An aquatic life genome sequencing suggests a complete loss of genes for ethylene biosynthesis and signalling pathways in the seagrasses, Zostera muelleri and Zostera marina, a new model for hormone studies.

Seagrasses are flowering plants which grow fully submerged in the marine environment. They have evolved a range of adaptations to environmental challenges including light attenuation through water, the physical stress of wave action and tidal currents, high concentrations of salt, oxygen deficiency in marine sediment, and water-borne pollination. Although, seagrasses are a key stone species of the costal ecosystems, many questions regarding seagrass biology and evolution remain unanswered. Genome sequence data for the widespread Australian seagrass species Zostera muelleri were generated and the unassembled data were compared with the annotated genes of five sequenced plant species (Arabidopsis thaliana, Oryza sativa, Phoenix dactylifera, Musa acuminata, and Spirodela polyrhiza). Genes which are conserved between Z. muelleri and the five plant species were identified, together with genes that have been lost in Z. muelleri. The effect of gene loss on biological processes was assessed on the gene ontology classification level. Gene loss in Z. muelleri appears to influence some core biological processes such as ethylene biosynthesis. This study provides a foundation for further studies of seagrass evolution as well as the hormonal regulation of plant growth and development.

Introduction

Seagrasses are a polyphyletic group of monocotyledonous plants which are capable of living underwater in marine environments (Larkum et al., 2006; Waycott et al., 2006). They are descendants of terrestrial plants which returned to life in the aquatic environment and consist of ~60 species, most of which have long narrow leaves and grow in large submerged meadows (Wissler et al., 2011). All seagrasses belong to the order Alismatales, which also includes several terrestrial plant species, 11 families of aquatic freshwater species, and four families that are fully marine. The marine families include Posidoniaceae, Zosteraceae, Hydrocharitaceae, and Cymodoceaceae (Larkum et al., 2006; Waycott et al., 2006).

Living fully submerged in a marine environment poses many challenges, including light attenuation through the water column (Dalla Via et al., 1998), resisting the forces of wave action and tidal currents (Waycott et al., 2006), high concentrations of salt in the surrounding seawater, and growing in anoxic marine sediment rich in sulphide (Terrados et al., 1999). Seagrasses have evolved to grow and reproduce under these difficult environmental conditions, emphasizing a unique/novel morphology, physiology, and biochemistry compared with terrestrial plants. They have developed aerenchyma in their leaves, roots, and rhizomes to tolerate anoxia; stomata are absent; and they have reduced stamen and corolla and elongated pollen without an exine layer to facilitate hydrophilous pollination. Living in a saline environment, they have adapted to elevated ionic pressure, as well as high sodium and chloride concentrations. They can take up nitrate from the surrounding water and ammonia from the sediment, and survive in a reducing/anoxic rhizosphere with high levels of sulphides. Many of the fundamental evolutionary questions involving the unique biology of seagrasses remain open, including adaptation to living in a saline environment and the evolution of stress response strategies (Waycott et al., 2006). The identification of seagrass genes and their related cellular processes may provide insights into seagrass evolution and adaptation to life in the marine realm. The characterization of genes that are lost in seagrass compared with other plant species suggests which molecular processes are no longer active or have significantly diverged in seagrass, while the identification of conserved genes between seagrasses and other plants can help resolve processes that are indispensable to plant life both on land and in the marine realm.

Previous genomic analysis of seagrasses has been performed using expressed sequence tags (ESTs) (Reusch et al., 2008; Franssen et al., 2011, 2014; Wissler et al., 2011; Gu et al., 2012; Kong et al., 2013). Recent years have seen major advances in DNA sequence technologies. Many major species now have draft genome assemblies, and DNA sequencing data are providing valuable insights into plant physiology and evolution. Draft genome assembly projects remain expensive and time consuming, and are unlikely to be undertaken for many of the non-model species for some time. However, methods employing the analysis of unassembled sequence data still permit valuable comparative genomic analysis without the expense or time required for whole-genome assembly. In this study, a comparative genomics approach using unassembled whole-genome shotgun sequence data has been applied to characterize gene conservation and loss in the marine seagrass Zostera muelleri in comparison with the genomes of five sequenced plants: Arabidopsis thaliana, Oryza sativa (rice), Musa acuminata (banana), Phoenix dactylifera (date palm), and Spirodela polyrhiza (duckweed). Through the identification of genes that are either lost in Z. muelleri or remain conserved, insights can be gained into Z. muelleri evolution and adaptation to the marine environment.

The results obtained suggest loss of ethylene biosynthesis and signalling pathways in Z. muelleri, which is supported by the analysis of data for Zostera marina and Zostera noltii, together suggesting loss of ethylene production across the Zosteraceae. Ethylene is a gaseous hormone, a core regulator of many developmental processes, and the ethylene signalling network was thought to be ubiquitously present throughout higher plants (Lin et al., 2009). Seagrasses belonging to the genus Zostera present a new model for the study of ethylene signalling and hormone network signalling plasticity, and further studies may provide insights into the physiological and biochemical impacts of life without ethylene.

Materials and methods

Genome sequence data

Zostera muelleri ssp. capricorni (Jacobs et al., 2006) plants were collected from Pelican Banks in Gladstone Harbour (Queensland, Australia) in November 2011, transferred with rhizomes attached in a 5–10cm deep sediment layer into 1 litre rectangular, clear plastic food storage containers, and delivered on the same day to the University of Technology Sydney. The plants were acclimatized for 2 months in an aerated and temperature-controlled mesocosm under Sydney summer conditions with weekly partial seawater changes. Leaf blades of a single plant were snap-frozen and stored in liquid nitrogen for later stage DNA extraction with a soil sample DNA extraction kit (http://www.mobio.com/soil-dna-isolation/powersoil-dna-isolation-kit.html) according to the manufacturer’s instructions. Prior to DNA extraction, the plant material was ground to a fine powder in liquid nitrogen with a pre-chilled mortar and pestle. Genomic DNA was sequenced using an Illumina HiSeq 2000-SBS v3.0 sequencer with 100bp paired-end (PE) technology and an insert size of 304bp at the Ramaciotti Centre at the University of New South Wales (UNSW; NCBI:PRJNA253152). The libraries for genome sequencing were prepared with the Illumina Tru-seq DNA-seq kit.

Zostera marina sequencing reads were downloaded from the NCBI Short Read Archive (SRA: SRR494397). A subset of the reads (PE, 140bp in length) totalling ~30× coverage was used for analysis. Furthermore, the coding sequences (CDSs, primary transcript) for A. thaliana (TAIR 10; Arabidopsis Genome Initiative, 2000), O. sativa (MSU v7; International Rice Genome Sequencing, 2005), S. polyrhiza (v2; Wang et al., 2014), Solanum lycopersicum (tomato; ITAG2.3; (Tomato Genome Consortium, 2012), P. dactylifera (v3), M. acuminata (v1; D’Hont et al., 2012), and Nelumbo nucifera (lotus; v2; Ming et al., 2013) were downloaded (the full list of ftp addresses are available in the Supplementary data at JXB online).

Genome size and coverage estimation

Genome sizes of Z. muelleri and Z. marina were estimated using k-mer analysis (Zhang et al., 2013). K-mer distribution was obtained using Jellyfish v2.0 (Marçais and Kingsford, 2011). The k-mer size used was 21 (Supplementary Figs S1, S2 at JXB online). The percentage of bases not sequenced at a given coverage was calculated according to the following probability function: P(Y=y)=C^y∙e^–C/y!, y=0 (y is the number of times a base is sequenced, C is coverage) (Lander and Waterman, 1988)

Short read discontiguous (dc-) megaBLAST mapping evaluation

Two different types of BLAST (BLAST+ v2.28) (Camacho et al., 2009) comparison searches were performed. In the first, simulated tomato whole-genome shotgun sequencing reads were mapped to the A. thaliana coding sequences (RvsCDS comparison). In the second, full-length tomato CDSs were compared with A. thaliana CDSs. Short read sequence data were simulated using the tomato genome as the reference using wgsim (Li et al., 2009), resulting in read-lengths of 100bp and total coverage of ~15×. Reads were mapped to the A. thaliana CDSs using dc-megaBLAST (Ma et al., 2002; Camacho et al., 2009) (e-value 1e-5). Only best hits were retained, and horizontal coverage of CDSs (the fraction of the CDSs which had reads mapped to them) was calculated using a custom python script calculate_blast_coverage.py. CDSs with only one read mapping, or multiple reads mapping, all of them having the same start and end co-ordinates were considered to have horizontal coverage 0.

Solanum lycopersicum (tomato) CDSs were compared with A. thaliana CDSs using dc-megaBLAST (e-value 1e-5) and only best hits were retained.

The two BLAST results were used to evaluate the feasibility of using short read mapping as replacement for the whole-length CDS comparison. To evaluate the robustness of the proposed approach, discrepancy rates (DRs) between RvsCDS and CDSvsCDS results were calculated according to the following formulae:

DR genes lost [DRL]=(number of genes defined as lost based on horizontal coverage in RvsCDS comparison that had a significant hit in the CDSvsCDS comparison)/(total number of genes defined as lost in RvsCDS comparison)

DR genes conserved [DRC]=(number of genes defined as conserved based on horizontal coverage in RvsCDS comparison that had no significant hit in the CDSvsCDS comparison)/(total number of genes defined as conserved in RvsCDS comparison)

The DR values were plotted against different horizontal coverage cut-off values and used to define optimal horizontal coverage values for further analysis.

Calculation of CDS coverage

Sequencing reads from Z. muelleri and Z. marina were compared with the CDSs from five plant species: A. thaliana, O. sativa, P. dactylifera, M. acuminata, and S. polyrhiza using dc-megaBLAST (e-value 1e-5). Only best hits were retained, and CDS coverage was determined as previously described in the section on short read discontiguous (dc-) megaBLAST mapping evaluation.

Orthologous gene clusters (OGCs) construction

A set of genes conserved between the seven species (monocot: O. sativa, P. dactylifera, M. acuminata, and S. polyrhiza; and dicot: A. thaliana, S. lycopersicum, and N. nucifera) was identified using reciprocal best BLAST (RBB; BLASTP) with an e-value 1e-5 threshold between A. thaliana genes and genes from the remaining six species. If an A. thaliana gene had more than one RBB hit within the same species (more than one top score in reciprocal searches based on the e-value), bit scores were considered; the gene with the highest bit score and all the genes within 10 points of the highest bit score were included as RBB hits. Genes were assigned into clusters, each containing one A. thaliana gene and its putative orthologues in the six remaining species. A subset of OGCs including only clusters which contained at least one gene originating from a monocot species was extracted and termed OGCsM.

Identification of genes lost and conserved

For each gene in the OGCsM, previously calculated CDS coverages were inspected across A. thaliana and all the monocot orthologous genes present in the cluster. If the average coverage across all CDSs was <2%, the cluster was considered to represent a gene lost in seagrass. If the average coverage across all CDSs was >50%, the cluster was considered to represent a gene conserved in seagrass. OGCsM rather the full set of OGCs was considered since Z. marina and Z. muelleri are monocots and dc-megaBLAST comparison against other monocot species is more likely to be successful. The cut-off values were chosen based on calculated DRs. Each OGCM consisted of at least two genes (the A. thaliana gene and at least one orthologue in the monocot species). Therefore, to be considered as a lost or conserved gene it had to be present in A. thaliana and at least one of the four remaining species (O. sativa, P. dactylifera, M. acuminata, and S. polyrhiza). The approach used allows identification of genes lost and conserved using unassembled genomic data. However, it is important to note that while this approach will identify genes which are lost or conserved relative to the existing reference gene set, it will not discover newly evolved genes.

Gene ontology annotation

The A. thaliana orthologue from each identified cluster was used to derive gene ontology (GO) annotation (Ashburner et al., 2000) available via TAIR (Lamesch et al., 2012) (version available on 7 March 2014, GO terms with NR and ND evidence codes were filtered out). The topGO R package (Alexa et al., 2006) available via Bioconductor (Gentleman et al., 2004) v2.14 was used to test for enrichment of GO annotation terms using all identified OGCsM clusters as background. Enrichment was assessed using the Fisher exact test (using weight count).

Analysis of ESTs from the Dr Zompo database

ESTs representing the A, B, and C labelled data sets for Z. marina in the Dr Zompo database and the A data set for Z. noltii were downloaded from the Dr Zompo EST database (Wissler et al., 2009). UCLUST (Edgar, 2010) (identity threshold 0.99) was used to remove redundant ESTs. RBB hits, as described above, were identified between A. thaliana CDSs and Z. marina and Z. noltii ESTs.

Analysis of ESTs from three terrestrial Alismatales species

Assembled transcriptomes of Anthurium andraeanum (Tian et al., 2013) (PRJNA24104) and Zantedeschia aethiopica (PRJNA205467) were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/). UCLUST (v7.0; Edgar, 2010; identity threshold 0.99) was used to remove redundant ESTs. RBB hits, as described above, were identified between A. thaliana CDSs and ESTs. Unassembled sequencing reads from Amorphophallus bulbifer were downloaded from SRA (SRR553186). Genes present were identified using the same strategy as described above for Z. muelleri and Z. marina genomes. Mirroring the threshold of <2% to consider a gene as lost, a threshold of ≥2% was used to consider a gene as present.

Multiple sequence alignments of EIN3-like 1 (EIL1) orthologues

Multiple sequence alignment between Z. marina and Z. noltii EIL1 orthologues, identified from the Dr Zompo EST database, EIN3/EIL1 orthologues in the seven monocot and dicot species identified in OGCs, and EIN3 orthologues identified in A. andraeanum and Z. aethiopica transriptomes was performed using T-coffee (Notredame et al., 2000) in Jalview (v2.8.1) (Waterhouse et al., 2009). EIL1 and EIN3 were identified based on RBB hits with A. thaliana genes. The transcripts corresponding to EIL1 and EIN3 were translated in all six reading frames using EXPASY (http://www.expasy.org/). The longest open reading frame (ORF) was chosen for further analysis.

Results

Estimated genome coverage

In this study, whole-genome sequencing of Z. muelleri was conducted, yielding a total of 39 Gbp of sequence data (191 141 643 read pairs), with an average coverage of ~43×. Additionally, ~30× genome coverage Z. marina sequence data were used for comparative analysis and genome size estimation. The estimated genome size of Z. muelleri based on the K-mer distribution (Huang et al., 2009; Potato Genome Sequencing Consortium, 2011; Parkin et al., 2014) is ~900 Mbp, which is around twice the size of the Z. marina genome, which is estimated to be 430 Mbp based on the K-mer distribution. With the estimated coverages being 30× and above, all of the two genomes should be sampled according to Lander–Waterman statistics (Lander and Waterman, 1988) (at 30× coverage the faction of the genome that will not be sequenced is 9.4e–12%), so the gene loss presented here is unlikely to be due to uneven sampling of the genome.

Evaluation of short read mapping as a gene conservation/loss detection tool

In order to evaluate the feasibility and robustness of using short read mapping as a replacement for full-length CDS comparison as a gene loss/conservation detection tool, two BLAST comparison searches were performed. Tomato was chosen for simulations because of a similar estimated divergence time (~120–130 million years) between tomato and A. thaliana, and between Alismatales (which include Z. muelleri and Z. marina) and core monocots (Janssen and Bremer, 2004; Wang et al., 2014). In the first comparison, simulated tomato whole-genome sequencing reads were mapped against A. thaliana CDSs (RvsCDS comparison). In the second comparison, full-length tomato CDSs were compared with A. thaliana CDSs (CDSvsCDS comparison). The DRs between RvsCDS and CDSvsCDS search results were calculated using different horizontal coverage cut-off values and are presented in Fig. 1. Based on the DR values, coverage cut-offs were defined as 2% for genes to be considered lost.

Fig. 1.

Discrepancy rates (DRs) calculated based on tomato data for genes lost (DRL) (A) and conserved (DRC) (B) using different horizontal coverage cut-offs.

OGC construction and identification of genes lost and conserved

A total of 17 522 OGCs were identified. A total of 16 007 clusters which contained at least one orthologue representing a monocot species (OGCsM) were found. A list of OGCs genes can be found in Supplementary Table S1 at JXB online. A total of 4091 genes conserved and 2147 genes lost between Z. muelleri and the five other plant species (A. thaliana, O. sativa, P. dactylifera, M. acuminata, and S. polyrhiza) were identified, respectively. Lists of genes conserved and lost can be found in Supplementary Tables S2 and S3 at JXB online, respectively.

GO analysis of genes conserved suggests conservation of fundamental biological processes

GO analysis identified 175 significantly enriched (P<0.05) biological process terms within the conserved gene data set. The 39 GO terms with the lowest P-values are presented in Table 1, and a full list can be found in Supplementay Table S4 at JXB online. Biological processes significantly enriched in genes conserved include: glucose metabolic processes, ribosome biogenesis, translation, photosynthesis, and response to salt stress.

Table 1.

Top 39 significantly enriched GO terms corresponding to the genes predicted to be conserved in Z. muelleri compared with five other plant species and the corresponding P-values

GO ID	Term	P-value
GO:0006091	Generation of precursor metabolites and energy	<1e-30
GO:0006094	Gluconeogenesis	<1e-30
GO:0046686	Response to cadmium ion	<1e-30
GO:0006412	Translation	<1e-30
GO:0019320	Hexose catabolic process	<1e-30
GO:0010498	Proteasomal protein catabolic process	<1e-30
GO:0070271	Protein complex biogenesis	<1e-30
GO:0051788	Response to misfolded protein	3.80E-22
GO:0016192	Vesicle-mediated transport	4.30E-21
GO:0015979	Photosynthesis	1.20E-20
GO:1901605	α-Amino acid metabolic process	2.20E-19
GO:0043094	Cellular metabolic compound salvage	9.90E-19
GO:0007030	Golgi organization	6.40E-17
GO:0009651	Response to salt stress	7.30E-17
GO:0019941	Modification-dependent protein catabolic process	1.20E-16
GO:0046034	ATP metabolic process	3.30E-16
GO:0006195	Purine nucleotide catabolic process	3.80E-16
GO:0009207	Purine ribonucleoside triphosphate catabolic process	8.80E-16
GO:0015991	ATP hydrolysis-coupled proton transport	2.10E-15
GO:0001510	RNA methylation	5.30E-15
GO:0009225	Nucleotide-sugar metabolic process	1.10E-14
GO:0008104	Protein localization	1.20E-14
GO:0042254	Ribosome biogenesis	1.30E-14
GO:0007264	Small GTPase-mediated signal transduction	1.60E-14
GO:0015748	Organophosphate ester transport	3.30E-14
GO:0043648	Dicarboxylic acid metabolic process	6.20E-13
GO:0046129	Purine ribonucleoside biosynthetic process	6.90E-13
GO:0006354	DNA-templated transcription, elongation	1.20E-12
GO:0007010	Cytoskeleton organization	2.20E-12
GO:0080147	Root hair cell development	3.00E-12
GO:0051645	Golgi localization	1.40E-11
GO:0051646	Mitochondrion localization	1.40E-11
GO:0060151	Peroxisome localization	1.40E-11
GO:1901293	Nucleoside phosphate biosynthetic process	2.70E-11
GO:0006833	Water transport	2.20E-10
GO:0051273	β-Glucan metabolic process	6.50E-10
GO:0009240	Isopentenyl diphosphate biosynthetic process	1.50E-08
GO:0006779	Porphyrin-containing compound biosynthetic process	1.80E-08
GO:0006740	NADPH regeneration	2.30E-08

GO analysis of genes lost in Z. muelleri points to modification of ethylene biosynthesis and signalling

The list of A. thaliana representatives of genes lost in Z. muelleri was tested for over-representation of GO terms. In total, 28 GO terms in the biological process category were significantly over-represented (P<0.05). The GO terms and associated confidence values are presented in Table 2. The biological processes most influenced by gene loss include lipid transport, ethylene biosynthesis and signalling, response to iron ion, and defence responses. Out of 78 genes in the OGCsM which are associated with ethylene biosynthesis, 23 are lost in Z. muelleri. Additionally out of 10 genes involved in the regulation of ethylene-mediated signalling, six are lost in Z. muelleri.

Table 2.

Significantly enriched GO terms corresponding to the genes predicted to be lost in Z. muelleri

GO ID	Term	P-value
GO:0006869	Lipid transport	2.00E-09
GO:0009693	Ethylene biosynthetic process	7.20E-07
GO:0042218	1-Aminocyclopropane-1-carboxylate biosynthetic process	8.60E-06
GO:0010105	Negative regulation of ethylene-activated signalling pathway	0.00012
GO:0009807	Lignin biosynthetic process	0.00015
GO:0010044	Response to aluminium ion	0.00041
GO:0071281	Cellular response to iron ion	0.0007
GO:0048451	Petal formation	0.00085
GO:0048453	Sepal formation	0.00085
GO:0002237	Response to molecule of bacterial origin	0.00102
GO:0080027	Response to herbivore	0.00112
GO:0010227	Floral organ abscission	0.00135
GO:0009813	Flavonoid biosynthetic process	0.00161
GO:0009626	Plant-type hypersensitive response	0.0029
GO:0009408	Response to heat	0.00345
GO:0052544	Defence response by callose deposition in cell wall	0.00374
GO:0006952	Defence response	0.00504
GO:0042542	Response to hydrogen peroxide	0.00514
GO:0015824	Proline transport	0.00848
GO:0010941	Regulation of cell death	0.01416
GO:0009625	Response to insect	0.01477
GO:0005983	Starch catabolic process	0.01486
GO:0001101	Response to acid	0.01569
GO:0009611	Response to wounding	0.01739
GO:0009736	Cytokinin-activated signalling pathway	0.0264
GO:2000038	Regulation of stomatal complex development	0.03496
GO:0036294	Cellular response to decreased oxygen levels	0.04875
GO:0010375	Stomatal complex patterning	0.04875

This enrichment analysis suggests that ethylene biosynthesis and signalling is highly impacted by gene loss in Z. muelleri. Therefore, a detailed analysis was performed on 27 core genes associated with ethylene biosynthesis and signalling (Table 3).

Table 3.

Summary of 27 genes involved in ethylene synthesis and signalling which were used in the analysisThree categories are possible: gene present (+), gene absent (–), and information not available (N/A).

Gene ID	Protein name	Function	OGCsM	Z. muelleri genome	Z. marina genome	Z. marina ESTs	Z. noltii ESTs
AT1G12010	ACO	ACC oxidase	–	N/A	N/A	–	–
AT2G19590	ACO1	ACC oxidase	+	–	–	–	–
AT1G62380	ACO2	ACC oxidase	+	–	–	–	–
AT1G05010	ACO4	ACC oxidase	+	–	–	–	–
AT1G77330	ACO5	ACC oxidase	+	–	–	–	–
AT3G61510	ACS1	ACC synthase	+	–	–	–	–
AT1G01480	ACS2	ACC synthase	+	–	–	–	–
AT2G22810	ACS4	ACC synthase	+	–	–	–	–
AT5G65800	ACS5	ACC synthase	+	–	–	–	–
AT4G11280	ACS6	ACC synthase	+	–	–	–	–
AT4G26200	ACS7	ACC synthase	+	–	–	–	–
AT4G37770	ACS8	ACC synthase	+	–	–	–	–
AT3G49700	ACS9	ACC synthase	+	–	–	–	–
AT1G62960	ACS10	No ACC synthase activity	+	+	+	+	+
AT4G08040	ACS11	ACC synthase	+	–	–	–	–
AT5G51690	ACS12	No ACC synthase activity	+	+	+	–	–
AT2G40940	ERS1	Ethylene receptor	+	–	–	–	–
AT1G04310	ERS2	Ethylene receptor	–	N/A	N/A	–	–
AT1G66340	ETR1	Ethylene receptor	+	–	–	–	–
AT3G23150	ETR2	Ethylene receptor	+	–	–	–	–
AT3G04580	EIN4	Ethylene receptor	+	–	–	–	–
AT5G03730	CTR1	Raf-like kinase	+	+	–	–	–
AT5G03280	EIN2	Signal transducer	+	–	–	–	–
AT3G20770	EIN3	Transcription factor	+	+	+	–	–
AT2G27050	EIL1	Transcription factor	+	+	+	+	+
AT5G21120	EIL2	Transcription factor	–	N/A	N/A	–	–
AT1G73730	EIL3	Transcription factor	+	+	+	+	–

The S. polyrhiza genome and trancriptomes of three terrestrial species belonging to the order Alismatales contain genes associated with ethylene biosynthesis

Spirodela polyrhiza is a close relative of seagrasses (both belong to the order Alismatales) and its genome was recently sequenced (Wang et al., 2014). It was postulated that another member of the genus Spirodela, S. oligorrhiza, produces ethylene under stress conditions via an alternative pathway without the involvement of 1-aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS) and ACC oxidase (ACO) (Mattoo et al., 1992). However, an S. polyrhiza orthologue of both ACS (Spipo24G0002100) and ACO (Spipo23G0011700) was identified in the OGCsM. Additionally, orthologues of ethylene receptors (Spipo6G0049300 and Spipo1G0021500), CTR1 (Spipo0G0009700), and EIN2 (Spipo8G0029200) were also identified in S. polyrhiza.

Genes associated with ethylene synthesis and signalling are present in the transcriptomes of three terrestrial Alismatales species: A. andraeanum, Z. aethiopica, and A. bulbifer. In A. andraeanum, transcripts corresponding to orthologues of ACO, ethylene receptors, EIN2, and EIN3 were detected. In Z. aethiopica, transcripts corresponding to orthologues of ACO, ethylene receptors, CTR1, EIN2, EIN3, and EIL3 were found, while in A. bulbifer transcripts corresponding to orthologues of ACO, ACS, ethylene receptors, CTR1, EIN2, EIN3, EIL1, and EIL3 could be detected.

Multiple genes involved in ethylene biosynthesis and signalling are lost in Z. muelleri and Z. marina

Genes lost in Z. muelleri include all orthologues of the genes found in OGCsM encoding ACS (encoding orthologues of: ACS1, ACS2, ACS4, ACS5, ACS6, ACS7, ACS8, ACS9, and ACS11) and all orthologues of the genes found in OGCsM encoding ACO (encoding orthologues of: ACO1, ACO2, ACO4, and ACO5). Additionally, orthologues of OGCsM genes associated with the regulation of ethylene signalling are lost in Z. muelleri. The list includes orthologues of four well characterized ethylene signalling molecules (ETR1, ETR2, ERS1, and EIN4). The orthologue of an ethylene signal transducer protein EIN2 is also missing in Z. muelleri. Orthologues of transcription factors EIN3, EIL1, and EIL3 appear to be present in Z. muelleri (Table 3).

Using the same read mapping method, the presence of orthologues of the genes listed in Table 3 was tested for in the genome of another seagrass species, Z. marina. The genes encoding orthologues of ACS1, ACS2, ACS4, ACS5, ACS6, ACS7, ACS8, ACS9, ACS11, ACO1, ACO2, ACO4, ACO5, ETR1, ETR2, ERS1, EIN4, CTR1, and EIN2 also appear to be absent in the Z. marina genome.

Analysis of ESTs supports loss of ethylene biosynthesis and signalling in Z. marina and Z. noltii

RBB searches between A. thaliana CDSs and Z. marina ESTs revealed 10 074 RBB relationships in total. The only transcripts corresponding to proteins involved in ethylene biosynthesis and signalling that were identified in the Z. marina EST database correspond to the orthologues of EIL1 and EIL3, similar to the observations in Z. muelleri (Table 3). Similarly, RBB searches between A. thaliana CDSs and Z. noltii ESTs resulted in 8793 RBB relationships, and the only transcript corresponding to proteins involved in ethylene biosynthesis and signalling encodes a putative orthologue of EIL1.

Multiple sequence alignment between Z. marina EIL1 and orthologues of EIL1 and EIN3 found in OGCs suggests at least partial conservation of function

Multiple sequence alignment between Z. marina EIL1, orthologues of EIL1 and EIN3 found in OGCs, and EIN3 orthologues found in A. andraeanum and Z. aethiopica transriptomes was performed. Both EIL1 and EIN3 are homologous proteins ~600 amino acids in length, containing three domains: a DNA-binding domain (DBD) at the N-terminus, a proline-rich domain (PRD) in the central region, and a protein degradation domain (PDD) at the C-terminus (Cho et al., 2012). The DBD, which spans five α-helices (Yamasaki et al., 2005), appears to be well conserved in Z. marina (Supplementary Fig. S3 at JXB online). The two key DNA binding residues Pro216 and Lys245 (Cho et al., 2012) are also conserved. However, both Z. marina and Z. noltii EIL1 orthologues share an N-terminal deletion of ~130 residues.

Discussion

Whole-genome shotgun sequence data for the seagrass species Z. muelleri was compared with gene sets of four sequenced terrestrial species, A. thaliana, O. sativa, P. dactylifera, and M. acuminata, and one aquatic species S. polyrhiza. The species list contains a model dicot and a collection of four monocots. The genome-wide analysis of Z. muelleri gene conservation and loss aimed to identify genes which are common between these species as well as genes which are lost in Z. muelleri. The loss of ethylene biosynthesis and signalling genes in Z. muelleri was supported by the analysis of the Z. marina genome and transcriptome.

The genes which were identified as conserved between the five species and Z. muelleri are involved in a range of highly conserved cellular processes. The processes with the most confident GO term enrichment values include glucose metabolism, protein catabolism, and translation, and many of the genes that are involved in carbohydrate and protein metabolism. It is clear that many of the basic cellular processes are conserved between Z. muelleri and other plant species. The conservation of genes involved in response to salt stress also suggests that Z. muelleri may use some of the same pathways to combat salinity in the marine environment as other plant species.

The analysis of genes lost suggests major disruption of ethylene synthesis and signalling in Z. muelleri and Z. marina. Ethylene is a simple two-carbon gaseous compound which is a potent modulator of plant growth and development (Ecker, 1995). This plant hormone is involved in many aspects of the plant life cycle, including seed germination, root hair development, root nodulation in legumes, flower senescence, abscission, and fruit ripening (Johnson and Ecker, 1998; Wang et al., 2002). The production of ethylene is known to be controlled by internal signals during development and in response to external abiotic/biotic stimuli (Wang et al., 2002) including: wounding, drought, ozone, flooding, pathogen and insect attack, and salt stress (Cao et al., 2007, 2008; Wang et al., 2007; Yoo et al., 2009).

Ethylene is synthesized from S-adenosylmethionine (SAM). SAM is converted to ACC by ACS, and ACC is then oxidized by ACO to form ethylene (Wang et al., 2002). Ethylene is perceived by a range of receptors, including the five receptors found in A. thaliana: ETR1, ETR2, EIN4, ERS1, and ERS2. The ethylene signal is further transduced by a Raf-like kinase CTR1 and finally EIN2, which can activate a family of EIN3 transcription factors (Wang et al., 2002). The present analysis suggests that ethylene metabolism and signalling functions are missing in Z. muelleri, and that there is a complete loss of ethylene biosynthesis and signalling in this species (Fig. 2).

Fig. 2.

Ethylene biosynthesis and signalling. Ethylene is synthesized from S-adenosylmethionine (SAM) by ACC (1-aminocyclopropane-1-carboxylic acid) synthase (ACS) and ACC oxidase (ACO) to form ethylene. Ethylene is perceived by a range of receptors and the signal is further transduced by kinase CTR1 and EIN2. A family of EIN3 transcription factors are activated by ethylene. Proteins marked with a cross are most probably lost in Z. muelleri. Proteins marked with a tick are most probably present.

The loss of ethylene synthesis and signalling in Zosteraceae is further supported by analysis of Z. marina and Z. noltii EST data available in the Dr Zompo database (Wissler et al., 2009). Genes involved in ethylene synthesis and signalling are ubiquitously expressed in many plant species (Wang et al., 2002). Therefore, if they were present in the Z. marina genome, at least some of them may be expected to be expressed and present in the EST database.

In terrestrial plants, ethylene has been shown to be implicated in response to flooding stress (Voesenek and Sasidharan, 2013), such as triggering programmed cell death that leads to the formation of aerenchyma, a tissue which assists in conducting oxygen to the submerged root system of the plant (Voesenek et al., 2006). Aerenchyma in seagrasses are present constitutively in leaves and roots as an adaptation to a submerged life, which in turn might supersede any requirement for ethylene signalling for aerenchyma formation. Life on the sea floor is also associated with qualitative and quantitative changes in available light. In many plants, low-light conditions normally cause etiolation, shade avoidance, and leaf senescence, responses that involve ethylene (Franklin, 2008). Many seagrasses are adapted to low-light conditions (Ralph et al., 2007), again possibly obviating the need for ethylene in inducing shade avoidance responses. Ethylene is also constantly emitted by plants in a range of physiological processes such as during the circadian rhythm of the plant, during flower development, or upon wounding stress or in response to environmental stressors (Harren and Cristescu, 2013). In turn, ectopic ethylene induces ethylene signalling pathways within the plant (Barry et al., 2005; Liu and Wen, 2012). Hence, a plausible explanation as to why ethylene could be lost in plants under submerged marine conditions might be the drastically reduced efficacy of a gaseous hormone such as ethylene in an aquatic environment. Analysis of the genome and transcriptomes of four diverse species belonging to the order Alismatales suggests the presence of ethylene synthesis and signalling in all four species. It was postulated that S. oligorrhiza, a close relative of S. polyrhiza, does not produce ethylene via the ACC intermediate but via an alternative pathway (Mattoo et al., 1992). However, in this study, genes which appear to be orthologues of ACS and ACO were identified in the S. polyrhiza genome. Furthermore, S. polyrhiza possesses many components of the ethylene signalling pathway which appear to be lost in Z. muelleri and Z. marina. The presence of ethylene signalling in S. polyrhiza, which grows forming mats on the water surface, and its absence in the two seagrasses, Z. muelleri and Z. marina, which grow fully submerged, suggests that the loss of ethylene signalling may be associated with a fully submerged environment. However, additional analysis including studies of more aquatic species is necessary to test this hypothesis.

Analysis of both genomic and transcriptomic data points to the conservation of some of the downstream targets of the ethylene signalling cascade in seagrasses. Orthologues of EIN3/EIL transcription factors appear to be present in Z. muelleri, Z. marina, and Z. noltii. Interestingly, the genes encoding EIL1 putative orthologues in Zostera (the only detected transcript that could encode a protein involved in ethylene biosynthesis and signalling) have a large deletion in the N-terminal region (~130 amino acids). It could be hypothesized that the loss of the ethylene signalling network has relaxed the selective pressure of all genes involved in this pathway (considering that they are not involved in other functions). This could have offered the opportunity to recruit some of these genes in new functions. Such genes could have been under transient positive selection favouring amino acid changes or deletions (such as the loss of the N-terminal part of EIL1). Further analysis of these transcription factors, their sequences, regulators, and downstream targets may shed more light on the effects of loss of ethylene signalling.

Supplementary data

Supplementary data are available at JXB online.

Figure S1. K-mer distribution for the Z. muelleri genome.

Figure S2. K-mer distribution for the Z. marina genome.

Figure S3. EIL1 multiple sequence alignment.

Table S1. Complete list of genes found in OGCs.

Table S2. Complete list of genes conserved with corresponding GO annotation.

Table S3. Complete list of genes lost with corresponding GO annotation.

Table S4. Complete list of significantly enriched GO terms associated with genes conserved in Z. muelleri.

Supplementary Data

supp_66_5_1489__index.html

supp_eru510_SupplementaryMaterial1_good2_updated.pdf

supp_eru510_jexbot134312_file002.xlsx

Acknowledgements

We thank Dr Mark Brown (UNSW) for generous in-kind provision of DNA sequencing capacity at the Ramaciotti Centre as well as Drs Katherina Petrou, Marlene Zbinden (both UTS), and Michael Rasheed (JCU Cairns) and his team for the collection of seagrass in Gladstone Harbour and support. AWDL and MS acknowledge research funding from C3 as well as UTS Strategic Support for MS. The authors would like to acknowledge funding support from the Australian Research Council (Projects LP0882095, LP0883462, LP110100200, and DP0985953). Support is also acknowledged from the Queensland Cyber Infrastructure Foundation (QCIF) and the Australian Partnership for Advanced Computing (APAC).

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