Introduction

Transposable elements (TEs) have been found in virtually all eukaryotes and 80% of prokaryotes sequenced so far (Hua-Van et al., 2011). They are usually considered as selfish DNA with the capacity to proliferate inside the genome (Doolittle and Sapienza, 1980; Orgel and Crick, 1980). Most organisms have no efficient way of eliminating these potentially deleterious genetic elements from their genomes, although different mechanisms that silence the activity of TEs are being increasingly revealed (Galagan et al., 2003; Slotkin and Martienssen, 2007; Siomi et al., 2011; Watanabe et al., 2013). In spite of their parasitic nature, TEs also provide beneficial effects on the evolution of their hosts (Dunlap et al., 2006; Slotkin and Martienssen, 2007; Casacuberta and Gonzalez, 2013; Gifford et al., 2013; Riordan and Dupuy, 2013; Bennetzen and Wang, 2014). Characterizing the distribution and classification of TEs is thus important to better evaluate their role in shaping the evolution, structure and function of genomes across the whole tree of life.

TEs are traditionally split into two different classes: Class I TEs (retrotransposons) and Class II TEs (DNA transposons), differing in their mode of transposition (Wicker et al., 2007). Retrotransposons transpose through reverse-transcribed RNA intermediates. Retroviruses and endogenous retroviruses belong to Class I (Wicker et al., 2007). DNA transposons transpose within genomes without RNA intermediates. Retrotransposons have a ‘copy-and-paste’ transposition mechanism, whereas a majority of DNA transposons use a ‘cut-and-paste’ mechanism, although several Class II elements are known as ‘copy-and-paste’ DNA transposons. Retrotransposons are found in eukaryotes only, whereas DNA transposons are found in both eukaryotes and prokaryotes (Wicker et al., 2007).

Retrotransposons are often responsible for the marked expansion of genomes especially in higher eukaryotes (for example, 42% of the human genome (Lander et al., 2001), 76% in maize (Schnable et al., 2009), 67% in the chromosome 3B of bread wheat (Choulet et al., 2014); see (Arkhipova et al., 2003; Rho et al., 2010; Piednoel et al., 2013) for invertebrate genomes). A significantly large number of retrotransposons are observed in the red seaweed Chondrus crispus where they represent 55% of the genome (Collen et al., 2013). Retrotransposons are also found in unicellular eukaryotes, such as diatoms (Armbrust et al., 2004; Bowler et al., 2008), green algae (Derelle et al., 2006; Blanc et al., 2010, 2012) and choanoflagellates (Carr et al., 2008) as well as in other aquatic or terrestrial protists (Bringaud et al., 2007; Khan et al., 2007; Lorenzi et al., 2008; Bulman et al., 2011). Their frequency in the genome can vary between related species. For example, retrotransposons occupy 5.8% of the genome of the pennate diatom Phaeodactylum tricornutum, but only 1.1% of the genome of the centric diatom Thalassiosira pseudonana (Bowler et al., 2008). The genomes of Ostreococcus species harbor retrotransposons (Derelle et al., 2006; Palenik et al., 2007), whereas no TEs have been identified in the closely related Bathycoccus prasinos (Moreau et al., 2012).

Prokaryotic genomes also encode reverse transcriptases (RTs), although these RTs are less characterized than eukaryotic RTs. About 25% of sequenced bacterial genomes encode RTs (Simon and Zimmerly, 2008), which could be classified into 17 groups (Toro and Nisa-Martinez, 2014). The most prevalent groups of prokaryotic RTs are encoded in genetic elements such as group II introns, retrons and diversity-generating retroelements (DGRs).

In the current work, we explored recently available marine metagenomes and identified numerous genes encoding RT. We generated metagenomic data from samples collected at three sites in the Mediterranean Sea during the Tara Oceans project (Karsenti et al., 2011; Hingamp et al., 2013; Bork et al., 2015). The Mediterranean Sea is an evaporative, oligotrophic to ultra-oligotrophic basin (mostly due to phosphate deficiency (Berland et al., 1980; Krom et al., 1991; Antoine et al., 1995)). Despite its small size, it has a relatively complex pattern of plankton trophic regimes (D’Ortenzio and Ribera d’Alcala, 2009). Overall, a decreasing west–east gradient characterizes the surface chlorophyll pattern, further modulated by the presence of river runoffs (especially in the Adriatic Sea) and by the circulation, highly constrained by the orography of the surrounding regions (D’Ortenzio and Ribera d’Alcala, 2009). The planktonic community is generally dominated by small autotrophs, microheterotrophs and egg-carrying copepod species (see review by (Siokou-Frangou et al., 2010)). The metagenomic data were derived from size-fractionated samples and corresponded to a wide organism size range (0.2 μm–2 mm), thus providing a comprehensive access to the genomic diversity of prokaryotes, eukaryotes and viruses residing in these water masses. Metatranscriptomic sequence data were generated for all samples (above 0.8 μm). In this study, we investigated the abundance, classification and taxonomic origin of RT sequences detected in these samples, as well as their transcriptional status.

Materials and methods

Samples and sequence data

Samples were collected at three Tara Oceans stations: (1) Station (St) TARA_007 (37°1′16′′N, 1°56′58′′E; 23 September 2009), situated in the Algerian basin close to Algiers, (2) St TARA_023 (39°50′9′′N, 17°24′17′′E; 16 November 2009) in the Adriatic Sea close to Dubrovnik and (3) St TARA_030 (33°55′5′′N, 32°53′40′′E; 15 December 2009) in the East Levantine basin south of Cyprus. At these three stations, size-fractionated plankton samples (five fractions: pico-plankton (0.2–1.6 μm), piconano-plankton (0.8–5 μm), nano-plankton (5–20 μm), micro-plankton (20–180 μm) and meso-plankton (180–2000 μm)) were collected at two depths (surface (SUR) and deep chlorophyll maximum (DCM)). Sampling protocols and environmental data associated with each sample are available in (Tara Oceans Consortium, Coordinators; Tara Oceans Expedition, Participants, 2014; Chaffron et al., 2014a,2014b; Pesant et al., 2015). Methods for DNA and RNA extraction, complementary DNA synthesis and sequencing library preparation are provided in Supplementary Methods and Results.

Twenty-nine DNA samples (five size fractions) were sequenced using Illumina HiSeq technology (one sample (TARA_030/DCM/180–2000 μm) did not yield enough DNA to be processed). For two samples (TARA_007/SUR and DCM/5–20 μm), DNA yield was low, therefore, DNA was whole genome amplified before sequencing. Sequence data from these two samples were used only in part of the present study (they were not included in quantitative analyses). The metagenome samples yielded 4714 million reads. For assembly, 3160 million reads were used to generate 4.2 Gbp of assembled sequences in five million metagenomic contigs (Supplementary Table S1), with the use of either SOAPdenovo v1.4/v1.5 (Luo et al., 2012) or Velvet v1.0.15 (Zerbino and Birney, 2008). Only contigs ⩾500 bp were included in these assemblies. Open reading frames (ORFs) in the metagenomic contigs were identified by a combination of de novo gene-prediction methods (that is, metagene (Noguchi et al., 2006) and SNAP (Korf, 2004)) (Supplementary Table S2).

Twenty-three RNA samples (four size fractions above 0.8 μm) were sequenced using Illumina HiSeq technology (one sample TARA_030/DCM/180–2000 μm did not yield enough RNA to be processed). A total of 1 776 979 581 reads from polyA mRNA samples yielded 8.0 Gbp of assembled sequences in 66 million metatranscriptomic contigs (Supplementary Table S1), with the use of either Velvet v1.2.07 or Oases v0.2.8 (Schulz et al., 2012). Only contigs ⩾500 bp were included in these assemblies. We identified ORFs (⩾30 amino acids (aa)) in metatranscriptomic contigs using EMBOSS/GETORF (Rice et al., 2000).

More detailed descriptions of samples, sequence data and assembly processes are provided elsewhere (Jaillon et al., submitted). All environmental sequences presented in this study are available at the European Nucleotide Archive (http://www.ebi.ac.uk/ena) under the project identification number PRJEB402. Additional data are available from ftp://ftp.genome.jp/pub/db/community/tara/RT/.

Domain identification in metagenomes

Metagenomic ORFs were first masked using the SEG low-complexity sequence filter (Wootton, 1994). Using position-specific iterated basic local alignment search tool (Altschul et al., 1990, 1997), we performed sequence similarity searches from all position-specific score matrices in the Conserved Domain Database (CDD) of the National Center for Biotechnology Information (NCBI) (Marchler-Bauer et al., 2013) to the translated and filtered ORFs. Then the best CDD profile (E-value<10−5) was assigned to each ORF. To compile a list of retrotransposon- and retrovirus-related sequence domains recorded in CDD, the definition lines of CDD profiles were searched for relevant keywords (for example, ‘retrotransposon’, ‘retrovirus’, ‘retroviral’, ‘RNase’, ‘integrase’). We manually examined the candidates and produced the final list containing 56 CDD profiles (Supplementary Table S3). These CDD profiles were used to identify retrotransposon- and retrovirus-related sequence domains in the metagenomic ORFs. It should be noted that, for the analysis of RT-containing ORFs, we used hidden Markov models (HMMs) instead of position-specific iterated basic local alignment search tool (see below).

Classification of the RTs

We built seven HMM profiles (HMMER/hmmbuild (Finn et al., 2011)) based on multiple sequence alignments of diverse groups of RT sequences retrieved from the Gypsy database (http://gydb.org; (Llorens et al., 2011)) as well as from (Gladyshev and Arkhipova, 2011). Searches with these seven profiles plus 51 HMM pre-compiled profiles from Gypsy database were carried out against the metagenomic/metatranscriptomic ORFs (using hmmsearch, E-value<10−5). Identified metagenomic sequences were compared by BLASTP (E-value<10−5) with the reference RT sequences (Gypsy database and (Gladyshev and Arkhipova, 2011)) and the best BLAST hit was used for RT classification (for example, LINEs, Gypsy, Copia and so on). The longest RT-like ORF from the Tara Oceans metagenomes (AHX23DCM1GGMM11BCE.ASY1CTG52) and its close homologs (cryptophyte Dualen-1_GCr and Dualen-5_CCu) that we identified in the Marine Microbial Eukaryote Transcriptome Sequencing Project (http://camera.crbs.ucsd.edu/mmetsp/, (Keeling et al., 2014), as of May 2014) were submitted to Repbase (http://www.girinst.org/repbase/; (Jurka et al., 2005)). Relative gene abundances in the metagenomic and metatranscriptomic data sets were estimated by their ‘average coverage’ defined by the cumulative sizes of reads mapped on the contigs divided by contig sizes (see Supplementary Methods and Results for detail).

Phylogenetic analysis

We aligned long metagenomic RT sequences (from 497 to 2543 aa) on the previously described reference HMM profiles using HMMER/hmmalign. We discarded alignment columns with >80% of gaps, and the resulting multiple sequence alignments used for phylogenetic analyses contained 462 positions (214 sequences) for the RT groups that included LINEs and Gypsy (Figure 1a), 257 positions (171 sequences) for Copia RTs (Figure 1b) and 315 positions (132 sequences) for BEL RTs (Figure 1c). The phylogenetic reconstructions were performed using the Phylogeny.fr server (Dereeper et al., 2008) with PhyML 3.0 (Guindon et al., 2010). The best fitting substitution model and rate variation parameters were selected using ProtTest 3 (Darriba et al., 2011) according to the smallest Akaike Information Criterion: VT+I+G+F for the RT sequence set that include LINEs and Gypsy (Figure 1a), LG+I+G+F for Copia RTs (Figure 1b), RtREV+I+G+F for BEL RTs (Figure 1c). Bootstrap values were calculated with 100 bootstrap replicates. The resulting phylogenetic trees were edited using MEGA6 (Tamura et al., 2013).

Figure 1
figure 1

ML-trees of known and environmental RT sequences. (a) ML-tree of known RT sequences with 124 RT-like sequences identified in the metagenomic data. Black dots indicate RT-like sequences from the metagenomic data. EC1 stands for Environmental Clade 1, LEC for LINE Environmental Clades (LEC1-LEC3) and GEC for Gypsy Environmental Clades (GEC1, GEC2). LTR retrotransposons: Gypsy, Copia, BEL and retrovirus. Non-LTR retrotransposons: LINE (APE-type and REL-type). Distributions know reference RTs in prokaryotes (P) and eukaryotes (E) are as follows: LINE (E), Gypsy (E), Caulimovirus (E), Retrovirus (E), PLE (E), DIRS (E), RTL (E), RVT (E/P), TERT (E), group II intron (E/P), DGR (P), retron (P), retroplasmid (E), Hepadnavirus (E). ANB7SUR0CCII11BDE.ASY2CTG1927.ANO1.snap_1 is marked by a purple arrow, and AHX7DCM1GGMM11BCE.ASY1CTG361.ANO1.mga_1 by a green arrow. The sequence marked by an orange arrow is AHX23DCM1GGMM11BCE.ASY1CTG52.ANO1.mga_1. (b) ML-tree of known RT sequences with 100 Copia RT-like sequences identified in the metagenomic data. Black dots indicate RT-like sequences from the metagenomic data. CEC stands for Copia Environmental Clades (CEC1-CEC4). Copia belong to LTR retrotransposons. Taxonomic distributions of known reference Copia are as follows: CoDi (diatoms), GalEA (animals and red algae), PyRE1G1 (red algae), Hydra (Cnidarian), Tork (land plant), Mtanga and Tricopia (insecta), copia (insecta), pCreta (fungi), 1731 (diptera), Osser (plant), Retrofit (plant), Sire (plant), Oryco (plant), Ty (fungi). (c) ML-tree of known RT sequences with 100 longest BEL RT-like sequences identified in the metagenomic data. Black dots indicate RT-like sequences from the metagenomic data. BEC stands for BEL Environmental Clades (BEC1-BEC5). BEL belong to LTR retrotransposons. Tas (nematoda and cnidaria), Bel (insecta), Pao (insecta and vertebrata), Sinbad (invertebrata), Suzu (echinodermata and vertebrata). New environmental clades satisfying the three criteria defined in Materials and Methods are marked by a red-shaded area.

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Criteria for environmental clades

To delineate ‘environmental clades’ that were only comprised of environmental sequences, we used the following three criteria: (i) the clade contained at least three environmental sequences, (ii) there was no known RT sequences (that is, RT sequences of known taxonomic origin from the public databases such as NCBI non-redundant database (NR)) within the clade and (iii) the bootstrap branch support for the clade was ⩾80%. Prior to applying the second criterion, we first identified candidates for environmental clades fulfilling criteria (i) and (iii). We then performed BLASTP searches from the RT domains of the environmental sequences of the candidate clades against three databases: NR, our RT reference database (see above) and themselves. Most of the members of the initial candidate clades had BLASTP scores with other members of the same environmental clade higher than with any other known sequences, but several environmental sequences had best hits to known RT sequences. As the latter case may not satisfy the criterion (ii), these known sequences were added in our phylogenetic analyses. Only clades that satisfied the three criteria in the final phylogenetic trees were annotated as ‘environmental clades’.

Taxonomic annotation

Metagenomic and metatranscriptomic RT-like ORFs were compared using BLASTP to reference databases (UniProt (release of May 2014) and Marine Microbial Eukaryote Transcriptome Sequencing Project). When the best hit reference sequence showed ⩾60% amino-acid sequence identity against a query sequence (E-value<10−8), the taxonomic annotation of the reference sequence was transferred to the query sequence. This percent identity threshold was determined according to the distribution of sequence similarities among known Copia and Gypsy RT sequences to maximize the accuracy of taxonomic annotation at the second level of the hierarchy of the NCBI taxonomic classification such as Stramenopile and Viridiplantae (Supplementary Figure S1).

Detrended correspondence analysis

To estimate the biotic (that is, organismal) and abiotic (that is, physicochemical) variables explaining patterns of RT distribution across samples, we used a detrended correspondence analysis (DCA) and the envfit function of the R package vegan (Oksanen et al., 2007). Abiotic variables were available via PANGAEA (Tara Oceans Consortium, Coordinators; Tara Oceans Expedition, Participants, 2014; Chaffron et al., 2014a,2014b) through the sample codes in Supplementary Table S4. Biotic composition of samples were derived from the 18S-V9 rDNA barcodes described in de Vargas et al. (2015), Supplementary Table S5). Vector fitting (envfit) with 1000 permutations was used to estimate the significance of correlations between the ecological variables and the ordination by DCA (P-values<0.05).

Results

Characteristics of the sampling sites

The Mediterranean Sea shows a relatively complex pattern of plankton trophic regimes (D’Ortenzio and Ribera d’Alcala, 2009). Although not being representative of all the possible trophic regimes, the three sampling stations are related to quite different biomes. The first station, TARA_007, sampled the incoming (modified) Atlantic waters along the Algerian coast. These relatively fresh waters are separated from the deep layers by an intense pycnocline that prevents deep convection in winter, thus inhibiting the renewal of nutrients from the deep reservoir. Here, typical early fall conditions characterized the water column, with a shallow mixed layer (18 m) and mild temperatures (23.8 °C at surface). A shallow DCM (44 m) had a chlorophyll value of 0.5 mg Chl m−3, that is, five times higher than the surface values. Despite the late fall sampling, TARA_023, in the more productive central Adriatic Sea, shows a very shallow mixed layer (9 m), possibly owing to the recent arrival of coastal waters from Croatia at the very surface. It has a surface temperature of 17.6 °C and a relatively deep DCM (54 m). Similarly sampled in late fall, TARA_030, at the center of the Cyprus gyre in the ultra-oligotrophic Eastern Basin, was characterized by a warmer surface temperature (20.4 °C) and a shallow mixed layer (41 m), overlying a DCM at 77 m. Both SUR and DCM chlorophylls were very low, as usual in this region (0.04 and 0.14 mg Chl m−3, respectively).

RTs dominate the metagenomics gene content in size fractions above 5 μm

ORFs from 29 metagenomic assemblies were searched against NCBI/CDD. Out of 7 598 631 ORFs, 43% (3 236 369 ORFs) showed significant sequence similarity to the domain database. Of these, 18 780 ORFs were related to retrotransposons or retroviruses (Supplementary Table S3). Some of these CDD domains were ranked at the highest abundance levels. For instance, 9 of the 15 CDD profiles that recruited the largest numbers of metagenomic ORFs from the 180–2000 μm size fraction corresponded to retrotransposon/retrovirus-related domains (Table 1). The protein domains representing the RT of non-long terminal repeat (LTR) retrotransposons (cd01650) was ranked at the top (that is, 2782 ORFs). Other RT domains (cd01647, cd01644, pfam07727 and pfam00078) recruited from 222 to 1207 ORFs. Retrotransposon/retrovirus-related integrases (pfam00665), RNase H (pfam05380 and cd06222), and apurinic-like endonucleases (pfam03372) showed a large number of hits (from 186 to 988 ORFs). Other highly ranked domains unrelated to retrotransposons included ankyrin repeats, C2H2-type Zn-finger proteins, trypsin-like serine proteases, PIF1 helicases, homeobox transcription factor SIP1 and nidogen and related basement membrane proteins (Table 1). These are known to be encoded in multicopy genes. High abundances of retrotransposon/retrovirus-related domains were clearly observed in the three largest size fractions (that is, 180–2000 μm, 20–180 μm and 5–20 μm fractions) (Supplementary Figure S2 and Supplementary Table S6), with RTs of non-LTR retrotransposons (cd01650) and LTR retrotransposons including retroviruses (cd01647) being the most highly represented domains.

Table 1 Highly represented CDD profiles in the ORF set from the 180–2000 μm size fraction metagenomes
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To classify and characterize the diversity of these retrotransposon/retrovirus-related sequence domains, we specifically focused on RT domains for the subsequent analyses. We identified 11 419 RT-like ORFs in the 29 metagenomes. The size of the RT-like metagenomic ORFs varied from 30 to 2543 aa (200 aa on average), whereas the length of typical known RTs is between 150 aa and 250 aa. The detected RT-like sequences were thus often incomplete, but many of them were long enough for phylogenetic analysis.

Phylogenetic trees indicated that the metagenomics-derived RTs were widely distributed over diverse RT groups (Figure 1 and Supplementary Figure S3). The tree in Figure 1a classified all RT-like sequences except Copia (Figure 1b) and BEL (Figure 1c) RTs. A majority of the metagenomic RTs were grouped with RTs from eukaryotes, with a high representation within the LINE and Gypsy groups. We identified three LINE clades that were represented only by environmental sequences (LINE Environmental Clades: LEC1 to 3), two Gypsy Environmental Clades (GEC1 and 2) and another environmental clade (EC1). Several sequences fell into groups of prokaryotic retroelements. For instance, one ORF (ANB7SUR0CCII11BDE.ASY2CTG1927.ANO1.snap_1, 0.2–1.6 μm fraction) marked in Figure 1a encoding an RT and DNA polymerase A resembles the Group D prokaryotic retroelements in sequence and structure (Kojima and Kanehisa, 2008). Another sequence (AHX7DCM1GGMM11BCE.ASY1CTG361.ANO1.mga_1, 0.8–5 μm fraction) was related to Abi (abortive bacteriophage infection) genes (Toro and Nisa-Martinez, 2014), without further precision about its origin. The metagenomic Copia RT sequences were distributed across the GalEA (marine arthropods) and three known groups of diatom Copia elements (CoDi-I (CoDi2.4, 3.1, 4.1, 4.3, 4.4, 4.5 and 7.1), CoDi-II (CoDi5.1 to 5.6) and CoDi6 (CoDi6.1 to 6.7) (Maumus et al., 2009)) (Figure 1b). Several RT-like sequences did not show close relatives in the reference Copia RT sequence set (Copia Environmental Clades: CEC1–4). Many of the metagenomic BEL RT sequences were grouped within Pao, Sinbad, Suzu, Tas and Bel retroelements (Figure 1c). Our phylogenetic analysis revealed the existence of five deeply branched and clearly delineated environmental clades for the BEL group (BEL Environmental Clade: BEC1–5).

Relative abundances of metagenomic RT sequences (based on average contig coverage) revealed that they were more abundant in the meso-, micro- and nano-plankton samples (5–2000 μm) than in the pico- and piconano-plankton samples (0.2–5 μm) (Figure 2). For three samples, the relative abundance of RT-like ORFs alone exceeded 8% of the abundance of all ORFs in the respective samples (that is, St TARA_023/DCM/180–2000 μm, 13.47%; St TARA_030/SUR/180–2000 μm, 9.02%; St TARA_030/DCM/5–20 μm, 8.73%).

Figure 2
figure 2

Relative RT gene abundance and classification of the RTs identified in the metagenomic data. In the station names, S and D denote, respectively, SUR and DCM depths.

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Most metagenomics RT sequences originate from retrotransposons

We then classified metagenomic RT sequences (11 419 ORFs) (Figure 2). Consistent with our phylogenetic analyses for selected sequences, RTs from autonomous retrotransposons such as Gypsy, BEL and LINE-AP/LINE-REL were found to dominate in meso-, micro- and nano-plankton size fractions.

Taxonomic annotation suggested that RT-like sequences in large-size fractions (>5 μm) mainly originated from eukaryotic genomes (Supplementary Figure S4 and Supplementary Figure S5). The most represented groups were metazoans, followed by stramenopiles. In the piconano-plankton size fraction (0.8–5 μm), the taxonomic coverage was wider, with the detection of over 10 RT-like sequences in haptophytes, stramenopiles and cryptophytes than in the larger size fractions. Copia RTs were enriched in this piconano-plankton size fraction relative to other larger size fractions.

The longest ORF, AHX23DCM1GGMM11BCE.ASY1CTG52.ANO1.mga_1, (2543 aa; St TARA_023/DCM/0.8–5 μm; Figure 1a) encompassed an apurinic-like endonuclease domain in addition to an RT domain and was found related to the cryptophyte LINEs, such as Dualen-1_GCr and Dualen-5_CCu, which we reconstructed from transcriptome sequences derived from Marine Microbial Eukaryote Transcriptome Sequencing Project (Keeling et al., 2014) (Supplementary Figure S6). Dualen-1_GCr from Geminigera cryophila encodes a 5287 aa protein including SET methyltransferase, C48 peptidase, apurinic-like endonuclease, reverse transcriptase, ribonuclease H, CCHC zinc finger and restriction-like endonuclease domains. This domain configuration is unique to the cryptophyte Dualen among other previously characterized Dualen elements (Kojima and Fujiwara, 2005). The main characteristic of the Dualen LINEs (also known as RandI (Kapitonov et al., 2009)) is to encode both apurinic-like and restriction-like endonucleases (dual-endonucleases). The longest ORF with an RT-like domain thus likely represents a group of Dualen elements distributed among cryptophytes.

The relative abundance of bacterial-like environmental RT sequences increased with decreasing sample fraction size, with a high abundance of RTs of putative proteobacterial origin in line with the known overrepresentation of this taxon in the ocean. RT-like ORFs of predicted bacterial origin corresponded mainly to group II introns and retrons (Supplementary Figure S5).

Bacterial and viral RT transcripts were detected in all size fractions

Metatranscriptomic data were screened for the presence of transcriptionally active RT-like ORFs using HMM profiles. The search resulted in the identification of 4 258 RT-like ORFs. Relative abundances of transcriptionally active RTs among all transcripts varied from 0.002% (St TARA_007/DCM/180–2000 μm) to 0.15% (St TARA_007/DCM/20–180 μm) (Figure 3). Taxonomic annotation of these transcribed RTs indicated that many originated from eukaryotes (Supplementary Figure S7). However, metatranscriptomic RTs showed a distinct classification pattern to that observed in metagenomes. First, the relative abundance of LINE and BEL RTs decreased in the metatranscriptomes relative to the metagenomes, whereas the opposite was true for Gypsy (detected in 22 samples) and Copia (detected in all samples) RTs (Figure 3). Second, the relative abundances of bacterial RTs increased in the metatranscriptomic data for all fraction sizes. Group II intron RTs, which were mainly detected in pico- and piconano-fractions of metagenomes, were detected in 20 metatranscriptomes, including those from larger size fractions. Retron-type RT transcripts were detected in the metatranscriptomes from all the analyzed size fractions from Station 7. RT sequences similar to DGRs were identified in samples from Station 30. Third, the frequency of retrovirus RTs was much higher than in the metagenomes (Figure 3 and Supplementary Figure S7). These viral RTs were observed in various size fractions (22 samples). In the St TARA_023/SUR/20–180 μm sample, retroviral RTs were the most dominant type of transcribed RTs (Figure 3). Putative taxonomies were assigned for 2050 out of 4258 metatranscriptomic RT-like sequences using BLAST. Gypsy and Copia are often best matching to Tracheophyta (923 of 1092 taxonomically assigned Gypsy sequences and 388 of 463 taxonomically assigned Copia sequences), retroviruses to Euteleostomi and Retroviridae (157 and 188 out of 367, respectively), Caulimovirus to Magnoliophyta (22 of 30), Group II intron to Gammaproteobacteria (28 of 66), DGR to Achromobacter (10 of 10) and Retron to Afipia (seven of seven).

Figure 3
figure 3

Relative RT gene abundance and classification of transcriptionally active RT-like sequences identified in the metatranscriptomic data.

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Correlation with environmental variables

The observed RT transcription pattern (Figure 4) was analyzed with DCA to identify its possible association with environmental variables (that is, 22 abiotic parameters and 107 taxon composition vectors for eukaryotes). Overall, samples were clustered according to their sampling sites (rather than by size fractions) on the ordination plane. Vector fitting indicated that PO4 concentration most significantly correlated with the RT transcription pattern (r2=0.7349, P-value=0.001; Figure 4 and Supplementary Table S7). Samples from St TARA_007 were placed in the region on the ordination plane where relatively high PO4 concentrations were measured. The LINEs, BEL and retrons were abundant in these samples. When the same method was used with the metagenomic RT gene abundance, samples from the same size fraction tended to cluster together, with the 0.8–5 μm size fraction samples being outliers (Supplementary Figure S8). PO4 (r2=0.3080, P-value=0.042) and NO2+NO3 (r2=0.3604, P-value=0.035) concentrations were found to be the strongest abiotic variables explaining this RT gene distribution (Supplementary Figure S8 and Supplementary Table S8). In addition, 27 taxon abundances were found to be important factors explaining the distribution, with the 0.8–5 μm size fraction samples being placed in the same direction on the ordination plane (Supplementary Table S8). The group II intron RT class strongly correlated with these biotic variables. In contrast, retron RT abundance showed a correlation with NO2+NO3 concentration. Overall, these analyses identified nutrients (PO4 and NO3) as the main environmental factors explaining the distributions of RT genes/transcripts in these Mediterranean Sea metagenomes and metatranscriptomes.

Figure 4
figure 4

Detrended Correspondence Analysis (DCA) of metatranscriptomic RT gene abundance. DCA ordinations of 21 samples are shown for metatranscriptomic RT gene abundance, with significant (P⩽0.05) environmental vectors fitted using envfit (Oksanen et al., 2007). Arrows indicate the direction of the (increasing) environmental gradient, and their lengths are proportional to their correlations with the ordination. X stands for samples from size fraction 180–2000 μm, L for 20–180 μm, M for 5–20 μm and S for 0.8–5 μm. Samples from St TARA_007 is colored in pink, St TARA_023 in blue, and St TARA_030 in green.

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Discussion

We investigated the abundance, classification and transcriptional status of RTs using a newly generated set of marine metagenomes and metatranscriptomes. We showed a remarkably high representation of metagenomic RTs for the three largest organismal size fractions enriched in eukaryotic genomes. They were more abundant than any other known sequence domains defined in NCBI/CDD. We showed that most of these metagenomic RTs originated from retrotransposons and comprised diverse RT lineages.

The identified metagenomic RTs covered a wide spectrum of eukaryotic lineages. Our conservative taxonomic assignments suggest their presence in metazoans, stramenopiles, cryptophytes, haptophytes, Viridiplantae, Alveolata and Amoebozoa, with the most numerous assignments being to metazoans (Supplementary Figure S5). This wide phylogenetic coverage is consistent with the notion that retrotransposons are ubiquitous in eukaryotic genomes and have participated in their evolution since the radiation of the major eukaryotic lineages. Classification of the metagenomic RT sequences showed that the majority of them belonged to one of the four major known groups of retrotransposons, namely LINEs (non-LTR retroelements), Gypsy (LTR retroelements), BEL (LTR retroelements) and Copia (LTR retrotransposons). Our phylogenetic analyses revealed many previously unrecognized clades across these groups of retrotransposons (Figure 1). Furthermore, the longest RT-like ORF was identified as a cryptophyte Dualen element. To our knowledge, there is no previous report on the presence of Dualen in cryptophytes. In a previous study, Maumus et al., (2009) examined the abundance of RTs in metagenomic data for size fractions under 20 μm (that is, 0.1–0.8 μm, 0.22–0.8 μm, 0.8–3.0 μm, 3.0–20.0 μm). They found a positive correlation between the frequency of RTs and the size of organisms. Our results, extending the coverage of organism sizes up to 2000 μm, revealed a similar trend (Figure 2) probably owing to an increased tolerance for transposon proliferation in large genomes of larger organisms. It should be noted that the taxonomic assignment of environmental RT sequences analyzed in the present study should be periodically revisited in the future, as more eukaryotic genomes, presently underrepresented, are added to reference databases.

RTs are known in prokaryotes but are less abundant than in eukaryotes. Well-described prokaryotic RTs are found in group II introns, retrons and DGRs (Simon and Zimmerly, 2008). We detected these three types of RTs in the metagenomic data sets from small size fractions (0.2–5 μm). Among these three types of elements, only group II introns are known to exhibit autonomous mobility (Simon and Zimmerly, 2008). Retrons are chromosomally encoded RT-encoding elements that produce multicopy single-stranded DNA/RNA molecules in the cell (Inouye et al., 2011). Very little is known about their function, mobility and effect on the host cell, but retrons may be more abundant in environmental bacteria than previously recognized from the studies of model organisms given the detection of their sequences in our study as well as in a recent environmental sequencing study (Labonte et al., 2015). DGRs, found in phage and bacterial genomes, can confer selective advantages to their hosts by diversifying DNA sequences through RT-mediated processes that introduce nucleotide substitutions at defined locations within a target gene (Medhekar and Miller, 2007; Miller et al., 2008; Arambula et al., 2013). In stark contrast to eukaryotic elements, these bacterial retroelements are not known to accumulate in high numbers within a genome (<1% of a genome) (Simon and Zimmerly, 2008). Consistently, RT-like sequences detected in the small size fraction metagenomes (0.2–5 μm) amounted only up to 0.26%. The eukaryotes whose abundance was found to correlate with the abundance of group II intron RTs might have some ecological links with bacteria harboring this type of RT (Supplementary Table S8).

The RT transcriptional landscape obtained from metatranscriptomes was markedly different from that observed from the metagenomes. First, relative RT abundances in metatranscriptomes were much lower (0.002–0.148%; 0.021% on average) than in metagenomes (0.001–13.47%; 2.23% on average) and exhibited less variability across different size fractions. This lower abundance of expressed RTs is probably due to the small proportion of transcriptionally active RT genes among those found in metagenomes. Second, the transcribed RTs were dominated by the three groups of LTR retrotransposons (Gypsy, Copia and retrovirus) and group II introns. Many of the taxonomically assigned Gypsy and Copia sequences were associated to Streptophyta, retroviruses to Vertebrata (or Retroviridae) and Group II intron to Gammaproteobacteria. Interestingly, RT transcripts from these elements were detected in all analyzed size fractions. Transcription of LINEs was previously shown to be induced by stress (Teneng et al., 2007), and the detection of LINE transcripts was indeed restricted to a smaller number of samples with high PO4 values than for LTR retrotransposons. Furthermore, we noticed that for certain bacterial RT groups, activity was confined to specific sampling stations. Retron activity was observed only in St TARA_007, and DGR only in St TARA_030, and they were often taxonomically assigned to Afipia and Achromobacter, respectively. When detected, these two groups of bacterial RTs were transcriptionally active in all the analyzed size fractions. Presence of RT transcripts from bacterial elements (group II intron, retron and DGR) in large-size fractions is likely due to the existence of bacteria physically associated to larger organisms (feeding, parasitic, endosymbiotic; (Lima-Mendez et al., 2015)) or those naturally forming aggregates, for instance, in the pellet of animals (Frada et al., 2014) or on the surface of microplastics. RT transcripts might be transcribed in such bacteria. It is widely accepted that many small-sized organisms can be associated with larger material or organisms (Arnosti et al., 2012). Nevertheless, we cannot formally exclude the possibility of small amount of contamination of free floating bacteria. Bacteria may form artifactual aggregates during sampling and could be retained on the filters, although filter clogging was avoided in our sampling. Sequences of bacterial origin were included in our metatranscriptomic data likely due to the interference of short polyA stretches (Dreyfus and Regnier, 2002) mimicking the presence of eukaryotic mRNA polyA-tails. Therefore, the relative abundance of bacterial RT transcripts might have been underestimated in our study.

Following a systematic analysis of the available genomes of eukaryotes, prokaryotes and viruses as well as metagenomes, Aziz et al. (2010) found that Class II transposases were the most abundant and ubiquitous genes in nature. However, in our list of highly abundant sequence domains (Supplementary Table S6), we found only a few such domains (for example, Tn3 transposase DDE domain (pfam01526) ranked 78th (180–2000 μm fraction), and the putative transposase OrfB (PHA02517) ranked 74th (5–20 μm fraction)). This may be due to the fact that bacterial, archaeal and viral sequences were overrepresented in the data analyzed by Aziz et al., 2010. Alternatively, the Class II transposases may be underrepresented in the CDD database; Aziz et al. re-annotated all TEs in their study. More precise and systematic quantitative comparisons of the relative abundances of Class I and Class II TEs (enriched in different size fractions) will require other types of data than those used in these and our studies, because size-fractionated samples are not suitable for such a comparison. Therefore, we did not compare the abundance of Class II TEs with that of Class I TEs in this study. Furthermore, the high abundance of RTs and their transcription patterns observed in our environmental sequence data set from the Mediterranean Sea will have to be investigated with more global data converging a wider range of oceanic biomes.

The selfish nature of RT-encoding elements and their intricate functional/evolutionary interactions with their hosts have contributed to their success in persisting and propagating in the genomes of all domains of life. Given the abundance and transcriptional activities observed in this and previous studies, RTs in marine environments might have important roles not only for long-term evolution, but also for adaptive processes occurring within plankton populations over shorter time scales.