Article
Transposon-dr iven transcr iption is a conserved
feature of verte brate sperm atogenesis and
transcript evolution
Matthew P Davis
1 , †
, Claudia Carrieri
2 , 3 , †
, Harpreet K Saini
1
, Stijn van Dongen
1
, Tommaso Leonardi
1
,
Giovanni Bussotti
1 , 4
, Jack M Monahan
1
, Tania Auchynnikava
5
, Angelo Bitetti
6
, Juri Rappsilber
5 , 7
,
Robin C Allshire
5
, Alena Shkumat ava
6
, Dónal O ’ Carroll
2 , 3 ,*
& Anton J Enright
1 ,**
Abstract
Spermatogenesis is associated with major and unique changes to
chromosomes and chromatin. Here, we sought to understand the
impact of these changes on spermatogenic transcriptomes. We
show that long terminal repeats (LTRs) of specific mouse endoge-
nous retroviruses (ERVs) drive the expression of many long non-
coding transcripts (lncRNA). This process occurs post-mitotically
predominantly in spermatocytes and round spermatids. We
demonstrate that this transposon-driven lncRNA expression is a
conserved feature of vertebrate spermatogenesis. We propose that
transposon promoters are a mechani sm by which the genome can
explore novel transcriptional substrates, increasing evolutionary
plasticity and allowing for the genesis of novel coding and non-
coding genes. Accordingly, we show that a small fraction of these
novel ERV-driven transcripts encode short open reading frames
that produce detectable peptides. Finally, we find that distinct ERV
elements from the same subfamilies act as differentially activated
promoters in a tissue-specific context. In summary, we demon-
strate that LTRs can act as tissue-specific promoters and contri-
bute to post-mitotic spermatogenic transcriptome diversity.
Keywords endogenous retroviruses; genome evolution; lncRNA;
spermatogenesis; transcriptome
Subject Categories Chromatin, Epigenetics, Genomics & Functional
Genomics; Transcription
DOI 10 . 15252 /embr. 201744059 | Received 10 February 2017 | Revised 29
March 2017 | Accepted 11 April 2017
Introduction
The production of high-qualit y gametes is essential to the propaga-
tion of life and the long-term health of a species. Thus, cells of the
germline and the molecular processes occurring within them carry
special importance to the evolution of life. Spermatogenesis (Fig 1A)
is a developmental process that ensures continuous production of
spermatozoa and fertility in adult life [1]. Spermatogenesis can be
simplified to three distinct stages: mitotic, meiotic and spermio-
genic. The mitotic component comprises spermatogonial popula-
tions containing spermatogonial stem cells and differentiating
spermatogonia [2]. These divide numerous times to amplify the pool
of cells that will complete spermatogenesis, ensuring the production
of large quantities of sperm [1]. Thereafter, cells enter the meiotic
phase undergoing DNA replication, chromosome recombination
followed by two rounds of segregation generating haploid round
spermatids. These subsequently enter terminal differentiation of
spermiogenesis, converting these cells of round morphology into
highly specialized spermatozoa [3]. The processes of meiosis and
spermiogenesis are associated with dramatic changes to the chro-
matin template and transcription itself (Fig 1A). Leptotene and
zygotene (early stages of meiosis) are transcrip tionally inert. Transi-
tion to pachytene coincides with resumption of transcription and
genomewide loss of euchromatic repressive markers (H3K9me2)
[4,5]. Furthermore, the fundamental nature of chromatin dramati-
cally changes through spermiogenesis repackaging and compacting
the haploid genome [3]. This is achieved through successive
replacement of the majority of histones with transitional proteins
and then protamines [6]. Previous studies clearly indicate that
testes, at the whole-tissue level, express a significantly greater
number of transcripts than other tissues with particularly high long
1 European Molecular Biology Laboratory, European Bioinformatic s Institute (EMBL-EBI), Cambridge, UK
2 European Molecular Biology Laboratory, Mouse Biology Outstation, Monterotondo, Italy
3 MRC Centre for Regenerative Medicine, Institute for Stem Cell Resear ch, School of Biological Sciences, University of Edinburgh, Edinbur gh, UK
4 Institut Pasteur – Bioinformatic s and Biostatistics Hub, C 3 BI, USR 3756 IP CNRS, Paris, France
5 Wellcome Trust Centre for Cell Biolo gy, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
6 Institut Curie – CNRS UMR 3215 , INSERM U 934 , Paris, France
7 Institute of Biotechnolog y, Technische Universität Berlin, Berlin, Germany
*Corresponding author. Tel: + 44 131 6519631 ; E-mail: donal.oc [email protected]
**Corresponding author. Tel: + 44 1223 492668 ; E-mail: aje@eb i.ac.uk
†
These authors contributed equally to the work
ª 2017 The Authors. Publ ished under the terms of the CC BY 4 . 0 licen se EMBO reports 1
Published online: May 12, 2017

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Figure 1 .
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non-coding (lncRNA) expression [7 – 10]. Furthermore, recently,
systematic efforts have been made to understand the intricacies of
changes in both transcription and the chromatin state throughout
this developmental process through the analysis of specific cell
populations [9,11]. Amongst other observations, these have defined
a progressive transition to a permissive transcriptional state in post-
mitotic populations. This includes the general upregulation of a
number of genomic elements, including several repeat classes [9]. A
unifying feature of all the listed chromatin alterations is that they
expose the germline to the vulnerability of transposon mobilization
via loss of these repressive markers.
Transposable elements (TEs) occupy a large fraction of mamma-
lian genomes having colonized approximately 35 – 50% of human
and mouse genomes [12,13]. In mouse, the two most significant
classes of autonomous TEs are long interspersed nuclear elements
(LINEs) and endogenous retroviruses (ERVs) occupying approxi-
mately 19 and 9% of the mouse genome, respectively [13]. ERVs
are retroviruses that colonized the germline and are then transmit-
ted vertically across generations [14]. Retroviruses code for a series
of proteins ( gag, pro, pol and env ) flanked by two long terminal
repeats (LTRs) that are essential for their replication [15]. However,
upon acquiring an endogenous lifecycle, the env protein is no longer
required and may be lost, while the replicative components of ERVs
retain the hardware required to copy-paste themselves to novel loca-
tions [16]. ERVs replicate via intermediate RNA genomes, with
reverse transcription converting these to DNA for integration. The
repetitive nature of the LTRs allows for effective replication of the
viral ends [17]. In addition, LTRs contain transcription factor bind-
ing sites (TFBS), a promoter and a polyadenylation signal
[15,18,19]. Due to the new set of selective pressures associated with
vertical rather than horizontal transmission, not all ERVs remain
complete [20]. Copies often accumulate mutations or become frag-
mented over time [21], and frequently, solitary LTRs remain at an
integration site following recombination between adjacent LTR
regions [22].
Non-LTR retrotransposons, such as LINEs and SINEs, are also
prevalent [23] in vertebrate genomes (e.g. LINE1), and such
elements are again capable of disrupting proximal gene activity
either via specific internal features or through insertional mutagene-
sis [24]. Thus, ERVs, LINEs and SINEs have the potential to be
directly and highly mutagenic both in terms of insertional gene
disruption and through gene deregulation associated with integra-
tion of their powerful regulatory elements. They also indirectly
provide homology for non-allelic recombination causing genomic
deletions, inversions and duplications [24,25]. Thus, transposons
have had major impact on the architecture, function and evolution
of animal genomes.
Due to the features associated with their LTRs, ERVs are increas-
ingly seen as both drivers of genome architecture but also as active
players in shaping transcriptomes in tissue-specific manners. ERVs
have been shown to act as enhancers [26,27], alternative promoters
[28], splice sites with associated exonic sequences [29] and
polyadenylation sites [18]. LTRs have been observed as alternative
promoters of individual protein-coding genes. For example, the
species-specific insertion of LTRs regulate the NAIP locus [28] and
in mouse an LTR acts as an alternative promoter for Dicer [30].
High-throughput sequencing increasingly illustrates the role of
ERVs in genomewide transcription [31,32]. This has been most
comprehensively demonstrated in embryonic tissues and pluripotent
cells [33] where ERV-associated transcripts have spliced into adja-
cent genes or genomic regions [34,35]. In the case of the mouse
embryo, MuERV-L enhancer co-option drives the expression of over
a hundred totipotency-related genes at the two-cell stage [35].
However, ERVs have also been shown to have wider roles in other
tissues such as the placenta where ERV env genes play an essential
function in placental development and ERVs are enriched within
enhancers, contributing transcription factor binding sites [27,36].
Finally, there is evidence that ERVs impact the female germline
where ERV-derived transcriptional start sites (TSSs) are a significant
phenomenon [37] and the male germline where RLTR10B is linked
as a promoter to at least 10 transcripts in testes [32].
lncRNAs have previously been postulated as a possible pool for
deriving novel functionality and novel peptides [38,39] Their rapid
birth and death makes them a suitable substrate for this type of
evolution [8,10,40,41]. In the testis, pervasive transcription has
been hypothesized to underpin the emergence of novel transcripts
[42]. Hence, the germline represents a testing ground for transcrip-
tional exploration and evolution, as one expects generally toxic and
deleterious products to be rapidly eliminated.
Previous studies of the male germline reveal a highly complex
and global RNA regulatory network of mRNAs, lncRNAs and
piRNAs with TEs and pseudogenes acting as regulatory sequences
[11,43]. Other studies clearly indicate that testes express a signifi-
cantly greater number of transcripts than other tissues [8,9]. TEs
have previously been found as functional domains within lncRNAs
and have contributed to their origin, diversification and regulation
[44 – 46]. Profound changes in chromatin during spermatogenesis
provide a window of opportunity for transposon activity, coupled
◀ Figure 1 . Discover y and analysis of non-co ding transcripts during muri ne spermatogenesis.
A The stages and major events of murine spermatogenesis .
B Left: The number of detected tran script clusters for protein-coding (green), lncRNA (purple ) and intermediate classes (i.e. transcripts which passed one of two test s for
coding potential). In each case, the total number of detected non-redu ndant transcripts is shown in parentheses. Right: The genomic context classification for the
detected lncRNA clusters, broken down according to hierarchy (see Appendix Supplementary Methods ). Bottom: The fraction of detected transcript clusters of eac h
type according to the cell type in which they are most highly expressed.
C Left bottom: Testes-specific Pol II occupanc y at TSSs (  1 kb) from the assembly for coding and lncRNA transcripts. Left top: Averaged Pol II occupancy profiles
around TSSs for coding (green) and lncRNA s (purple). Right bottom: Scaled exon conservatio n levels (  1 kb) for coding and lncRNA transcripts. Right top: Averaged
exon conservation summary profiles for coding (green) and lncRNAs (purple).
D Heatmap showing percentage sequence identity for each of 2 , 002 detected mouse lncRNA transcripts against identified matches in the comple te genomes of 15
species. The lowest level of detectable homology was 40 % (white). A total of 1 , 422 lncRNAs are excluded as their repeat content rendered them unmappable , even in
mouse.
E The relative express ion of both coding and non-coding clusters in eac h cell type.
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Matthew P Davis et al ERV-driven germline transcription EMBO reports
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with pervasive transcription of lncRNAs [8], thus creating a unique
environment for transcript evolution. Hence, we sought to deter-
mine whether transposable elements drive this pervasive lncRNA
transcription and contribute to de novo transcript genesis.
Results
Defining mouse spermatogenic transcriptomes
To explore features of the spermatogenic transcriptomes, we gener-
ated ribo-depleted strand-specific RNA-Seq libraries isolated from
several purified populations of mouse germ cells. The three princi-
pal stages of adult spermatogenesis were represented by in vitro -
cultured spermatogonial stem cell lines, meiotic spermatocytes and
the haploid round spermatids (Fig 1A). In addition, erythroblasts
(EBs) were used as a non-germline out-group for comparison.
Samples were sequenced to high depth and reads were assembled
de novo and ab initio and merged to produce a unified transcript
set (Appendix Fig S1A). These transcripts were filtered, on splic-
ing, length and cross-assembly representation (Appendix Fig S1B
and C) and grouped into transcriptional clusters sharing overlap-
ping exons. Finally, the coding potential of all transcripts was
determined using BLAST [47] and phyloCSF [48]. Thresholds for
determining the coding potential of loci were defined through
comprehensive analysis of scores associated with known protein-
coding and non-coding loci derived from Ensembl [49] (v69)
(Appendix Fig S1D and E). In total, 68990 transcripts remained
after filtering, representing 17058 clusters (Fig 1B). Of these clus-
ters, 12,013 are protein-coding, 3,424 were confidently ascribed as
lncRNAs and 1,621 as “intermediate”, having only passed one of
the coding potential tests (Fig 1B). The vast majority (92.6%) of
clusters that overlapped a pseudogene loci were placed in the
coding or intermediate classes and pseudogene-associated clusters
were depleted from the lncRNAs (Appendix Fig S2); 6,511 tran-
scripts have not been previously identified in Ensembl ( v81 ), and
more than 75% of the lncRNA transcripts are novel
(Appendix Table S1). The largest proportion of lncRNAs are classi-
fied as intergenic followed closely by those overlapping the 5 0 TSS
of known protein-coding genes (Fig 1B).
To investigate the quality of assigned TSSs in our assembly, in
the absence of cell type-specific 5 0 Cap analysis gene expression
(CAGE) tags, we explored the 5 0 localization of ENCODE [50] testis
RNA Polymerase II (Pol II) ChIP-Seq signals. As expected, coding
transcripts have a strong TSS-associated Pol II peak (Fig 1C).
LncRNA transcripts also associate with Pol II peaks with a weaker
signal, perhaps due to the lower level of expression of lncRNAs in
general [7]. To explore the quality of TSSs, we compared 5 0 ends of
assembled transcripts to FANTOM5 CAGE peaks (Appendix Fig
S3A) and Ensembl annotation (Appendix Fig S3B). In general, TSSs
associated with higher read depth more closely resemble those in
Ensembl (Appendix Fig S3B). It is clear from these analyses that
although a large number of our assembled TSSs have CAGE
evidence, a proportion of the assembly likely represent fragments of
transcripts. This is to be expected, as cell-specific matched CAGE
samples were not available for filtering (Appendix Supplementary
Methods). However, even had these data been available, transcripts
with repetitive promoter regions (such as those discussed below)
would be discarded at a higher frequency when using approaches
reliant on the mapping of tags to TSSs.
We inspected exon conservation across the assembly. Protein-
coding transcripts exhibit higher sequence conservation across their
exons and in particular at splice junctions (Fig 1C), while lncRNA
exons are far less conserved [8,51 – 53]. We next explored gene-level
conservation via homologue detection from 15 whole genomes
(Fig 1D). Interestingly, many lncRNAs (42%) were not mappable to
any species due to the presence of repetitive and low-complexity
sequences (median repeat and low-complexity content 41% versus
9% in mapped, see Appendix Supplementary Methods). This
homology-based approach confirms the existence of sequence
across species, but does not indicate however, whether such homol-
ogous regions are transcribed. Mappable lncRNAs exhibit low levels
of conservation; 22% are unique to mouse and a further 41% are
pr esen t onl y in mous e and ra t. A smal l frac ti on (2.1 %) ar e high ly
cons er ved an d det ec tab le in 10 or more sp ec ies . Anoth er st riki ng
fa cet o f spe rm ato geni c ln cR NAs is that ther e is a dr am ati c inc re ase in
the ln cR NA expr essi o n lev els as sper ma toge ne sis pr og ress es (Fi g 1E
an d App en dix F ig 4S B) . At th e ro und sp erma ti d sta ge , lnc RN As
exp re ssio n ap pro ache s th at of codi ng tr ansc ri pt s, at lea st in term s of
log
10
(F PKM) va lue s, al th ough i n raw expr es sio n term s , lncRN As ar e
sti ll ex p ress ed at a lo wer level . Th is i s in st ar k co ntra st to eryt hr obl as t
cont ro ls, wh er e lnc RNA s expr es sio n is sig nifi ca nt ly lowe r than co di ng
gen es , in agre em en t with pre vio us ly publ is hed da ta [7]. Ad di ti onal ly,
we ob se rve th at th es e high ly ex pr es sed po st- mi toti c lnc RNA s ar e al so
mo re li ke ly to be i nt erge ni c th an an y ot her cl ass (App en di x Fi g S4 A).
In sum ma ry , the po st- mi toti c sp erma to cyte an d roun d sp erma ti d tran -
sc rip tome s are ch ar acte ri zed by a high fr eq uenc y of abun da nt ly
exp re ssed , cl ade- sp ec ifi c and in te rgen ic ln cR NA s.
The expression of ERV-associated lncRNAs is a characteristic of
post-mitotic spermatogenic transcriptomes
We sought to determine the genomic origins of this class of upregu-
lated spermatogenic lncRNAs. Spermatogenesis coincides with
dramatic chromatin remodelling associated with derepression of
certain TEs, although transposition is suppressed at the post-tran-
scriptional level [4,54]. Furthermore, TEs have been observed to be
a mobile source of transcription factor binding sites and promoters
[19,28].
We hypothesized that certain TEs have overcome suppression in
the murine male germline to act as drivers of adjacent non-coding
transcription. If this were the case, one would expect a higher frac-
tion of non-coding transcription occurring near these elements. To
test for this enrichment, we compared the presence of TEs within
the promoters of both coding and lncRNA transcripts (Fig 2A). A
number of elements were shown to be significantly enriched in
lncRNA versus protein-coding promoters. Of these, LTR elements
were the most significant ( P < 1 × 10
 42
). This relative increase in
the proportion of non-coding promoters associated with LTR
elements is also reflected as a genomewide enrichment for the
elements in the promoters and at the TSSs of lncRNAs expressed in
round spermatids, particularly at higher expression thresholds
(Appendix Table S2). Additionally, the observed increase in lncRNA
expression in later stages of spermatogenes is is associated with an
increasing fraction of LTR-linked lncRNAs (Fig 2B). In round sper-
matids, up to 1,051 expressed lncRNA clusters (33%) are associated
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C
D
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B
Figure 2 .
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with one or more ERV overlappin g either a promoter region or TSS
(Appendix Fig S5). Interestingly, SINE elements and DNA elements
are also enriched in lncRNA promoters (but, with the exception of
SINE elements in EBs, not at the TSSs, Appendix Table S2). In the
case of SINE elements, this enrichment is not reflected as a shift in
the proportion of the associated promoters that are non-coding
(Fig 2A) and may represent a general enrichment of SINE elements
in genic regions [55].
We next sought to understand whether this observed enrichment
within lncRNA promoters is generic to all LTR elements or specific
ERV subfamilies. Promoters containing ERVs were examined to
identify those more likely to be associated with lncRNAs transcripts
than expected. This analysis reveals a set of lncRNA-associated ERV
subfamilies enriched in proximal lncRNA transcripts relative to
protein-coding promoters (Figs 2C and E, and Appendix Fig S4C).
Indeed, ORR1E is associated with the promoters or TSSs of 62
lncRNAs and only 85 protein-coding genes. By contrast, MT2B is
paired with 18 and 233 genes, respectively (Appendix Fig S6). The
subfamilies most associated with lncRNA expression are members
of class II & III ERVs [56]. This set comprises: ORR1 (Class III), MT
(Class III), RMER (Class II & Class III) and RLTR (Class II). Indeed,
several related subfamilies of ORR1 (especially ORR1E) are linked to
lncRNA expression (Fig 2D). Irrespective of the ERV subfamily clas-
sification, associated lncRNAs are upregulated at later stages of
spermatogenesis (Fig 2E) with intergenic lncRNAs tending to make
up a large fraction (Appendix Fig S4D). These associations are again
reflected in ORR1E, RMER17C and MTE2b showing a genomewide
enrichment in the promoters and at the TSSs of lncRNA transcripts,
particularly in post-mitotic cells (Appendix Table S2).
In summary, within the post-mitotic spermatogenic transcrip-
tome, many ERV subfamilies are highly associated with promoters
of abundantly expressed lncRNAs.
Select ERV elements act as lncRNA promoters in spermatocyte
and round spermatids
The above association between ERV elements and lncRNAs may
indicate that LTRs of ERVs act as the actual promoters of these tran-
scripts. Previous studies have also indicated this likelihood in
somatic tissues [46]. We sought to exclude the alternative possibili-
ties that the observed effect is simply a bias for the insertion of
subsequently silent ERVs into open chromatin proximal to active
genes or that the ERV enrichment is a consequence of difficulties in
read mapping and assembly across repetitive regions. To this end,
we sought independent, transcriptome-wide evidence to test for
ERV promoter activity. We mapped a panel of FANTOM5 [57] CAGE
tags to the complete set of individual ERV elements. Those ERV
elements found in the promoters of our assembly are indeed tran-
scriptionally active in adult testes (Fig 3A). Although some other
individual elements from the same subfamilies possess broader tran-
scriptional profiles.
Next, we aimed to determine whether ERVs are actively driving
lncRNA transcription. Here, we harness the strand specificity of
CAGE tags. If non-coding transcription was being driven by ERV
elements, one would expect to observe CAGE-derived strand-
specific expression associated with ERV elements to overlap TSSs.
Indeed, this is precisely what is observed (Fig 3B, “overlapping”)
with ERVs overlapping TSSs exhibiting strand-specific (sense)
expression. We also consider ERV elements not directly overlapping
TSSs (Fig 3B, “non-overlapping”). Strikingly, the expression of ERV
elements present in the promoter (1kb) but not directly overlapping
the TSS is considerably lower. In general, these elements remain rela-
tively transcriptionally silent. Interestingly, when we compute simi-
lar data for coding transcripts (Appendix Fig S7), we also observe an
effect, again only for overlapping ERV elements. However, here the
effect appears to be predominantly antisense for many subfamilies of
ERV. Hence, using FANTOM5 CAGE data, we confirm that ERV
elements are a source of genomic transcripts in the testes.
As a complementary approach, we reanalysed our RNA-Seq data
using a method to allow directional TSSs to be easily recognized
and to demonstrate the potential for transcriptional activity within
repeat subsets. We divide promoter ERV elements into two sets
according to whether they directly overlap the TSS of an assembled
transcript regardless of coding potential. For each of these two sets,
we compute the coverage of uniquely mapped reads across individ-
ual elements and then calculate the mean coverage across all
aligned subfamily members. ERV sets enriched in active promoters
are expected to produce a directional coverage gradient of RNA-Seq
signal, due to transcription starting at a TSS within each ERV and
continuing in 3 0 direction. On the other hand, ERV sets not acting as
promoters are expected to have uniform or no RNA-Seq coverage if
they are part of longer transcripts or are not transcribed. Using the
◀ Figure 2 . Increasin g influence of transposable elements during spermatogenes is.
A The shift in the proportion lncRNAs prom oters associated with specific repeat classes relative to a genomewide background. Relative enrichments in mouse round
spermatids for the four major classes are shown . Repeat classes are ordered according to adjusted P -values (Holm ’ s method) of enrichment, while “ n ” denotes the size
of each promoter set. Only repeats with > 500 associated regions are shown.
B For each sample, the numb er of lncRNA clusters without (dark blue) and with (red) ERV/LTR elements in their promoter are shown. Left: Each lncRNA is assigned to
the single sample where it had maximal expression. Right: Each lncRNA is assigned to any sample where its expression was above a minimum FPKM threshold .I n
both representations , only lncRNA clusters with a single TSS were used.
C Enrichments for lncRNA-associated ERV/LTR subfamilies. Enrichment is computed based on the relative proportion (coding vs. non-coding ) of promoters associated
with LTR elements. Individual bars represent relative non-coding enrichment for specific ERV subfamilies. A conservative approach (light green bars) computes
enrichment for non-coding promoters relative to the non-coding fraction of genomewide promoters that contain any ERV. A less conservative enrichment (dark green
bars) is computed relative to the non-coding fraction of promoters without any detectable ERV. Promoter sets where both conservative and less conservative
enrichments are above zero are termed “ lncRNA-associated ” . Those below zero for both measures are ass igned as “ background ” . All other ERV subfamilies are te rmed
“ intermediate ” .
D Heatmap of relationships between different ERV subfamilies bi-cl ustered according to sequence similarity correlation. The colour key to the right of the plot
corresponds to eac h element ’ s ERV family.
E The number of non-coding clusters with one or more prom oter containing a relevant ERV element according to the cell type. Clusters are assigned to the cell type
within which they exhibit their maximum expression.
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ORR1E element as a representative example (Fig 3C and
Appendix Fig S8A), we observe a strong, sense coverage gradient in
the 3 0 direction of downstream transcripts across the TSS. In stark
contrast, there is no directional antisense signal. This implies mono-
directional transcription beginning within the ERV element. Addi-
tionally, no signal in either direction is identified amongst ORR1E
elements not overlapping an assembly TSS. Furthermore, this tran-
scriptional gradient is observed only in late stages of spermatogene-
sis (Fig 3C and Appendix Fig S8A). Next, we assess such coverage
gradients across all promoter-associated ERV elements
(Appendix Fig S8B) from lncRNA-associated, background and inter-
mediate subfamilies (Fig 2C). This analysis clearly demonstrates
B
0
0.2
0.4
0.6
0.8
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1.2
1.4
SSC Scyte EB RStids
Mean logged coverage
−1.0
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Antisense Sense
Opposite
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End
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testis adult
pancreas
mammary gl. lactating d2
prostate
uterus pregnant d19
hippocampus
mammary gl. pregnant d19
placenta d17
skin
Intestinal mucosa
submandibular gl.
vesicular gl.
eyeball
aorta
cerebellum
spinal cord
olfactory brain
pituitary gl.
ovary
uterus
adrenal gl.
spleen
epididymis
RLTR20A4
ORR1A3-int
ORR1B1-int
MTE2b
MTEa
ORR1D2
RLTR17B Mm
MTE2a
ORR1D1
ORR1F
ORR1E
RMER17C
MTC
MTD
MT2B
RMER15
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A
accessory axillary lymph node
Non-Overlapping Overlapping
MTE2a
MTC
ORR1D1
ORR1D2
ORR1E
RMER17C
MTEa
ORR1F
MTE2b
RMER15
sense
sense
antisense
antisense
RLTR17B Mm
overlapping
non-overlapping
Promoter Associated ERV Expression by T issue
Log 2 (scaled tag counts+1)
lncRNA Associated
Intermediate
Background
average log 2 (counts+1)
Expression gradients originating from L TRs
non-overlapping overlapping
Figure 3 . Analysis of ERV expression.
A The CAGE expression (log 2 scaled) of promoter-associated ERV elements from selected subfamilies. When CAGE tags map multiple loci, these are split between
repeats to avoid double counting. The colour bar (left) corresponds to the ERV set to which the ERV element belongs: either lncRNA-associ ated, intermediate or
background.
B The CAGE-derived expression of lncRNA promoter-ass ociated ERV element sets found in the promoters of non-cod ing transcripts, divided acco rding to the relationship
of the ERV to the closest adjacent TSS (overla pping or non-overlapping, according to inset cartoon) and the orientation of the CAGE tags relative to the TSS (sense or
antisense). Only uniquely mapped reads are considered.
C Mean cell type-specific RNA-Se q read coverage gradien t across promoter-associated ORR 1 E-LTRs and their flanking regions for repeats overlapping a transcript TSS
and those elsewhere in the promoter region. Coverage is reported in the sense and antisense orientation re lative to the TSS. Each row depicts a representative
replicate from one of the four cell types.
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Matthew P Davis et al ERV-driven germline transcription EMBO reports
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that lncRNA-associated ERV elements exhibit similar expression
patterns to the ORR1E LTR described above. The expression of inter-
mediate and background ERV class members (Fig 2C) is again
mono-directional. However, it is no longer as restricted to meiotic
and post-meiotic stages. In conclusion, LTRs of several specific ERV
subfamilies are active promoters driving lncRNA expression in late
spermatogenesis.
TE-driven transcription is a conserved feature of vertebrate
spermatogenic transcriptomes
Having observed ERV families driving expression of lncRNAs in
spermatocytes and spermatids, we sought to determine whether this
is a conserved feature of vertebrate spermatogenesis. We obtained
and analysed data from rat ( R. norvegicus ) and zebrafish ( D. rerio ).
Rat diverged from mouse approximately 30 Mya [58], while zebra-
fish diverged from mammals over 400 Mya. We performed strand-
specific RNA-Seq from ribosomal-d epleted RNA isolated from rat
spermatocytes and zebrafish whole testis. We tested these samples
to determine whether a broad panel of promoter-associated repeat
elements are more likely to be associated with lncRNAs or coding
genes. Again, we observe ERV elements as the most highly enriched
repeats in lncRNA promoters in rat (Fig 4A and Appendix Fig S8C).
We also observe LINE and SINE B4 elements enriched in both rat
and mouse. The association of ERVs and LINE element families in
mouse (promoter and TSS) and in rat (promoter) lncRNAs was con-
firmed by genomewide enrichments in these regions (Appendix
Table S2). In contrast, zebrafish show abundant enrichment of DNA
repeats for lncRNA promoters relative to their protein-coding coun-
terparts (Fig 4A) and LINE/LTR families are not significantly
enriched in lncRNA promoters genomewide (Appendix Table S2).
For all species considered, the vast majority of SINE element
subfamilies showed no enrichment in lncRNA promoters and are
skewed towards coding promoters (Fig 4A). Given these results and
in the light of our initial enrichment analysis (Fig 2A), coverage
gradient analysis was performed for the most significantly enriched
family of repeats, from the LINE, LTR and DNA classes, in each
species. We again sought to confirm that repeats are actively driving
the expression of adjacent or overlapping TSSs (Fig 4B and
Appendix Fig S8D – F). The MALR-LTR elements drive expression of
overlapping TSSs in both mouse and rat spermatocytes. However,
zebrafish exhibits only a weak signal for Gypsy LTR-driven expres-
sion in zebrafish testes. This may be a consequence of the use of
whole testes samples or may suggest that LTR-driven transcription
is not as significant a component of germline transcription in tele-
osts. In contrast, LINE elements represent active promoter elements
in all three species. DNA elements appear to be broadly transcrip-
tionally silent (Appendix Fig S8D – F) with gradients much more
difficult to discern, although there is some evidence that hAT-
Charlie repeats are transcriptionally active in rat spermatocytes
(Appendix Fig S8E). In summary, LTR-associated lncRNAs are a
conserved feature of rodent meiotic transcriptomes; however, the
phenomenon of repeat-driven transcription can be expanded to
include specific groups of LINE elements in mouse, rat and zebrafish.
ERV-derived lncRNAs as a source of transcript evolution
Having confirmed that ERV and transposable elements can drive the
transcription of lncRNAs in vertebrate spermatogenic transcrip-
tomes, we hypothesized that the expression of novel transcripts
through the co-option of LTR-derived promoters could provide an
opportunity to evolve novel non-coding and/or coding genes. In the
case of coding genes, open reading frames (ORFs) would evolve that
could be translated into peptides. These emerging nascent ORFs
would be subject to positive selection as they test, acquire or refine
novel functionality. To test this hypothesis, we identified the longest
ORF in each assembled protein-coding and non-coding transcript
cluster. Subsequently, for each ORF, we calculated the fraction of
bases undergoing negative selection or rapidly accumulating
changes in the ORF compared to its putative 3 0 UTR. As expected,
ORFs of protein-coding transcripts show a greater proportion of
bases undergoing negative selection (Fig 5A) as compared to their
3 0 UTRs. For lncRNAs, neither their ORFs nor their 3 0 UTRs are under
clear negative selection. However, such ORFs could be rapidly
evolving. To test this, we extracted subsets of non-conserved ORFs
to explore in detail (see Appendix Supplement ary Methods). We
observed a small but significant shift, with a greater proportion of
ba ses in th ese OR F sets ev ol vin g rapi dly as co mp ar ed to thei r 3 0 UTRs
( P < 0. 01 & P < 1.3 × 10
 6
, lncR NA s an d pr otei n-co di ng , re spec -
ti vely , Fi g 5B). As one migh t ex pe ct, n on-c ons er ve d rap idl y evo lvi ng
pr ote in- codi ng tr an scr ipt s are enri ch ed for “i mm une resp o nse” -
re late d ge ne s and “s pe rm – egg re co gni tio n” pr ote ins
(A ppen di x Tabl es S3 – S5 ). Th e sim i lar s hift for l nc RNAs is surp ri si ng.
Th is sugg es ts th at at lea st som e lncR NA s cont ai n nasc en t novel OR F s,
accu mu la ting ch an ge s more ra pidl y th an expe ct ed , perhap s un der
se lec tive pres su re . Base d on OR F v 3 0 U TR comp ar iso ns , ORFs fr om
ER V-as so ciat ed lnc RN As ar e not cons erv ed as expe ct ed , showi ng a
ver y sli ght sh if t tow ards more ra pi d evo lut ion ( Fig 5C). To un de r-
stan d w heth er ER V-dr ive n l ncRN A s enco de pe pt ide s fr om nove l
ORF s, w e sub je cted sper ma tocy te an d ro und sp er ma ti d prot ei ns to
LC -MS/ MS ma ss s pect ro me try loo k ing fo r suc h pep tide s. Fro m 17 5
can di date ER V -dri ven OR Fs , we ide nt ifi ed pep tide s corr es po ndi ng to
23 o f the se lect ed OR Fs (Fi g 5D and Ap pe ndi x Ta ble S6 ) an d furt he r
conf i rmed th ei r exis tenc e wit h targ et ed mas s spectr o metr y. Su ch
pe ptid es ar e un lik el y to b e func ti ona l, ye t ma y se rve as pr ecu rsor s to
func ti ona l pept i des via ev ol ut ion.
▸
Figure 4 . Germl ine transposon activity in three species.
A Relative numbers of non-coding ( y -axi s) and coding ( x -axis) promoter regions containing specific repeat sub families for mouse (top), rat (mi ddle) and zebrafish
(bottom). The straight black line in all cases repres ents the expected levels based on the genomewide freque ncy of expressed promoter regions. Selected repeats are
highlighted to illustrate non-coding or coding-enriched repeat families. Repeat classes and enrichment sig nificance are indicated (inset legend). P -values measure the
significance of non-cod ing promoter enrichment according to a hypergeometric test.
B The accompanying RNA-Seq read coverage gradien t plots for the mos t highly enriched non-coding promoter-associate d repeat family from the LINE and LTR classes,
in each of the three species. Coverage across repeat sets is provided in both sense and antisense orientation s relative to the adjacent TSS for repeats either containing
a TSS (overlapping) or which coincide with a 1 -kb promoter region (non-overlapping ). Coverage plots are provided for a single representative replicate.
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AB
Figure 4 .
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A
B
D
C
Figure 5 .
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Activity of ERV elements as promoters in somatic tissue
Our results indicate ERVs are drivers of major transcriptional plas-
ticity in the male germline. Previously, Faulkner et al [31]
performed a thorough analysis of retrotransposon-derived TSSs
using FANTOM 4 CAGE data. We therefore expanded the FANTOM5
CAGE analysis to explore the extent to which our ERV subfamilies
of interest perform as active promoters in other tissues (Fig 6A).
These data show distinct patterns of ERV expression both between
tissues and across ERV subfamilies, confirming the results of the
earlier work [31]. However, given that the expansion in ERV-driven
lncRNA expression in the later stages of male gametogenesis, we
were surprised to see that some of the ERV subfamilies associated
with this process appear to be almost ubiquitously expressed across
germline and somatic tissues. We therefore investigated the expres-
sion of individual repeats for three members of the ORR1 group of
elements (Fig 6B). Remarkably, individual repeats within the same
subfamily have very distinct expression profiles. This is perhaps
most striking for ORR1E with many elements expressed almost
exclusively in the male germline, while others are either expressed
in many tissues or upregulated in the accessory axillary lymph
node, spleen, intestinal mucosa and uterus.
Having established that ERV elements are expressed across many
tissues, we wished to expand this analysis further to explore
whether ERV-regulated lncRNA expression is phenomenon-
restricted to the germline. Using Ensembl annotation and mouse
ENCODE RNA-Seq data, we identified tissue-specific lncRNAs in a
panel of 13 tissues. For these genes, we assessed the absolute
number and fraction of transcripts with a promoter or TSS-asso-
ciated ERV element (Appendix Fig S9). From this assessment, it is
clear that although LTR-associated lncRNA transcription is most
apparent in testes where more than 350 tissue-specific lncRNA tran-
scripts have an ERV element overlapping their TSS, a comparable
fraction of tissue-specific lncRNA TSSs overlap an ERV element in
adipose tissue, lung and liver. At a more granular level, many of the
subfamilies most frequently associated with testis-specific lncRNA
TSSs match those identified by our earlier analyses (Appendix Fig
S10).
These results indicate that although the germline is a significant
source of ERV-driven transcription, ERVs can drive transcription
across many tissues in a highly regulated manner.
Discussion
We present one of the most comprehensive analyses to date of non-
coding transcription during spermatogenesis, complementing the
work of previous studies [9,11]. We were able to achieve high
developmental resolution by assembling the transcriptome from
ribo-depleted RNA-Seq data sets derived from sorted populations
representing the principal stages of spermatogenesis, and subse-
quently, we link this to repeat element expression. Specifically, we
have made particular effort to perform comprehensive analyses of
the quality of our transcriptome assembly and lncRNA models. Our
analysis of repeat expression and its association with large numbers
of lncRNAs in small cell populations highlights many issues
hampering our understanding of these phenomena. It is clear from
the large number of novel transcripts identified in this study
(Appendix Table S1) that in order to fully understand the transcrip-
tomes of specific cell types, transcriptome assembly is essential.
However, in these situations, independent and matched end
evidence is difficult to ascertain and in many cases challenging to
map to a single specific loci. We believe that our data set can also
provide a backbone for further improvements and observations.
Importantly with respect to this work, we confirmed many of our
observations using alternative approaches, incorporating data sets
from Ensembl, ENCODE and FANTOM [49,50,57].
As previously described we note an increase in promiscuous
transcription in spermatocytes and round spermatids [9]. However,
uniquely, we observe MaLR, ERVK and ERVL ERVs driving signifi-
cant expression of lncRNAs in mouse post-mitotic spermatocyte and
round spermatid populations, with the ORR1 family the most strik-
ing example. lncRNA expression is characterized by dramatic
increases in the number of loci expressed and their overall degree of
expression as spermatogenesis progresses, peaking at the round
spermatid stage. As the testis has the most pervasive expression of
lncRNAs as compared to other organs [8,9], our observation of LTR-
driven transcription in post-mitotic spermatogenic cells may in a
large part explain this phenomenon. TEs have been shown to be
involved in the shaping of lncRNA functional domains [45] and it is
now widely accepted that they can form active endogenous promot-
ers, driving the expression of sets of lncRNAs, in particular in
embryonic stem cells and the early embryo. However, here we show
them actively driving the transcription of significant wave of
lncRNAs in the post-mitotic male germline. Hence, these elements
act as an origin for the expression of species- and clade-specific
genes. As such, these findings further corroborate an observation
from the mouse ENCODE project which found ERV1, ERVK, ERVL
and MaLR subfamilies to be enriched in mouse-specific promoters
[59]. ORR1E elements appear to be particularly active in driving
lncRNA expression. This is relevant in the light of previous work
that demonstrated that in rodents, the predominant promoter for
Naip genes is an ORR1E element, in contrast to the human copy of
the gene [28]. This suggests ORR1E may play a particularly impor-
tant role in the derivation of novel promoters. However, although
ERVs represent the repeats most highly enriched in lncRNA
◀ Figure 5 . Transcrip ts annotated as non-co ding with putative open reading frames or det ectable peptides.
A The proportion of significant ly conserved bases in the ORF versus paired 3 0 UTR of the longest ORF selected for each cluster for both coding (left) and non-coding
(right) loci. The red line represents the threshold for the selection of poorly conserved loci (sum of ORF and 3 0 UTR posit ive proportions < 0 . 4 ).
B The proportion of bases evolving at a significant rate in the longest ORF and paired 3 0 UTR at poorly conserved coding and non-cod ing loci selected fro m (A). P -values
compare ORF proportions to those of the 3 0 UTR, according to a sign test.
C Relative conservatio n of the longest ORF from transcripts with an ERV element ove rlapping the TSS which were selected as re presentatives of non-coding clusters.
Top: Proportion of significantly conserved nucleotides. Bottom: Proportion of rapidly evolving nucleotides. In all cases contour s are indicative of point density.
D Two examples of “ non-coding ” clusters with ERV-deriv ed expression for which short peptides have been confirmed by mass spectroscopy. In each case, transcript
models, cDNAs, confirmed peptides, longest ORF, associated repe at and other genomic features are indicated.
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AB
Figure 6 . ERV tr anscription across multiple tissues by family and subfamily.
A CAGE-derived expression heatmap for ERVs in a panel of 28 tissues including somatic tissues. Tissues and ERV subfamilies are bi-clustered according to their
correlation. Tissues and repeat subfamilies with similar patterns will usually group together. The depth of multim apping reads are divided across repeats. A scaled
depth of 1 , 000 in one or more tissue is required for inclusion.
B CAGE-derived expression of individual repeat elements from the ORR 1 D 1 , ORR 1 E and ORR 1 F subfamilie s. The depth corresponds to uniquely aligned CAGE tags with a
scaled depth of 10 required in one or more tissue for inclusion.
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promoters, they are far from alone in this association with lncRNAs,
with LINEs also clearly implicated.
The activity of transposable elements is regulated by a number of
me chan is ms, an d the on set of LT R-d ri ven ln cR NA expr es si on coi n-
cide s with ge no me wide lo ss of th e euch ro mati c H3K9 me 2 repr essi o n
[4 ]. H3K 9me2 i s kno wn to be resi de nt and re pr ess LI NE1 el emen ts
unti l th e la te zy gote ne sta ge [6 0] . In ad dit ion to th e upre gu lat io n of
tr ansc ri pti on fact o rs that m ay bi nd resp ec tiv e LTR s, it coul d be that
the ge no mew id e loss of H3 K9m e2 unl ea sh es this wa ve of ER V-d ri ven
tr ansc ri pti on. In dee d, CpG DN A meth yl at ion alo ne is ins uffi ci en t to
re pres s bo th IA P an d LI NE 1 elem en ts du ri ng me io si s [4].
The germline is conceptually an ideal location to select and
evolve transcript function, as any broadly toxic gene product (RNA
or protein) would be rapidly eliminated through deleterious effects
on gametogenesis. This “out of the testis” hypothesis has been
described previously by Kaessmann [42]. To investigate further, the
work by Soumillon et al [9] explored the increased transcription of
duplicate genes and intronless retrocopies in this unique environ-
ment and also noted an increase in lncRNA transcription. It is this
lncRNA transcription and its potential that we have aimed to
describe in more detail. The presence of mobile and fully formed
promoters endows the vertebrate genome with the opportunity to
rapidly innovate genetic products. Although large numbers of these
TEs would be silent in most somatic tissues, their activation in post-
mitotic spermatogenic cells provides an opportunity for the tran-
scriptome to explore expansive genomic space for potential de novo
gene genesis and subsequent selection of ORFs within lncRNA tran-
scripts. By conn ec ting ER V pro mo ters pr es ente d here to ex tens iv e
an nota ti on of as soci at ed lncR NA s, we we re abl e to inves ti ga te
whet he r th ese tran s cri pts co uld be fu rt her se le ct ed fo r func ti on. A s a
fi rst st ep , we ha ve be gu n to expl or e th eir po te ntia l fo r the ev o luti on
of pro te in-c odi n g loc i. Gen esis o f func ti ona l prote in -c odi ng loci fr om
int er geni c re gion s an d lnc RNA ha s been demo ns trat ed [3 5, 38,3 9, 61 ]
an d in so me ca se s lin ked to the te st is [6 2,63 ]. How ever , th ere re ma in
ma ny unan sw er ed ques ti on s rega rd ing th e mech an ism s invo lv ed in
thes e pro ce sse s. Our ide nt ifi cati o n of pept id es from a subs et of ERV -
dr ive n lnc RN As su gges ts TE pro mo ters coul d fa cil it ate th e ma le ge rm-
lin e as a so urce o f proto ge nes [6 1]. Gi ven th at most lnc RN As ar e
spec ie s- spe ci fic o r res tr icte d to a cl ose ly re lat ed cl ad e [6 4], th e me ch-
an isms de sc ribe d here ma y hav e spec ia l imp ort ance in de no vo ge ne -
si s of bo th lncR NA s and pe ptid e- enco di ng lo ci.
Intriguingly, our analysis of FANTOM5 CAGE data demonstrated
that LTR-driven transcription can be far more widespread than we
were expecting. This is in agreement with the work of Faulkner et al
[31] where the authors went further to demonstrate, as an example,
the tissue specificity of individual members of the VL30 subfamily
of LTRs using an earlier iteration of the FANTOM data. Subsequent
to this earlier analysis, we were able to investigate the expression of
repetitive elements to a relatively high resolution using the latest
CAGE data. In doing so, it appears that individual LTRs of subfami-
lies selected as drivers of lncRNA expression in gametogenesis
themselves have intricate, divergent and tissue-specific expression
profiles (Fig 6). Similarly intricate cell-specific expression patterns
have been noted in the early embryo [34]. This is complemented by
our findings when using Ensembl annotation to define promoter
regions and ENCODE sequence data (Appendix Fig S10). Although
the extent to which LTRs influence expression in testes appears to
be relatively unparallel ed (Appendix Fig S9), the phenomenon itself
is ubiquitous with the potential for much broader impact, extending
beyond the germline adding to the expanding literature describing
similar phenomena.
Finally, we can show that retrotransposon-driven expression is a
conserved feature of vertebrate spermatogenesis and plays a particu-
larly significant role in driving and regulating lncRNA expression in
rodents. Understanding the extent to which ERVs rewire transcrip-
tional networks will be an important future direction, but this work
helps to transfer ERVs from their traditional status as parasitic and
opportunistic DNA elements to promoter elements with a major
influence on the regulation, diversification and evolution of verte-
brate transcriptomes.
Materials and Methods
For full detail of experimental methods in all cases, please refer to
the Appendix.
Sample preparation and sequencing
Per each cell type and species, libraries were prepared from two
biological replicates. Erythroblasts were differentiated from E12.5
mouse livers in vitro [65,66]. SSCs were cultured in vitro as
described previously [67]. Ex vivo germ cells were obtained from
dissection of 8-week-old adult mice through enzymatic digestion
and mechanical disaggregation in EKRB buffer as previously
described [68]. Mouse spermatocytes and round spermatids were
isolated and purified through Becton Dickinson Aria II cell sorter
upon staining with Hoechst DNA dye as previously published [69].
Adult rat spermatocytes were obtained and purified through a simi-
lar procedure. Zebrafish were raised and maintained using standard
procedures [70]. Whole testes were dissected from 1-year-old adult
male AB zebrafish. Mice used were inbreed C57BL/6N strain, rats
were Sprague Dawley

strain purchased from Charles River. Mice
were maintained at the EMBL Mouse Biology Unit, Monterotondo,
in accordance with Italian legislation (Art. 9, 27 January 1992,
number 116) under licence from the Italian health ministry. RNA
was prepared via Qiazol lysis followed by DNase treatment in the
presence of an RNase inhibitor. RNA was recovered via EtOH
precipitation; 5 l g of RNA was ribodepleted using Ribo-Zero (Illu-
mina), and 50 ng of this RNA was used for strand-specific cDNA
library preparation (ScriptSeq, Illumina). Libraries were purified
and analysed on High-Sensitivity DNA chip (Agilent) on BioAna-
lyzer, and each paired-end library was sequenced on one lane of a
HiSeq 2000 sequencer.
Transcriptome assembly
Two biological replicates from erythroblasts, spermatogonial stem
cells, spermatocytes and round spermatids were processed. Adapter
contamination was removed, replicates merged and sequences de-
duplicated such that each sequence is unique per sample using
Kraken [71]. Transcriptomes were assembled using two approaches,
Cufflinks and Trinity [72,73]. For Cufflinks, reads were mapped
using TopHat2 [73]. Trinity transcripts were remapped to the
genome using gmap [74]. Cuffmerge merged these into a unified
assembly. Transcripts were discarded if they matched any of these
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criteria: length < 200 nt, maps to supercontig, is unspliced or has no
strand. Unspliced transcripts were removed to mitigate DNA
contamination and remove retrocopies. The assembly was further
refined by comparing the original eight assemblies to the unified
assembly using a Jaccard score overlap test. Transcripts below a
cumulative Jaccard threshold ( < 2.5) were discarded. This threshold
was based on CAGE and polyA data. Filtered assembly transcripts
were clustered according to exonic overlap, generating a set of
related, transcript clusters encoding multiple isoforms.
Transcriptome classification and analysis
Transcripts were assigned as non-coding if they had a PhyloCSF
[48] score < 50 (based on 29 species UCSC alignments [75]) and no
BLASTx match (E > 1 × 10
 10
) versus Ensembl [49] peptides and
PfamA/B [76]. Transcripts were assigned as “intermediate” if they
matched only one of those criteria. In general, expression was
obtained using FPKM values from RSEM [77] and the replicate mean
was used. For heatmap expression visualization, global compar-
isons of major repeat classes and the three-species analysis, DESeq2
[78] was used to normalize and transform raw counts generated by
HTSeq [79]. Transcript genomic classification was determined with
respect to Ensembl annotations including external enhancer data.
An overlap of 1nt is counted as a genomic feature match. Matches
are assigned by strand or as “both” where an overlap is not
stranded. Pseudogene annotation was also derived from Ensembl.
For RNA Pol II analysis, ChIP-Seq data from ENCODE [50] were
used. Reads were merged between replicates and remapped to the
genome (mm10, Bowtie2). Coverage was computed and visualized
using Bedtools2 and deepTools [80,81]. When multiple isoforms
have TSS sites within 50 bp, only the longest isoform is retained.
For exon conservation analysis, exons of the longest isoform of each
transcript were matched to PhyloP [82] scores for the mouse
genome (UCSC). To assess the quality of TSS annotation, the assem-
bly was compared to FANTOM5 CAGE peaks [57]. Bedtools was
used to identify the closest peak to the TSS of transcripts from
coding and non-coding clusters. These distances were compared to
those of Ensembl transcripts. To assess TSS annotation relative to
read depth, the expression of TSSs was measured using RSEM
(v1.2.7) [77]. TSSs from coding loci were divided by expression
quantiles and the distance to the nearest Ensembl protein-coding
promoter was calculated. Non-coding TSSs were separated using the
same thresholds.
Repeat and ERV analysis
Repeat annotation was obtained using RepeatMasker, NCBI/
RMBLAST and the RepeatMasker database of elements (v20130422)
for mouse, rat and zebrafish. When non-overlapping repeat analysis
is performed, the lowest scoring repeat match at overlapping sites is
trimmed until no overlaps remain. Promoter analysis was performed
by searching for overlaps between repeats and sites 1 kb upstream
of defined TSSs. Unless otherwise stated (see Appendix Supplemen-
tary Methods), when non-redundant promoter analysis is performed
a random transcript from each transcript cluster is selected, the
promoter is defined and all overlapping promoters are excluded. For
ERV expression gradient analysis, aligned reads (Tophat2) were fil-
tered to remove multimappers and retain only the first of each pair.
deepTools was used to calculate positive and negative strand cover-
age of ERV elements overlapping promoter regions and TSSs and
their flanking regions (  2 kb). When assessing the impact of ERVs
on the non-coding transcriptome, promoter and TSSs overlapping
ERVs were considered independently and clusters were assigned the
TSS set in preference.
Promoter enrichments
Zebrafish and rat annotation was downloaded from Ensembl (v81),
filtered and assigned a coding potential (see Appendix Supplemen-
tary Methods). Gene expression was derived from HTSeq counts
and genes were filtered on the specified threshold. For the remain-
ing non-coding genes, promoters (1 kb upstream of transcripts) and
TSSs (  200 nt) were determined. Within each set, regions with a
non-strand-specific overlap were merged. Effective genome sizes
were calculated for each species. The least frequent repeat sets were
removed from the analysis. Within each repeat set, overlapping
repeats were merged and the central nucleotide considered as repre-
sentative. A binomial test was used to assess an enrichment of
repeats falling within the non-strand-specific, merged promoter
regions. P -values were adjusted according to the Hochberg method.
For comparison of coding to non-coding promoter proportions,
briefly, repeats were divided according to family, subfamily or class.
Overlapping repeats in each set were merged. Expression was deter-
mined as above, and for each cell type, a minimum threshold of
0.25 FPKM was used. Promoters with non-strand-specific overlaps
were merged. These promoter regions representing solely coding or
non-coding genes were retained. Promoter regions overlapping the
repeat sets were counted. Repeat sets with fewer than 50 representa-
tive promoter regions were discarded. A hypergeometric test was
performed to find repeat sets with a non-coding/coding promoter
region enrichment. The mouse ENCODE tissue analysis was
performed by mapping 13 paired-end strand-specific samples to the
mouse genome followed by count quantitation, normalization and
generation of FPKM values where necessary (see Appendix Supple-
mentary Methods).
Evolutionary analysis
Detection of conserved lncRNAs was performed via mapping the
longest isoform from each lncRNA cluster against 15 genome
sequences obtained from Ensembl using WU-BLAST [83] seed
matches followed by realignment as previously published [84]. For
longest ORF analyses, the longest complete ORF was selected for
each transcript cluster. Conservation per nucleotide was calculated
using PhyloP (mm10 genome, 60-way vertebrate alignment, UCSC).
For each transcript, the fraction of nucleotides with positive or nega-
tive PhyloP scores was computed separately (cut-off = 1.301;
P ≤ 0.05) for both the ORF and paired 3 0 UTR.
CAGE analysis of repeat expression
Cap analysis gene expression tags were retrieved from FANTOM5
[57]. rRNA matches were filtered using swan (Kraken), and reads
that map to a repeat masked genome were discarded (Bowtie, up to
3 mismatches). Remaining CAGE tags were mapped to a database of
sequences for individual, non-redundant ERV elements (Bowtie, 2
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mismatches). Read depth was split between multimapped reads.
Both uniquely mapped and multimapped counts were used.
Mass spectrometry
A database for LC-MS/MS search was created from a set of ranked
ORFs selected based on a series of criteria (see Appendix Supplemen-
tary Methods). Spermatocyte and round spermatid cell extract
preparation and digestion were performed [85]. Peptides (200 ng)
were injected on EasySpray 50 cm column (Thermo) connected to
an Orbitrap Fusion Lumos (Thermo). Mascot (Matrix Biosciences)
was used to search for matches with the following settings: MS1
tolerance: 5 ppm, MS2: 0.1Da, max missed cleavages: 2. Samples
were re-analysed with targeted MS, when only ERV-derived peptides
selected from previous experiment were measured. Targeted data
extraction (MS1 filtering and PRM) was performed using Skyline 3.5
[86] with tolerances of dotp > 0.75, idotp > 0.8.
Data access
Primary sequencing data, assembled transcripts and ancillary data
are available via the European Nucleotide Archive (Study:
PRJEB15333 ) and the European Bioinformatics Institute (http://
www.ebi.ac.uk/research/enr ight/testome).
Expanded View for this article is available online.
Acknowledgements
We thank V. Benes, the staff of the EMBL Genome Core facility, for high-
throughput sequencing support and EMBL FACS facility for cell isolation. This
research was primarily supported by grants from the Euro pean Research Coun-
cil (FP 7 / 2007 - 2013 /ERC no. GA 310206 ), the BBSRC (BB/J 01589 X/ 1 ) and EMBL
Core funding. M.P.D. was partly funded by an MRC Me thodology Research
Fellowship (MR/L 012367 / 1 ). G.B. was supported by an EMBL Interdisciplinary
Postdoc (EIPOD) fellowship under Marie Curie Actions Cof und (no. 291772 ). J.R.
was supported by the Wellcome Trust (nos. 1031 39 , 092076 , 108504 ). R.S. The
Wellcome Trust Cent re for Cell Biology is supported by core funding from the
Wellcome Trust ( 092076 /Z/ 10 /Z). R.C.A. is a Wellcome Trust Principal Researc h
Fellow ( 095021 /Z/ 10 /Z). A.S. and A.B. were supported by grants from ERC
(FLAME- 337440 ), ATIP-Avenir and La Fondation Bettencourt Schueller.
Author contributions
MP D le d th e anal y sis of t he d at a an d per f orm ed th e co re co mp ut atio na l an al ys es .
CC co nt ri but ed t o the des ig n an al ysis an d ge ner at io n of the li br ar y/ prep ar at io n
of th e s amp le s. H KS co nt rib ut ed t o th e anal y sis an d the ge ner at io n of t he as sem -
bly . S vD , TL, GB , JMM an d AJE co ntri b ut ed t o the an al ysis . T A per fo r med the mass
sp ec tro me try un de r the gu id ance o f RCA an d JR. AS and AB co ntr ib ut ed to th e
an al ysi s an d pr ov id ed t he z ebra f is h sam ple s . AJE an d DO c on ceiv e d an d
sup er vi sed t hi s stud y. MP D , AJE an d DO wr ote t he fin al v er si on of the ma n us cri pt .
Conflict of interest
The authors declare that they have no conflict of interest.
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Why institutions use Plag.ai for originality review, entry 29

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