aqu1 models, with many of the revised genes having a different structure (i.e. exon-intron architecture) or length (Figure 1B). 4,340 Aqu1 gene models are not supported at all in the Aqu2 annotation. Also 43,279 (71.6%) of ab initio and 7,918 (79.4%) of NCBI annotated genes are included in Aqu2. Some NCBI models are fragmented into smaller gene models resulting in 9,188 Aqu2 models from 7,918 NCBI models (Figure 1B). In contrast, many adjacent ab initio models have been merged resulting in 32,383 Aqu2 models from 43,279 ab initio models (Figure 1B).

Aqu2 covers 16.7% more of the current A. queenslandica genome (Table 2) and includes 35,231 newly annotated exons, with 5,309 of the previous Aqu1 models having additional exons in their corresponding Aqu2 models (Figure 2A). There is also a marked increase in the number of 3’ and 5’ untranslated regions (UTRs) (Figure 2B and Table 2). The use of CEL-Seq 3’ end evidence results in an increase from 6,021 to 14,892 annotated 3’ UTRs (Table 2) in Aqu2, while 5’ UTRs only increase from 4,457 to 9,873 (Table 2).
Figure 2

Improvement in Amphimedon queenslandica gene annotations. A) Increase in the number of exons (x-axis) per gene (y-axis) between Aqu1 (gray) and Aqu2 (black) for all genes with less than 30 exons. B) Increase in the number (y-axis) and size of coding (CDS - blue) and non-coding (3’ UTR – pink, 5’ UTR – green) transcript regions (x-axis) between Aqu1 (left panel) and Aqu2 (right panel). The transcript region size is displayed in log-scale. C) Genome browser example of improvements in gene annotations between ab initio (Augustus, SNAP and GenomeScan – purple track), Aqu1 (dark green-blue track), EVM models with annotated UTRs (bright blue) and Aqu2 (orange track). The gene on the right side of the panel is GATA and the one on the left has no significant match in other organisms. Thick blocks represent coding exons, thin blocks non-coding exons and lines introns. Small arrows on introns denote the direction of transcription. Scaffold number and position, and scale bar shown on top. D) Distribution of the intergenic distance between annotated genes of Amphimedon queenslandica (Aqu2) displayed in log-scale. The median intergenic distance (587 bp) is shown as a vertical line (red).

A comparison of the protein best blast hit (BBH) of Aqu1 and Aqu2 gene models against the SwissProt database reveals that Aqu1 and Aqu2 have a similar number of BBH to metazoan proteins, with slightly fewer matches in Aqu2 (419 proteins). In contrast, Aqu2 has more unique coding sequences (i.e. no blast matches in the database) with identifiable PFAM domains proteins (3,804) compared than Aqu1 (2,649) (Additional file 1: Figure S1), potentially expanding the list of lineage-restricted genes. Finally, there are 17,310 unannotated genes in Aqu2 compared to 7,879 in Aqu1, which will require further verification to establish if they are present in other basal metazoans, unique to poriferans, or restricted to demosponges.

Improvements in Aqu2 are exemplified in the locus depicted in Figure 2C, which shows the gene encoding the developmental transcription factor GATA [1,25-27], and a previously unannotated gene transcribed in the opposite direction from a putative bidirectional promoter. This gene was missing from the previous annotation (Aqu1) although it was predicted by ab initio methods (Figure 2C). In Aqu2 both genes have annotated 3’ and 5’ UTRs; CEL-Seq data further extend both the 3’ ends. The significant increase in gene model number and length results in a more gene dense genome with a decrease of the median intergenic distance from 929 to 587 bp (Figure 2D).

Identification of previously unannotated protein-coding genes

Although Aqu2 represents a more complete picture of the genes present in the Amphimedon, we find most improvements in conserved gene families are minor and are generally restricted to more accurate assignment of exons and untranslated regions. However there are a few notable exceptions. For instance, we identified a number of new transcription factors, including the Aristaless homeobox (ArxC) gene, which, in spite of having been previously identified by Larroux et al. [28] was missing from the Aqu1 annotation. In this case, the 5’ end of the corresponding Aqu1 model is discarded in Aqu2 and the 3’ end extended. The discarded 5’ end encodes the splicing factor 3B subunit and now comprises the adjacent gene. Aqu2 also includes a new member of the POU transcription factor gene family and previously unannotated genes encoding neuronal proteins including the Synapse Differentiation-Induced Protein1-Like (Capucin), a gene expressed in the caudal and putamen brain regions of mouse and human, and a new version of CPEB, a protein involved in memory maintenance [29-32] (Additional file 1: Figure S2A,B).

Amphimedon possesses both conserved and novel transcription termination elements

We identified motifs enriched in 10,274 strict 3’ UTRs that are now annotated in Amphimedon. There are four long AT-rich motifs that are overrepresented in this region, three of which sit between 100 and 60 bp upstream of the transcription termination site (TTS) (Figure 3A,B). These motifs are more abundant than the polyadenylation signal sequence (PAS) consensus sequence (AWUAAA), which is found adjacent to and preceding the TTS (Figure 3A). One of the identified motifs - motif 8 - is a composite version of the polyA signal (AATx5 – Figure 3B) that, as expected, overlaps with the canonical PAS sequence (Figure 3A).
Figure 3

Transcription termination elements overrepresented in Amphimedon queenslandica 3’ UTRs. A) Frequency per 20 bp bin (y-axis) of enriched motifs in mRNA 3’ ends signals 2,000 bp around the transcription termination site (TTS), including the consensus polyadenylation signal (PAS) AWUAAA (orange line). Motif numbers correspond to those in panel B and relate to the motif ranking found by MEME. The most prevalent position of novel motifs 1, 6 and 9 is highlighted in the grey shaded area (−60 to −100 bp relative to TTS). B) Sequence logo, number of occurrences and significance for four AT-rich motifs enriched before the canonical polyadenylation signal (PAS). The significance value is the e-value of the log likelihood ratio of each motif, while the number of sites per class shows the number of 3’ UTRs out of a total of 10,274 strict 3’ UTRs that had a particular motif. C) Cumulative frequency of the PAS consensus sequence (AWUAAA) 500 bp upstream (dashed grey line) or downstream (solid black line) of a set of strict transcription start sites (TSS) on scaffolds longer than 50 kb and not overlapping with other TSSs.

Comparison of the cumulative frequency of the consensus PAS relative to the transcription start site (TSS) reveals PASs accumulate more rapidly upstream than downstream of a set of 3,309 strict TSSs (Figure 3C). This pattern of a lower frequency of PASs on the coding strand is consistent with PAS being associated with transcription termination in A. queenslandica [33].

Alternative splicing is dominated by intron retention

A conservative estimate of alternative splicing (AS) in A. queenslandica was obtained by considering only AS events supported by at least three different assembled transcripts (Additional file 1: Figure S3). Only AS events resulting in the acquisition of an alternative first or last exon were lowly supported, with 98% of these appearing, at most, in two different assembled transcripts (Additional file 1: Figure S3).

Based on these conservative estimates, alternative splicing in A. queenslandica appears to be less prevalent than in many eumetazoans [34], with less than 32% of the total transcripts detected in this study being generated by some form of AS (Figure 4). The large majority of AS events result in the retention of an intron, constituting 45% of all alternative splicing events and 57.1% of all alternatively spliced transcripts (Table 3 and Figure 4). The second most abundant splicing event results in the incorporation of an alternative terminal exon (22.1% of AS events), followed by alternative splice acceptor (17.3%) and donor (12.7%) (Table 3 and Figure 4).
Figure 4

Alternative splicing in Amphimedon queenslandica. A) Graphical depiction of AS event classes in relation to the canonically spliced isoform (black). From top to bottom: intron retention (IntRt - blue), alternative terminal exon (AltTEx - orange), alternative acceptor (AltAc - yellow), alternative donor (AltDo - green), exon-skipping (ExSk – light green), intron end (IntEnd - light orange) and intron start (IntSt - violet). The number of transcripts in each class of AS is shown in the second column and the percentage of all transcripts this represents is shown in parentheses. The total percentage is over 100% as different types of AS sometimes occur in the same transcript. B) Pie chart showing percentage of AS events belonging to the classes shown in panel A. C) Number of transcripts (y-axis) and percentage (number above each bars) of transcripts with AS events (x-axis) exemplified in panel A. Similar to panel A, the total percentage is over 100% as different types of AS events can occur in the same transcript. Colour coding and abbreviations in B and C are identical to those used in panel A.

Table 3

Alternative splicing in Amphimedon queenslandica

Abbreviation

AS Type

AS events*

Number of transcripts

% of AS events

% of AS transcripts +

% of All transcripts

IntRt

Intron retention

12,610

8,643

45.0%

57.1%

18.0%

AltTEx

Alternative Terminal Exon

6,191

4,442

22.1%

29.3%

9.3%

AltAc

Alternative Acceptor

4,843

4,163

17.3%

27.5%

8.7%

AltDo

Alternative Donor

3,562

3,175

12.7%

21.0%

6.6%

ExSk

Exon skipping

513

500

1.8%

3.3%

1.0%

IntSt

Intronic Start

148

148

0.5%

1.0%

0.3%

IntEnd

Intronic End

132

132

0.5%

0.9%

0.3%

*AS events supported by three or more transcripts.

+ The total percentage of transcripts is higher than 100% as different types of AS events can occur in the same transcript.

Discussion

The use of high-coverage developmental transcriptomes has markedly improved the gene annotations in the Amphimedon queenslandica genome, resulting in the refinement of existing gene models and the identification of a large number of previously unannotated genes. Given the high density of genes in the Amphimedon genome, the use of stranded RNA-Seq was proved essential for accurate gene identification and gene model prediction. The integration of CEL-Seq data from 82 developmental samples, spanning from early cleavage through metamorphosis into the juvenile form, further improves the gene models by (i) allowing 3’ UTRs to be extended to regions that have CEL-Seq sequence support and (ii) confirming the developmental expression of new gene models.

Combining stranded-RNA Seq and CEL-Seq data with existing gene models via a pipeline that relies both on de novo and genome-informed assemblies significantly improves the accuracy of existing gene models. Improvements include the addition or extension of 3’ and 5’ UTRs, the identification of missing exons, the removal of incorrectly predicted exons and the refinement of exon/intron boundaries. The high level of transcriptome coverage identified genes not included in previous annotations. This approach also has allowed us to identify gene models that were previously fused or fragmented in Aqu1. Although Aqu2 is primarily based on transcriptome evidence, both Aqu1 and Aqu2 show similar coverage of metazoan orthologues and support the expression of conserved metazoan proteins during Amphimedon development.

A number of biological observations emerge from these new gene annotations. First, the increase in the number of protein-coding genes in the Amphimedon genome has led to the expansion of some gene families, including those encoding developmental transcription factors, such as Arx and POU, and proteins involved in neuron functioning, such as Capucin and CPEB. It is worth noting that although the Aqu2 models have led to a better annotation of metazoan gene families, most conserved gene families were accurately annotated in Aqu1.

Second, the more accurate annotation of untranslated regions in Aqu2 allows for the identification to transcription start and termination sites. Genomic sequences in the vicinity of these sites contribute to the regulation of gene transcription and transcript termination and stability. Core transcriptional elements overlap with TSSs [35-38] and PASs and other motifs in the 3’ UTR control transcript termination, stability and localisation [39,40]. Analysis of the 3’ UTRs in Amphimedon reveals the enrichment of a number of AT-rich motifs 60–100 bp upstream of TTS. These currently appear to be unique to Amphimedon. Further, as observed in vertebrates [33], analysis of the frequency of PAS sequences upstream and downstream of putative TSSs in Amphimedon reveals a disproportionate depletion of PASs in the direction of transcription compared to in the opposite non-coding direction. This is consistent with PAS signals participating in transcription termination in Amphimedon.

Third, the extension of existing genes and the annotation of new genes both have contributed to an overall increase in gene density. Indeed, the Amphimedon genome is the most gene dense animal genome currently known [5]. In addition to having minimal intergenic spacing (median = 0.59 kb), intron size in Amphimedon is markedly smaller than other animals (see [5] and Table 2). Both intergenic and intron size in Amphimedon are more similar to non-metazoan opisthokonts [1,5,23]. Given the basal position of poriferans, these characteristics suggest sponges may have retained genomic features of the first metazoans. Although protein-coding gene content in sponges includes many metazoan innovations [1,5,9-11], their genome organisation and gene structure appears to be more similar to simple unicellular ophistokonts.

Fourth and consistent with the above observation, the level and modes of alternative splicing in Amphimedon is more akin to those found in yeast than in other animals. This sponge has lower proportion of alternative splicing events compared to other animals, particularly those that result in exon-skipping and gene product diversification. These very low levels of exon-skipping are similar to those observed in yeast [41,42] and in contrast to bilaterians, where exon-skipping is often the most prevalent form of AS [42,43]. As an increase in average intron size correlates with increased levels of exon-skipping [34,44], the limited exon-skipping and small intron size in Amphimedon are consistent with these genomic features and processes emerging later in eumetazoan evolution, after the divergence of this and the sponge lineage.

Fifth, the new Aqu2 models greatly expand the number of gene models without orthologues to over 20,000. Nearly all these genes are developmentally expressed based on RNA-Seq and CEL-Seq data. With a paucity of whole genome data from phylum Porifera it is currently difficult to reconstruct the evolutionary history of these genes.

Conclusions

In improving the accuracy of the Amphimedon gene models we have increased the number of full-length genes with accurate transcription start and termination sites. This allows for the future identification and analysis of promoters and other regulatory sequences populating intergenic DNA and UTRs. Combined with experimental manipulation and a detailed analysis of gene expression, the analysis of cis-regulatory DNA provides a means to understand the logic underlying sponge morphogenesis and cell specification and differentiation. When placed in a comparative framework, this knowledge informs our understanding of the evolution of the cell types [24,45] and developmental mechanisms underpinning metazoan body plans.

Methods

Sample collection and sequencing

Adult, juvenile, and competent and pre-competent larvae of Amphimedon queenslandica sponges were collected from Heron Island Reef, Great Barrier Reef, Queensland, Australia as previously described [46]. Total RNA from each developmental stage was extracted using the standard TRIzol regent protocol (Invitrogen) and genomic DNA was removed by DNase treatment. The RNA quality was assessed using the Agilent 2100 Bioanalyzer. RNA was paired-end sequenced using the Illumina HiSeq2000 platform (Illumina, San Diego). All samples were sequenced in a strand sensitive fashion. We additionally sequenced an adult sponge tissue sample at high-depth in an unstranded manner to help detect low-abundance transcripts (Table 1).

De novo transcriptome assembly

Raw paired-end sequences were quality filtered using Trimmomatic [47]. The first 7 bp of each read were cropped and reads were subsequently trimmed if the average quality within a window of 4 bp was below 15. Unpaired reads and reads smaller than 60 bp were discarded. Quality-filtered paired-end reads were assembled de novo using Trinity [20] (Table 1). Each developmental stage was assembled independently with default parameters, with the exception of a lower transcript size cut-off of 200 nt and jaccard-clipping. These assembled transcripts for each developmental stage were aligned and condensed using the PASA pipeline [24], where only transcripts with more than 90% transcript coverage (parameter --MIN_PERCENT_ALIGNED) and 95% identity (parameter --MIN_AVG_PER_ID) to the genome were merged. Peptides were predicted from these transcripts using Transdecoder [48] and used as further evidence for gene annotation (see below).

High-coverage unstranded adult sponge reads were quality checked and trimmed as described above. Remaining reads were independently assembled de novo three times using Trinity with default parameters, using a lower transcript size cut-off of 200 nt and jaccard-clipping, but including a minimum kmer coverage of 2, 4 and 10 (−−min_kmer_cov parameter). The three Trinity assemblies were subsequently merged using CAP3 [49] at 95% similarity and a minimum overlap of 100 bp. These sequences were mapped to the genome using gmap with a minimum of 90% identity [50] and provided as further transcript evidence to EVM [24], but not included in the main PASA transcript set (see Figure 1A, Table 1).

Reference based transcriptome assembly

Quality filtered reads from the four stranded libraries (Table 1) were mapped to the A. queenslandica genome [1] using Tophat2 [51] and assembled using Cufflinks2 [23]. Each developmental stage was assembled separately and shared transcripts were collapsed using Cuffmerge [23]. The gtf file obtained by Cuffmerge was converted to gff3 format and used as further evidence for gene annotation.

Evidenced based gene prediction and UTR annotation

Gene evidence and predicted gene structure were combined using EVM [24]. The evidence included a) ab initio predictions generated by Augustus, SNAP and GenomeScan [1], b) PASA generated consensus transcript assemblies based on both the stranded developmental Trinity assemblies and publically available Sanger ESTs [1], c) the Transdecoder predicted peptides of PASA consensus transcripts, d) the high-depth adult transcriptome and e) the genome-guided gene models generated by Cufflinks. RNA-Seq evidence was strongly favoured over ab initio and other predictions using the weight system incorporated in EVM. The evidence weights are summarized in Table S1. UTRs were added onto the gene models predicted by EVM [24] by two sequential PASA [24] rounds including annotation loading, annotation comparison and annotation updates to maximize incorporation onto gene models predicted by EVM, as per the suggestion of the authors in the PASA pipeline manual (http://pasapipeline.github.io/) (see Figure 1A).

CEL-Seq gene 3’ end extension

CEL-Seq developmental data (stages: cleavage, brown, cloud, spot, late spot, ring, late ring and swimming larvae) for Amphimedon developmental stages were retrieved from NCBI’s Gene Expression Omnibus (GSE54364) and are described in detail in [19]. 24 additional samples spanning from post-settlement postlarvae undergoing metamorphosis into the juvenile form and adult were provided by the Yanai lab (Anavy et al. unpublished). CEL-Seq reads were processed, quality filtered and mapped back to the A. queenslandica genome (ampQue1) using BWA [51] through the CEL-Seq analysis pipeline [52]. To identify transcript ends, we clustered all overlapping reads mapped to the same DNA strand in each individual developmental sample. Developmental stage replicates were processed individually. Clusters with at least 10 reads were retained. Clustered regions were identified in several developmental samples in a stranded fashion; resulting in a total of 74,973 CEL-Seq based clusters. We extended the 3' end of all EVM gene models with annotated 3’ UTRs whose last annotated exon had at least 10 bp overlap with these CEL-Seq clusters, only if their annotated 3' end was shorter than the one supported by CEL-Seq (Figure 1A).

Gene annotation

Open reading frames (ORFs) for all genes were predicted using Transdecoder [48]. All best ORF candidates were analysed for protein domains, signal sequences and transmembrane domain using hmmer 3.0 [53], signalp 4.1 [54], blastp + [55] and tmhmm 2.0 [56], and combined to annotate each ORF using Trinotate ([24]). Novel candidate genes were manually verified as related to other known genes in nr, RefSeq and SwissProt databases using the web interfaces of Blast and PSI-blast [55]. Their protein domains were also verified using the web versions of SMART [57] and InterProScan [58]. The sequences of newly annotated proteins discussed in the text are provided in the Additional file 1: Supplementary material. The complete set of predicted peptides and gene annotations can be accessed at http://amphimedon.qcloud.qcif.edu.au/index.html.

Assembly comparison

The assembly comparison shown in Figure 1B was done by intersecting Aqu1, ab initio gene models (Augustus, SNAP and GenomeScan), NCBI and Aqu2 annotations. An 80% genome coverage threshold was used to account for missing UTR regions in previous annotations. As in some cases a single Aqu1 gene might correspond to two or more Aqu2 genes or vice versa, we have used a hierarchical approach using the following order of precedence: Aqu2; Aqu1; ab initio; and NCBI. Only the original reference (Aqu2) set will be identical in number in the Venn diagram as the original number of elements in the set, while all other comparison sets (Aqu1, ab initio and NCBI) will have more or less elements depending on their overall correspondence with the reference set. Other intersections, such as the number of Aqu1 genes that are not supported in Aqu2, as well as the number of ab initio and NCBI covered in Aqu2 were done using overlapSelect (parameters: −overlapThreshold = 0.8 –strand) from the UCSC toolkit [59].

3’ end motif identification

10,274 3’ UTR sequences overlapping with CEL-seq based clusters found on genomic scaffolds longer than 50 kb were searched for nucleotide motifs using MEME (parameters: −maxsize 20000000 -p 14 -dna -nmotifs 10 -minw 6 -maxw 15 -mod zoops) [60]. We restricted our search to sequences in scaffolds longer than 50 kb to avoid PAS signal depletion due to lack of adjacent sequence. Motif frequency matrices were converted from MEME to Homer format and used to map their frequency around the annotated TTS and TSS using the Homer toolkit [61]. For the cumulative PAS signal distribution analysis, strict TSSs were defined as those of genes found in scaffolds longer than 50 kb whose promoters (100 bp upstream and 50 bp downstream of the TSS) did not overlap with other genes.

Alternative splicing analysis

The four stranded developmental transcriptomes and EST data were combined using the PASA pipeline with the alternative splicing detection option [24]. AS events supported by less than three transcripts were considered as lowly supported and removed from subsequent analyses.

Availability of supporting data

The transcriptome sequencing data has been submitted to NCBI’s Sequence Read Archive (SRA) with accession number SUB596470. The new gene annotations, gene and transcriptome nucleotide and peptide sequences can be downloaded from our website (http://amphimedon.qcloud.qcif.edu.au/downloads.html). The new Aqu2 annotations can be visualized at our local genome browser (http://amphimedon.qcloud.qcif.edu.au/genome_browser.html).

CEL-Seq data can be access through the Gene Expression Omnibus (GEO) with GEO Accession GSE54364 [56].

Abbreviations

EST: 

Expressed sequence tag

AS: 

Alternative splicing

TSS: 

Transcription start site

TTS: 

Transcription termination site

AltTEx: 

Alternative terminal exon

IntRt: 

Intron retention

AltAc: 

Alternative acceptor

AltDo: 

Alternative donor

ExSk: 

Exon-skipping

IntEnd: 

Intron end

IntSt: 

Intron start

UTR: 

Untranslated region

nt: 

nucleotide

bp: 

base pair

Mb: 

Megabase

Declarations

Acknowledgments

We acknowledge the help of Brian J. Haas and Brian M. Couger with usage of the Trinotate pipeline. We thank Nagayasu Nakanishi and members of Itai Yanai’s lab for generating the CEL-Seq data, and Sandie Degnan, Itai Yanai, Marie E. Gauthier, Felipe Aguilera and Federico Gaiti for their critical comments on the manuscript. This work was supported by an Australian Research Council grant to BMD.

Authors’ Affiliations

(1)
Centre for Marine Sciences, School of Biological Sciences, The University of Queensland

References

  1. Srivastava M, Simakov O, Chapman J, Fahey B, Gauthier MEA, Mitros T, et al. The Amphimedon queenslandica genome and the evolution of animal complexity. Nature. 2010;466:720–6.View ArticlePubMed CentralPubMedGoogle Scholar
  2. Srivastava M, Begovic E, Chapman J, Putnam NH, Hellsten U, Kawashima T. The Trichoplax genome and the nature of placozoans. Nature. 2008;454:955–60.View ArticlePubMedGoogle Scholar
  3. Moroz LL, Kocot KM, Citarella MR, Dosung S, Norekian TP, Povolotskaya IS, et al. The ctenophore genome and the evolutionary origins of neural systems. Nature. 2014;510:109–14.View ArticlePubMed CentralPubMedGoogle Scholar
  4. Ryan JF, Pang K, Schnitzler CE, Nguyen A-D, Moreland RT, Simmons DK, et al. The genome of the ctenophore Mnemiopsis leidyi and its implications for cell type evolution. Science. 2013;342:1242592.View ArticlePubMed CentralPubMedGoogle Scholar
  5. Suga H, Chen Z, de Mendoza A, Sebé-Pedrós A, Brown MW, Kramer E, et al. The Capsaspora genome reveals a complex unicellular prehistory of animals. Nat Commun. 2013;4:2325.View ArticlePubMed CentralPubMedGoogle Scholar
  6. King N, Westbrook MJ, Young SL, Kuo A, Abedin M, Chapman J, et al. The genome of the choanoflagellate Monosiga brevicollis and the origin of metazoans. Nature. 2008;451:783–8.View ArticlePubMed CentralPubMedGoogle Scholar
  7. Fairclough SR, Chen Z, Kramer E, Zeng Q, Young S, Robertson HM, et al. Premetazoan genome evolution and the regulation of cell differentiation in the choanoflagellate Salpingoeca rosetta. Genome Biol. 2013;14:R15.View ArticlePubMed CentralPubMedGoogle Scholar
  8. Erwin DH. Early origin of the bilaterian developmental toolkit. Philos Trans R Soc Lond B Biol Sci. 2009;364:2253–61.View ArticlePubMed CentralPubMedGoogle Scholar
  9. Degnan BM, Vervoort M, Larroux C, Richards GS. Early evolution of metazoan transcription factors. Curr Opin in Genet Dev. 2009;19:591–9.View ArticleGoogle Scholar
  10. Sebé-Pedrós A, Ariza-Cosano A, Weirauch MT, Leininger S, Yang A, Torruella G, et al. Early evolution of the T-box transcription factor family. Proc Natl Acad Sci U S A. 2013;110:16050–5.View ArticlePubMed CentralPubMedGoogle Scholar
  11. Richards GS, Degnan BM. The dawn of developmental signaling in the metazoa. Cold Spring Harb Symp Quant Biol. 2009;74:81–90.View ArticlePubMedGoogle Scholar
  12. Simpson TL. The Cell Biology of Sponges. 1st ed. New York: Springer; 1984.View ArticleGoogle Scholar
  13. Ereskovsky AV. The Comparative Embryology of Sponges. New York: Springer; 2010.View ArticleGoogle Scholar
  14. Leys SP, Hill A. The physiology and molecular biology of sponge tissues. Adv Mar Biol. 2012;62:1–56.View ArticlePubMedGoogle Scholar
  15. Philippe H, Derelle R, Lopez P, Pick K, Borchiellini C, Boury-Esnault N, et al. Phylogenomics revives traditional views on deep animal relationships. Curr Biol. 2009;19:706–12.View ArticlePubMedGoogle Scholar
  16. Schierwater B, Eitel M, Jakob W, Osigus H-J, Hadrys H, Dellaporta SL, et al. Concatenated analysis sheds light on early metazoan evolution and fuels a modern “urmetazoon” hypothesis. PLoS Biol. 2009;7, e20.View ArticlePubMedGoogle Scholar
  17. Sperling EA, Peterson KJ, Pisani D. Phylogenetic-signal dissection of nuclear housekeeping genes supports the paraphyly of sponges and the monophyly of Eumetazoa. Mol Biol Evol. 2009;26:2261–74.View ArticlePubMedGoogle Scholar
  18. Degnan BM, Adamska M, Craigie A, Degnan SM, Fahey B, Gauthier M, et al. The demosponge Amphimedon queenslandica: reconstructing the ancestral metazoan genome and deciphering the origin of animal multicellularity. CSH Protoc 2008, 2008:pdb.emo108. http://cshprotocols.cshlp.org/citmgr?gca=protocols%3B2008%2F12%2Fpdb.emo108.
  19. Anavy L, Levin M, Khair S, Nakanishi N, Fernandez-Valverde SL, Degnan BM, et al. BLIND ordering of large-scale transcriptomic developmental timecourses. Development. 2014;141:1161–6.View ArticlePubMedGoogle Scholar
  20. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.View ArticlePubMed CentralPubMedGoogle Scholar
  21. Haas BJ, Delcher AL, Mount SM, Wortman JR, Smith RK, Hannick LI, et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 2003;31:5654–66.View ArticlePubMed CentralPubMedGoogle Scholar
  22. Yuen B, Bayes JM, Degnan SM. The characterization of sponge NLRs provides insight into the origin and evolution of this innate immune gene family in animals. Mol Biol Evol. 2014;31:106–20.View ArticlePubMed CentralPubMedGoogle Scholar
  23. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2012;31:46–53.View ArticlePubMedGoogle Scholar
  24. Haas BJ, Salzberg SL, Zhu W, Pertea M, Allen JE, Orvis J, et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 2008;9:R7.View ArticlePubMed CentralPubMedGoogle Scholar
  25. Murakami R, Okumura T, Uchiyama H. GATA factors as key regulatory molecules in the development of Drosophila endoderm. Dev Growth Differ. 2005;47:581–9.View ArticlePubMedGoogle Scholar
  26. Martindale MQ, Pang K, Finnerty JR. Investigating the origins of triploblasty: “mesodermal” gene expression in a diploblastic animal, the sea anemone Nematostella vectensis (phylum, Cnidaria; class, Anthozoa). Development. 2004;131:2463–74.View ArticlePubMedGoogle Scholar
  27. Nakanishi N, Sogabe S, Degnan BM. Evolutionary origin of gastrulation: insights from sponge development. BMC Biol. 2014;12:26.View ArticlePubMed CentralPubMedGoogle Scholar
  28. Larroux C, Luke GN, Koopman P, Rokhsar DS, Shimeld SM, Degnan BM. Genesis and expansion of metazoan transcription factor gene classes. Mol Biol Evol. 2008;25:980–96.View ArticlePubMedGoogle Scholar
  29. Kandel ER. The molecular biology of memory: cAMP, PKA, CRE, CREB-1, CREB-2, and CPEB. Mol Brain. 2012;5:14.View ArticlePubMed CentralPubMedGoogle Scholar
  30. Bestman JE, Cline HT. The RNA binding protein CPEB regulates dendrite morphogenesis and neuronal circuit assembly in vivo. Proc Natl Acad Sci U S A. 2008;105:20494–9.View ArticlePubMed CentralPubMedGoogle Scholar
  31. Keleman K, Krüttner S, Alenius M, Dickson BJ. Function of the Drosophila CPEB protein Orb2 in long-term courtship memory. Nature Neurosci. 2007;10:1587–93.View ArticlePubMedGoogle Scholar
  32. Si K, Giustetto M, Etkin A, Hsu R, Janisiewicz AM, Miniaci MC, et al. A neuronal isoform of CPEB regulates local protein synthesis and stabilizes synapse-specific long-term facilitation in Aplysia. Cell. 2003;115:893–904.View ArticlePubMedGoogle Scholar
  33. Ntini E, Järvelin AI, Bornholdt J, Chen Y, Boyd M, Jørgensen M, et al. Polyadenylation site-induced decay of upstream transcripts enforces promoter directionality. Nat Struct Mol Biol. 2013;20:923–8.View ArticlePubMedGoogle Scholar
  34. Kim E, Magen A, Ast G. Different levels of alternative splicing among eukaryotes. Nucleic Acids Res. 2006;35:125–31.View ArticlePubMed CentralPubMedGoogle Scholar
  35. Lenhard B, Sandelin A, Carninci P. Metazoan promoters: emerging characteristics and insights into transcriptional regulation. Nat Rev Genet. 2012;13:233–45.PubMedGoogle Scholar
  36. Kadonaga JT. Perspectives on the RNA polymerase II core promoter. WIREs Dev Biol. 2011;1:40–51.View ArticleGoogle Scholar
  37. Juven-Gershon T, Kadonaga JT. Regulation of gene expression via the core promoter and the basal transcriptional machinery. Dev Biol. 2010;339:225–9.View ArticlePubMed CentralPubMedGoogle Scholar
  38. Smale ST, Kadonaga JT. The RNA polymerase II core promoter. Annu Rev Biochem. 2003;72:449–79.View ArticlePubMedGoogle Scholar
  39. Tian B, Graber JH. Signals for pre-mRNA cleavage and polyadenylation. WIREs RNA. 2012;3:385–96.View ArticlePubMedGoogle Scholar
  40. Alonso CR. A complex “mRNA degradation code” controls gene expression during animal development. Trends Genet. 2012;28:78–88.View ArticlePubMedGoogle Scholar
  41. Bitton DA, Rallis C, Jeffares DC, Smith GC, Chen YYC, Codlin S, et al. LaSSO, a strategy for genome-wide mapping of intronic lariats and branch points using RNA-seq. Genome Res. 2014;24:1169–79.View ArticlePubMed CentralPubMedGoogle Scholar
  42. Awan AR, Manfredo A, Pleiss JA. Lariat sequencing in a unicellular yeast identifies regulated alternative splicing of exons that are evolutionarily conserved with humans. Proc Natl Acad Sci U S A. 2013;110:12762–7.View ArticlePubMed CentralPubMedGoogle Scholar
  43. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science. 2008;321:956–60.View ArticlePubMedGoogle Scholar
  44. Keren H, Lev-Maor G, Ast G. Alternative splicing and evolution: diversification, exon definition and function. Nat Rev Genet. 2010;11:345–55.View ArticlePubMedGoogle Scholar
  45. Conaco C, Bassett DS, Zhou H, Arcila ML, Degnan SM, Degnan BM, et al. Functionalization of a protosynaptic gene expression network. Proc Natl Acad Sci U S A. 2012;109 Suppl 1:10612–8.View ArticlePubMed CentralPubMedGoogle Scholar
  46. Adamska M, Larroux C, Adamski M, Green K, Lovas E, Koop D, et al. Structure and expression of conserved Wnt pathway components in the demosponge Amphimedon queenslandica. Evol Dev. 2010;12:494–518.View ArticlePubMedGoogle Scholar
  47. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.View ArticlePubMed CentralPubMedGoogle Scholar
  48. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature Protoc. 2013;8:1494–512.View ArticleGoogle Scholar
  49. Huang X, Madan A. CAP3: A DNA sequence assembly program. Genome Res. 1999;9:868–77.View ArticlePubMed CentralPubMedGoogle Scholar
  50. Wu TD, Watanabe CK. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 2005;21:1859–75.View ArticlePubMedGoogle Scholar
  51. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.View ArticlePubMed CentralPubMedGoogle Scholar
  52. Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2:666–73.View ArticlePubMedGoogle Scholar
  53. Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14:755–63.View ArticlePubMedGoogle Scholar
  54. Petersen TN, Brunak S, Heijne von G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Meth. 2011;8:785–6.View ArticleGoogle Scholar
  55. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.View ArticlePubMed CentralPubMedGoogle Scholar
  56. Krogh A, Larsson B, Heijne von G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305:567–80.View ArticlePubMedGoogle Scholar
  57. Schultz J, Milpetz F, Bork P, Ponting CP. SMART, a simple modular architecture research tool: identification of signaling domains. Proc Natl Acad Sci U S A. 1998;95:5857–64.View ArticlePubMed CentralPubMedGoogle Scholar
  58. Zdobnov EM, Apweiler R. InterProScan–an integration platform for the signature-recognition methods in InterPro. Bioinformatics. 2001;17:847–8.View ArticlePubMedGoogle Scholar
  59. Kuhn RM, Haussler D, Kent WJ. The UCSC genome browser and associated tools. Brief Bioinform. 2013;14:144–61.View ArticlePubMed CentralPubMedGoogle Scholar
  60. Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol. 1994;2:28–36.PubMedGoogle Scholar
  61. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38:576–89.View ArticlePubMed CentralPubMedGoogle Scholar

Copyright

© Fernandez-Valverde et al.; licensee BioMed Central. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement