Architectural and evolutionary features of TE-derived TSSs shape tissue-specific promoter activity in the human genome

Domain: neuroscience
Published: 2026-01-31
Editorial outcome: major_revision

Abstract
Transposable elements are abundant in the human genome and have been increasingly recognized as sources of alternative promoters. Yet, the extent of their transcriptional activity in human tissues and the features that govern their regulatory potential remain unclear. Here, we integrated high-resolution RAMPAGE data from 115 human biosamples to construct a comprehensive atlas of 26,056 transcription start sites derived from transposable elements. These sites contribute to tissue-specific gene expression, with a notable fraction originating from primate- and hominid-specific elements. Transposable element–derived transcription start sites exhibit focused, narrow-peak architectures enriched for TATA boxes and depleted of CpG islands. Phylogenetic analyses reveal a continuous gradient in promoter strength and transcriptional precision across transposable element subfamilies, with evolutionarily younger elements retaining intrinsic promoter motifs that drive focused and robust transcription, whereas older, more divergent elements exhibit broader initiation patterns and lower intrinsic activity. Together, these findings advance our understanding of how the evolution and preservation of promoter features shape the capacity of transposable elements to be exapted as functional promoters, potentially contributing to lineage-specific regulatory innovation in primates.

Introduction
Transposable elements (TEs) are ubiquitous mobile genetic sequences that collectively constitute a substantial fraction of eukaryotic genomes and have served as pivotal driving forces shaping their architecture and function1,2. Approximately 45% of the human genome derives from multiple TE classes, including long interspersed nuclear elements (LINEs), short interspersed nuclear elements (SINEs), long terminal repeat (LTR) retrotransposons, and DNA transposons3,4. By virtue of their capacity to mobilize, these elements have been co-opted over evolutionary time to drive the emergence of novel regulatory modules, often catalyzing rapid innovation in gene expression programs5. To mitigate the potentially deleterious effects of unchecked transposition, the host genome has evolved multilayered epigenetic defenses, in particular DNA methylation and repressive histone modifications, to silence the vast majority of TE copies in somatic cells6,7. Nevertheless, accumulating evidence indicates that many TEs escape these repressive marks and are repurposed as functional regulatory elements, actively involved in key biological processes such as embryogenesis8,9, immune responses10,11, aging12, and tumorigenesis13,14. In particular, certain TE copies can function as alternative promoters of neighboring genes—initiating transcription within the TE sequence to yield TE-derived transcription start sites (TSSs)—thereby modulating host gene expression9,14,15. In our previous work, we systematically annotated 5768 human TE-derived TSSs from 1205 high-quality RNA-seq datasets spanning multiple tissue and cell types, demonstrating that TE-derived promoters are a widespread phenomenon across diverse biological contexts16.

Unlike autonomous TE transcripts initiated from canonical TE promoters, TE-derived TSSs represent co-opted regulatory elements that function as alternative promoters for nearby host genes5. These elements often harbor multiple transcription factor binding sites and core promoter motifs, enabling recruitment of the transcriptional machinery to initiate transcription of neighboring genes. For example, in lung cancer, an AluJb insertion upstream of the LIN28B oncogene serves as an alternative TSS, driving cancer-specific expression of LIN28B isoforms initiated within the AluJb element14. Similarly, during mouse preimplantation development, a mouse-specific MT2B2-derived TSS produces an N-terminally truncated Cdk2ap1 isoform that predominates in early embryos and enhances cellular proliferation9. Although TE-derived TSSs acting as alternative promoters have been well documented across diverse biological contexts, both the extent to which they contribute to total transcriptional output and the conditions under which they serve as the predominant TSS of a gene remain largely unexplored.

Beyond their context-specific roles, the evolutionary emergence of TE-derived TSSs remains poorly understood. In some cases, particularly within primate-specific ERVL-MaLR elements, the inserted LTR sequences already harbor core promoter motifs, enabling immediate transcriptional initiation upon insertion17. By contrast, other TE insertions may lack obvious promoter signals upon integration; cryptic motifs buried within their sequences can be exposed by alterations in the local chromatin environment, or point mutations may later generate de novo promoter activity18,19. As a result, nascent TE-derived TSSs are initially subject to purifying selection due to their potential to interfere with host gene expression, yet a subset resolves host–element conflicts and, through neutral drift or positive selection, becomes stably incorporated into existing regulatory networks20,21. Moreover, TE families differ widely in insertion age, indicating that both evolutionary timescale and genomic context crucially shape their promoter potential9,22. Importantly, the precise molecular mechanisms by which distinct TE families acquire promoter function, and the conditions under which they become the predominant TSSs of host genes, remain unknown.

In this study, we leveraged high-quality RAMPAGE (RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression) data across 115 human biosamples to curate and characterize 26,056 TE-derived TSSs, covering 87 tissues and 28 cell types, among which testis showed the highest counts and greatest diversity. Functionally, TE-derived TSSs contribute to tissue-specific gene expression, with a notable fraction originating from primate- and hominid-specific elements. Unlike genomic TEs, which are generally depleted from promoter regions, TE-derived TSSs adopt focused, narrow-peak architectures marked by TATA boxes and low CpG density—hallmarks of tissue-specific Pol II promoters. Our phylogenetic analyses reveal that younger TE subfamilies retain intrinsic promoter elements driving precise, high-strength initiation. By contrast, older, more divergent families exhibit dispersed TSS positioning, attenuated intrinsic activity, and partial loss of structural integrity. These findings highlight how preserved core promoter features and evolutionary divergence jointly shape the potential of TEs to be exapted as functional, lineage-specific promoters.

Results
Genome-wide identification of TE-derived TSS with RAMPAGE data
RAMPAGE23 employs paired-end sequencing of 5′-capped RNAs to map TSSs at single-nucleotide resolution and unambiguously link each TSS to its parent transcript. We previously curated high-quality RAMPAGE datasets from 115 biosamples (Supplementary Data 1) to accurately profile promoters of autonomous TE transcripts24 and protein-coding genes25. To systematically identify TE-derived TSSs, we built a three-step pipeline (Fig. 1A) extending our prior methods. First, we applied our previously developed entropy (E) metric24 during peak calling to distinguish true TSS peaks from background noise in repetitive loci (Supplementary Fig. 1A, left), and retained peaks overlapping annotated transposable elements as TE-overlapping TSSs. Next, we filtered these TE-overlapping TSSs by requiring that their paired reads extend into exonic regions of known or de novo assembled transcripts in the matched RNA-seq dataset (Supplementary Fig. 1A, right), ensuring transcriptional connectivity to host genes. Unlike short, unspliced autonomous TE transcripts24, transcripts initiated from TE-derived TSSs are typically spliced into exons, producing RAMPAGE read pairs that span substantially larger genomic distances (Supplementary Fig. 1B). Because RAMPAGE library fragments are typically shorter than 1 kb, read pairs spanning longer distances likely represent spliced transcripts rather than autonomous TE transcripts. We therefore required a minimum genomic span exceeding this fragment length for candidate read pairs. In addition, since over 75% of RAMPAGE read pairs from expressed genes or host genes with annotated TE-derived TSSs exhibited exon-assignment fractions above 0.5 (Supplementary Fig. 1C), we set this value as the threshold to ensure reliable gene association. Together, these criteria effectively enrich bona fide TE-derived TSSs while filtering out autonomous TE transcription.

As illustrated in Fig. 1B, we identified an unannotated alternative TSS for CFAP210 embedded within an intronic LTR52 element; this site exhibits strong H3K4me3 and DNase I hypersensitivity, and its read pairs directly connect the LTR52-derived TSS to downstream CFAP210 exons. When applied genome-wide, we observed that, unlike other TE-overlapping TSSs located far from genes, most TE-derived TSSs map within 1 kb of canonical promoters (Supplementary Fig. 1D). We therefore designate all TE-derived TSSs within this 1 kb window as high-confidence candidates (see Methods).

To evaluate the accuracy and comprehensiveness of TE-derived TSSs identified by our RAMPAGE pipeline, we performed multiple orthogonal validation approaches. First, we confirmed that TE-derived TSSs recapitulate key promoter features. They are strongly enriched for the initiator (Inr) motif at the +1 position (Fig. 1C) and exhibit significantly higher intrinsic transcriptional activity in SuRE assays26 than other TE-overlapping TSSs (Fig. 1D), although their activity is slightly lower than that of canonical TSSs. Luciferase reporter assays, using four randomly selected TE-derived TSSs cloned upstream of a luciferase reporter, showed that all TSSs drove activity significantly above the empty vector control and higher than two previously reported TE-derived TSSs27 (Supplementary Fig. 2A). In contrast, antisense insertion of the same TE-derived sequences markedly reduced luciferase activity, indicating strand-specific promoter activity (Fig. 1E). Expressed TE-derived TSSs also showed markedly stronger DNase I hypersensitivity, RNA Pol II occupancy, and H3K4me3 enrichment at their promoter regions compared to other TE-overlapping TSSs (Supplementary Fig. 2B), consistent with open chromatin and active transcription initiation.

To assess whether TE-derived TSSs give rise to processed transcripts rather than autonomous TE transcription, we examined sequence patterns within the 1 kb downstream region of each TE-derived TSS. These regions recapitulate canonical promoter features characteristic of mRNA TSSs (Fig. 1F), including enrichment of predicted 5′ splice-site motifs and depletion of polyadenylation signals28, suggesting that TE-derived TSSs are likely to initiate processed transcripts. Consistently, a significantly higher proportion of TE-derived TSSs were supported by splice-junction-spanning RAMPAGE read pairs compared with other TE-overlapping TSSs (93.8% versus 1.4%; one-sided Wilcoxon rank-sum test P-value < 2.2 × 10−16; Fig. 1G). Independent de novo assembly of RAMPAGE reads further confirmed that TE-derived TSSs generate genuine, multi-exonic transcripts, whereas assemblies from other TE-overlapping sites remained short and predominantly mono-exonic (Supplementary Figs. 2C, D). Furthermore, 5′ rapid amplification of cDNA ends (5′ RACE) and Sanger sequencing of four randomly selected, previously unannotated TE-derived TSSs in K562 cells confirmed the transcription initiation events identified by our pipeline (Fig. 1H; Supplementary Fig. 2E).

Having confirmed both functional and transcript-level evidence, we next evaluated the reproducibility of TE-derived TSS detection across 50 biosamples with biological replicates. The median Pearson correlation coefficient of TE-derived TSS signal was 0.90 (Supplementary Fig. 2F), demonstrating the strong reproducibility of our RAMPAGE calls.

To assess methodological robustness, we compared our results with TEProF229, an RNA-seq–based approach that identifies TE-derived TSSs through transcript assembly and read assignment. In representative samples including GM12878, K562, heart, liver, and testis, TEProF2 detected far fewer TE-derived TSSs than our RAMPAGE-based pipeline (Supplementary Fig. 3A). Nevertheless, 68–88% of TEProF2-identified sites were recovered by our pipeline, indicating high concordance and demonstrating that RAMPAGE substantially improves sensitivity for promoter detection. We further benchmarked our TE-derived TSSs against other TSS-mapping assays and curated promoter catalogs. A clear majority of TE-derived TSSs overlapped GRO-cap30 and PacBio Iso-Seq31, showing greater concordance than other TE-overlapping TSSs (Supplementary Fig. 3B). Consistent results were obtained when compared with FANTOM CAGE32, RAMPAGE rPeaks25, TE-TSS16, and cCREs-PLS33 (Supplementary Figs. 3C–F). Specifically, 84% of GM12878 and 91% of K562 high-confidence TE-derived TSSs were supported by GRO-cap or Iso-Seq, and an additional 7% and 4% coincided only with existing promoter annotations (Supplementary Fig. 3G). Notably, our pipeline uncovered 46 and 33 novel high-confidence TE-derived TSSs in GM12878 and K562, respectively, all lacking any prior annotation.

Collectively, these results validate the authenticity, robustness, and biological relevance of our TE-derived promoters.

A pan-tissue atlas of human TE-derived TSSs reveals extensive tissue specificity and functional relevance
By applying our pipeline to 115 previously curated high-quality RAMPAGE datasets25 spanning 87 tissues and 28 cell types (Supplementary Data 1), we identified 26,056 TE-derived TSSs expressed in at least one biosample, of which 13,552 met our high-confidence threshold (Fig. 2A). Of all TE-derived TSSs, 15,222 (58%) were detected in only a single biosample, and 7575 (56%) of high-confidence sites were likewise sample-specific. On average, each biosample contained 622 total and 355 high-confidence TE-derived TSSs (Fig. 2A). Within the high-confidence subset, each sample harbored a mean of 274 annotated and 76 novel TSSs, with the novel sites displaying significantly higher expression than annotated ones (median RPM 1.93 vs. 1.00; one-sided Wilcoxon rank-sum test P-value < 2.2 × 10−16; Supplementary Fig. 4A).

Notably, testis samples exhibited the highest number of sample-specific TSSs, accounting for 20% of all and 26% of high-confidence sample-specific sites, underscoring testis as a hotspot for TE-initiated transcription (Fig. 2A).

To assess their genomic origins and potential functional roles, we mapped each TE-derived TSS to its host transcript. Protein-coding genes and long non-coding RNAs (lncRNAs) accounted for 65% and 15% of all TE-derived TSSs, respectively, and for 52% and 19% of high-confidence sites (Fig. 2B; Supplementary Fig. 4B, C). The remaining TSSs mainly mapped to de novo assembled transcripts lacking existing annotation, representing 15% of total and 24% of high-confidence TSSs—a pattern most pronounced in testis. Our prior study using limited RNA-seq data showed that genes with TE-derived TSSs were both highly tissue-specific and enriched among highly expressed genes16. With this expanded atlas, we confirmed that genes with TE-derived TSSs are enriched among highly expressed genes (FPKM ≥ 10), comprising 5% of that group, which represents a ~1.5-fold enrichment relative to all expressed genes (Fig. 2C). Both high-confidence and all TE-derived TSS–associated genes exhibited significantly greater tissue specificity than housekeeping genes (Fig. 2D; one-sided Wilcoxon rank-sum test P-values < 2.2 × 10−16).

Given these gene-level observations, we investigated whether TE-derived TSSs themselves display tissue-specific expression. Clustering a subset of 18 biosamples (nine tissues, two donors) by TE-derived TSS profiles (Supplementary Fig. 4D) revealed tighter correlations within the same tissue (median Jaccard similarity = 0.32) than between different tissues (median Jaccard similarity = 0.09; one-sided Wilcoxon rank-sum test P-value = 1.3 × 10−6). Extending clustering to all 87 biosamples spanning 14 tissues again grouped biologically related tissues together (Fig. 2E; Supplementary Fig. 4E). Finally, Gene Ontology (GO) enrichment of genes harboring expressed TE-derived TSSs uncovered clear, tissue-relevant functions, including synapse part and postsynaptic density in brain, acute inflammatory response and complement activation in lung, contractile fiber and sarcomere in testis, and immune response and vesicle-mediated transport in spleen (Fig. 2E). Together, these results demonstrate that TE-derived TSSs not only expand transcript diversity but also carry strong, tissue-specific functional signals.

Origins and characteristics of transposable elements giving rise to TE-derived TSSs
Previously, we showed that LTR elements are the most enriched source of TE-derived TSSs16. To dissect positional and lineage preferences, we first compared the distributions of TE-derived TSSs and annotated TEs around genes (Supplementary Fig. 5A). Consistent with prior reports that TEs are generally excluded from core promoters due to purifying selection34, genomic TEs are significantly depleted near promoters. In contrast, TE-derived TSSs exhibit strong promoter-proximal enrichment, most prominently in testis samples, suggesting a lineage- and tissue-specific regulatory repurposing of these elements. We then quantified the TE classes contributing to TSSs and found that SINEs, LINEs, and LTRs collectively account for the majority of TE-derived TSSs, with smaller contributions from DNA transposons and SVAs (Fig. 3A). Across most tissues, seven core TE subfamilies—ERV1, Alu, L1, MIR, L2, ERVL, and ERVL-MaLR—are consistently over-represented, with testis engaging the widest variety of TE classes (Supplementary Fig. 5B). Within this core set, Alu and MIR contribute the majority of SINE-derived TSSs; L1 and L2 dominate LINE-derived sites; and LTR-derived TSSs originate predominantly from ERV1, followed by ERVL-MaLR and ERVL, with ERVK contributing the least (Fig. 3A). Finally, comparing TE-derived TSSs to all genomic TEs revealed that only ERV1, MIR, and L2 are significantly over-represented. Conversely, despite their high copy number, Alu and L1 are under-represented relative to the genomic background, whereas all other subfamilies show near-background or modest enrichment levels (Fig. 3B).

These subfamily-level patterns prompted us to ask whether TE-derived TSSs differ in their evolutionary origins and structural properties. To determine evolutionary age, we classified each TE-derived TSS into one of four age categories based on its presence–absence patterns across different mammalian genomes35 (Fig. 3C; see Methods). TSSs present in any non-primate mammalian genome were classified as “Mammals”; those restricted to primates as “Primates”; those shared by Old World Anthropoids as “OWA”; and those unique to the human–great ape lineage as “Hominids”. Unlike canonical TSSs, 93% of which fall into the ancient “Mammals” category, ~52% of TE-derived TSSs are primate-specific (encompassing the “Primates”, “OWA”, or “Hominids” categories), a shift toward recent insertions that echoes TE-driven regulatory innovation in primates10,17. Compared with promoter-proximal TEs, both primate-specific and “hominid” categories are more frequent among TE-derived TSSs. Specifically, 55% of high-confidence and 52% of all TE-derived TSSs fall into the primate-specific category, compared with 48% of promoter-proximal TEs (Chi-squared test P-values < 1.59 × 10−48). Furthermore, 13% of high-confidence and 10% of all TE-derived TSSs belong to the “Hominids” category, a substantial increase compared with only 2% of promoter-proximal TEs (Chi-squared test P-values < 2.2 × 10−16), suggesting recent TE insertions may contribute to lineage-specific regulatory innovation. This primate bias is equally pronounced in testis-specific TE-derived TSSs (Supplementary Fig. 5C).

We then examined the composition of TE subfamilies giving rise to these TSSs (Fig. 3D). Consistent with our age classification, ERV1 elements predominate in the “Hominids” and “OWA” categories, whereas Alu elements are most frequent in the “OWA” and “Primates” categories. In contrast, the ancient families MIR and L2 are largely confined to the “Mammals” category. When compared with promoter-proximal TEs, ERV1 is significantly enriched and Alu significantly depleted in primate-specific lineages, while ERVL and ERVL-MaLR elements show enrichment in non-hominid groups. Notably, the ERV1 enrichment and Alu depletion patterns are further amplified in testis-specific TE-derived TSSs (Supplementary Fig. 5C). To complement the phylogenetic classification, we examined sequence divergence as an orthogonal proxy for evolutionary age, with lower divergence indicating more recent origin. Divergence among TE-derived TSSs is lowest in the “Hominids” category and increases through “OWA”, “Primates”, to “Mammals” categories (Fig. 3E), consistent with younger elements being co-opted more recently36,37. When compared with promoter-proximal TEs, TE-derived TSSs in the “Hominids” and “OWA” categories show reduced sequence divergence, whereas those in the “Primates” and “Mammals” categories display higher divergence. At the subfamily level, Alu, L1, ERV1, ERVL, and ERVL-MaLR elements at TE-derived TSSs are less divergent than their promoter-proximal counterparts, while MIR and L2 elements are more divergent (Fig. 3F). Notably, in testis-specific TE-derived TSSs, this reduction in divergence is significant only for Alu and ERV1 elements (Supplementary Fig. 5D).

Because evolutionary age may influence not only sequence divergence but also copy integrity, we examined the structural completeness of TE copies contributing to TE-derived TSSs (Fig. 3G). In the non-LTR TE families MIR, L1, and L2, 93%, 98%, and 100% of TE-derived TSSs, respectively, derive from truncated copies. By contrast, in the LTR families ERV1, ERVL, and ERVL-MaLR, 53%, 32%, and 54% of TE-derived TSSs originate from full-length insertions. For comparison, only 27% of promoter-proximal ERV1, 14% of ERVL, and 31% of ERVL-MaLR elements are full-length. These data suggest that, although truncated non-LTR fragments can initiate transcription, full-length LTRs are preferentially retained or co-opted as promoters.

To assess whether lineage- and subfamily-specific TE-derived TSSs correspond to functional divergence in host genes, we performed GO enrichment analysis. Genes associated with hominid-specific TE-derived TSSs were enriched for neural and immune programs and for cardiac or muscle-related development (Supplementary Fig. 5E). Primate-specific TE-derived TSSs showed enrichment for immune, developmental, and metabolic processes (Supplementary Fig. 5E). Because ERV1 elements are strongly over-represented among primate-specific TSSs, we further examined genes associated with primate-specific ERV1-derived TSSs and found enrichment for interferon signaling, cell–cell adhesion, long-chain fatty acid metabolism, xenobiotic response, and transposable element silencing (Supplementary Fig. 5F). Together, these results indicate that both lineage origin and TE subfamily composition shape the functional diversification of TE-derived promoters, with primate ERV1 insertions particularly contributing to immune and metabolic regulatory innovation.

Finally, to explore which transcription factors (TFs) are preferentially associated with TE-derived TSSs, we overlapped them with ChIP-seq peaks from 694 TFs curated by the ENCODE Consortium38 (Supplementary Fig. 6A). TE-derived TSSs tend to be bound by a broader repertoire of TFs compared to promoter-proximal TEs (Chi-squared test, p < 2.2 × 10−16; Fig. 3H). Several general TFs—including POLR2A, TBP, TAFs, and YY1—show enrichment across TE-derived TSSs from diverse subfamilies (Fig. 3H). Aggregate ChIP-seq signal profiles in the matched biosample further support stronger binding of these TFs at TE-derived TSSs relative to promoter-proximal TEs (Supplementary Fig. 6B). In contrast, several TFs exhibited subfamily-specific patterns suggestive of preferential TF association, supported by both ChIP-seq signal and motif enrichment (Supplementary Fig. 6C). For instance, ETS1 and ETV5, which recognize canonical ETS motifs39, were preferentially enriched at MIR-derived TSSs. Similarly, NFYA and NFYB, which bind CCAAT boxes40, showed selective enrichment at ERV1- and ERVL-MaLR-derived TSSs. USF1 and USF2, which recognize E-box motifs41, were enriched at ERVL- and ERVL-MaLR-derived TSSs. Together, these results suggest that specific TE subfamilies may retain recognizable TF motifs that could facilitate subfamily-specific regulatory activity. Notably, TF binding patterns at TE-derived TSSs appear distinct from those governing autonomous TE transcription. For example, we previously showed that AP-1 family members, such as FOS and JUN, preferentially bind Pol III-transcribed Alu elements to promote their expression24. However, this enrichment was absent from Alu-derived TSSs (Supplementary Fig. 6D), which is suggestive of a transition from internal TE-driven transcription to host-mediated co-option of TE fragments as regulatory modules.

TE-derived TSSs are enriched for narrow-peak promoter architectures and contribute to gene expression
RAMPAGE enables single-nucleotide resolution mapping of transcription start sites for both autonomous TE transcripts24 and protein-coding genes25, making it ideally suited for quantifying promoter usage across all TSSs of a gene. To confirm the accuracy of our TE-derived TSS annotations, we overlapped our annotated TE-derived TSSs with RAMPAGE peaks from K562 and GM12878 and found that nearly all peaks fell within ±20 nt of their corresponding TE-derived TSSs (Supplementary Fig. 7A; permutation test P-values < 1 × 10−4). Consistently, RAMPAGE peak summits at TE-derived TSSs show variability comparable to that of mRNA TSSs and are significantly less variable than those at other TE-overlapping TSSs (Supplementary Fig. 7B). We next asked whether these sites conform to established Pol II promoter architectures. Human Pol II promoters are known to adopt either a narrow shape, characterized by a focused TSS, a TATA box ~30 nt upstream, and low CpG density, typically associated with tissue-specific expression, or a broad shape, in which initiation is dispersed, CpG density is high, and TATA boxes are absent, generally driving ubiquitous transcription42. We therefore classified all TSSs by RAMPAGE peak width into narrow (≤10 bp) and broad (>10 bp) categories (Fig. 4A). In comparison to canonical TSSs, TE-derived TSSs are more than twice as likely to adopt a narrow peak architecture (32% versus 15%; one-sided Wilcoxon rank-sum test P-values < 2.2 × 10−16; Fig. 4A), and these narrow peaks exhibit significantly less width variability across biosamples than broad peaks (Supplementary Fig. 7C), indicating that their promoter shape is consistently maintained under diverse conditions.

To determine whether these narrow-peak sites also bear the sequence hallmarks of focused Pol II promoters, we assessed TATA box presence and CpG context. TE-derived TSSs are 1.5-fold more likely than canonical TSSs to contain a TATA box (Chi-squared test P-values < 2.30 × 10−48; Fig. 4B), with narrow-peak TE-derived TSSs harboring the motif at –25 to –30 nt (Supplementary Fig. 7D), just as in typical Pol II promoters. By contrast, Pol III-transcribed Alu elements position their TATA boxes at –15 nt (Supplementary Fig. 7D), reflecting differences in the underlying transcriptional machinery. Additionally, over 93% of TE-derived TSSs lack upstream CpG islands compared with 60% of expressed canonical TSSs (Chi-squared test P-values < 2.2 × 10−16; Fig. 4B), and TE-derived TSSs display markedly lower GC content than their canonical counterparts (one-sided Wilcoxon rank-sum test P-values < 2.2 × 10−16; Supplementary Fig. 7E). Importantly, TE-derived TSSs that contain a TATA box almost always display narrow peaks, whereas those with upstream CpG islands consistently fall into the broad-peak category (Fig. 4B). Together, these observations demonstrate that, relative to canonical TSSs, TE-derived TSSs are enriched for narrow peaks and adopt the TATA-enriched, CpG-poor architecture characteristic of tissue-specific Pol II promoters.

We then quantified TE-derived TSS counts per gene and found that ~85.6% of genes possess a single TE-derived TSS, 1.1% harbor two, and just 0.3% carry three or more, with testis samples exhibiting elevated per-gene counts (Fig. 4C; Supplementary Fig. 8A). To assess how much these sites actually contribute to gene output, we defined promoter usage as the fraction of RAMPAGE tags at each TE-derived TSS divided by the total tags across all TSSs of that gene. More than 25% of genes with TE-derived TSSs obtain at least half of their promoter activity from these sites; when lowering the usage threshold to 10%, roughly half of such genes meet the criterion, indicating a substantial contribution of TE-derived TSSs to overall gene expression (Fig. 4D).

To dissect their functional importance, we categorized TE-derived TSSs by usage as unique (the sole TSS of the gene), predominant (the highest usage among all TSSs), or non-predominant (all others). Although non-predominant sites constitute the majority, roughly 25% are unique and 11% predominant (Fig. 4E; Supplementary Fig. 8B). Predominant TSSs typically account for at least 50% of promoter usage, and even non-predominant sites frequently contribute more than 5% (Supplementary Fig. 8C). In support of their functional impact, unique and predominant TSSs yield significantly stronger SuRE signals than non-predominant sites (Fig. 4F).

Finally, we examined the evolutionary origin and TE family bias behind these classes. Unique and predominant TE-derived TSSs are significantly enriched for primate-specific and hominid insertions (Fig. 4G), both at the TSS (Chi-squared test P-values < 2.2 × 10−16) and host gene levels (Chi-squared test P-values < 2.2 × 10−16), suggesting that recent TE integrations have driven lineage-specific regulatory innovation. Subfamily analysis further reveals that these sites most often arise from L1 and ERV elements within primate lineages and are depleted of the older MIR and L2 families typical of mammalian genomes (Supplementary Fig. 8D). Correspondingly, they exhibit lower sequence divergence in Alu, L1, and select ERV subfamilies compared with non-predominant sites (Supplementary Fig. 8E), consistent with their more recent origin and retention of intact promoter features43.

Young TE-derived TSSs preserve intrinsic promoter architecture to drive precise transcription initiation
Having shown that TE-derived TSSs differ in both transcriptional activity and evolutionary divergence, we next asked whether these differences stem from preserved, intrinsic TE promoter elements or from post-integration, host-driven regulation. To estimate evolutionary age, we used the percent divergence of each TE copy from its consensus sequence as a proxy for time since insertion. If intrinsic promoter strength were preserved, it should manifest as higher SuRE activity. Indeed, SuRE signals increase stepwise from the oldest to the youngest TE lineages, indicating that lower sequence divergence is linked to stronger intrinsic promoter activity (Supplementary Fig. 9A). Highly diverged MIR and L2 families exhibit weak SuRE activity, whereas low-divergence ERV1 elements show the strongest signals (Fig. 5A; Supplementary Fig. 9B).

To directly assess whether transcription initiation occurs from conserved positions within TE sequences, we aligned each TE-derived TSS to the consensus sequence of its subfamily and calculated the standard deviation of its relative position across genomic copies. Within the Alu, L1, and ERVL–MaLR families, subfamilies with lower sequence divergence exhibited tightly clustered TSS positions, while those with higher divergence showed broader, more dispersed TSS distributions (Fig. 5A).

Subdivision of the Alu family illustrates this gradual pattern clearly (Fig. 5B). AluY, the youngest subfamily specific to hominids44, displayed highly focal TSS positioning and the strongest SuRE signals. In contrast, the more ancient AluS and AluJ subfamilies exhibited progressively more dispersed TSS positioning and reduced SuRE activity. A similar divergence-associated hierarchy was observed in the L1 family (Fig. 5B). The human-specific L1HS subgroup retained narrow, consistent TSS placement and high SuRE activity, whereas the older L1P (primate-specific) and L1M (mammalian-wide) subfamilies showed increasingly dispersed TSSs and attenuated SuRE signals. The ERVL–MaLR superfamily followed the same evolutionary trajectory (Supplementary Fig. 9C). THE1, largely confined to primates45, exhibited focused TSS placement and robust SuRE reporter activity, whereas the older MST and MLT1 subfamilies, present across mammals, showed broader TSS distributions and weaker SuRE output.

Together, these findings indicate that promoter precision and activity form a continuous evolutionary gradient that parallels TE sequence divergence. Elements with low divergence preserve their intrinsic promoter architecture and drive strong, well-defined initiation, while those that have accumulated substitutions and deletions exhibit dispersed transcription initiation and increasing dependence on host regulatory environments.

Methods
Computational pipeline to annotate TE-derived TSSs using RAMPAGE data
RAMPAGE peaks were first called and filtered (left panel in Supplementary Fig. 1A) as previously described24,25. Briefly, we excluded RAMPAGE datasets with non-redundancy fraction < 0.25 and retained 115 RAMPAGE datasets as high-quality datasets (Supplementary Data 1). BAM files for these datasets, aligned to the GRCh38/hg38 reference human genome using STAR55 based on the ENCODE RAMPAGE Processing Pipeline (https://www.encodeproject.org/data-standards/rampage), were downloaded from the ENCODE Portal (https://www.encodeproject.org). Properly aligned read pairs (R1 and R2 denote each mate of the read pair) with uniquely aligned R2 reads were collapsed with the same alignment coordinates and the identical 15-bp barcode at the 5´-end of R2 reads to remove PCR duplicates. Read pairs from biological replicates were pooled together after the PCR duplicate removal. RAMPAGE peaks were clustered with the 5´-most base of aligned R1 reads using F-seq56 (parameter: feature length=30 and fragment size=0), and resized to the length that included 70% of the reads in each original peak to eliminate lengthy tails. RAMPAGE peaks were then filtered with entropy (E) ≥ 2.5 based on the coordinates of R2 reads in each peak24. Peaks overlapping transposon elements (hg38 rmsk.txt downloaded from UCSC) were defined as TE-overlapping TSSs.

With downstream R2 reads, RAMPAGE peaks were assigned to annotated genes from GENCODE (V29) or RefSeq (Curated, updated on 07 April 2018) and de novo assembled transcripts (StringTie57 with default parameters) from matched RNA-seq data (Supplementary Data 1). Candidate TE-derived TSSs (right panel in Supplementary Fig. 1A; Supplementary Data 2) were selected if (i) paired RAMPAGE reads linked the peak to exonic regions of annotated or de novo transcripts, (ii) the genomic span of the read pair was > 1 kb, and (iii) the exon-assignment fraction ≥ 0.5 (Supplementary Fig. 1B, C). Sites located within 1 kb of annotated GENCODE/RefSeq TSSs or de novo RNA-seq–assembled TSSs were classified as high-confidence TE-derived TSSs; remaining sites were labeled RAMPAGE-only (Supplementary Fig. 1D).

Comparison of TE-derived TSS with other TSS annotations and RNA-seq–based detection method
TSS annotations defined using GRO-cap peaks30 (Supplementary Fig. 3B), PacBio Iso-Seq31 (Supplementary Fig. 3B), FANTOM CAGE32 (Supplementary Fig. 3C), RAMPAGE rPeaks16,25 (Supplementary Fig. 3D), TE-TSS database16 (Supplementary Fig. 3E), and cCREs-PLS V433 (Supplementary Fig. 3F) were downloaded and processed, respectively, and overlapped with TE-derived TSSs in corresponding cell types. To be specific, GRO-cap peaks in GM12878 and K562 cells (GEO: GSE60456) were aligned to GRCh38/hg38 reference human genome using STAR with default parameters and called using F-seq (parameter: feature length=30 and fragment size=0). Assembled isoforms of PacBio ISO-Seq in GM12878 and K562 cells processed using TALON58 were downloaded from the ENCODE Portal (https://www.encodeproject.org), and the 5´-end of assembled isoforms were regarded as the TSSs. CAGE peaks called for GRCh38/hg38 reference human genome by FANTOM Consortium were downloaded from FANTOM5 data website (https://fantom.gsc.riken.jp/5/datafiles/reprocessed). RAMPAGE rPeaks were processed according to the rPeak pipeline using 115 RAMPAGE biosamples. TE-derived TSS annotations were downloaded from the TE-TSS database (http://xozhanglab.com/TETSS). The ENCODE cell type-agnostic candidate Cis-Regulatory Elements with promoter-like signatures (cCREs-PLS) were downloaded from the SCREEN resource (http://screen.encodeproject.org). In addition, to identify TE-derived transcripts directly from RNA-seq data, we employed TEProF2 using matched RNA-seq datasets. A TE-derived transcript was considered present if it showed an expression level ≥ 1 TPM and at least one unique read spanning the junction between the TE and the host transcript, as previously described29.

Mapping and normalization of SuRE data
Raw SuRE reads (iPCR, cDNA and plasmid reads) were downloaded from GSE78709 and processed as previously reported26, but remapped to the GRCh38/hg38 reference human genome. Reads of iPCR are combinations of unique barcode sequence and respective genomic DNA sequence for each SuRE fragment, and reads of cDNA and plasmid only record counts of barcode sequences in the SuRE library and the “input” library, respectively. Paired-end reads of iPCR and single-end reads of cDNA and plasmid were processed to remove adapter and vector backbone sequences using cutadapt. To associate barcode sequences with genomic coordinates, trimmed read pairs of iPCR were aligned to the GRCh38/hg38 reference human genome using Bowtie259 permitting a maximum insert length of 4 kb. For each SuRE fragment, cDNA and plasmid read counts from biological replicates were aggregated, and cDNA counts were normalized by plasmid counts (“input”) to obtain the SuRE enrichment profiles.

Tissue specificity analysis
We evaluated the tissue specificity of genes harboring TE-derived TSSs using a previously defined tissue specificity score60, calculated as:
Tissue specificity score = (P(1-x_i))/(N-1)
where N denotes the total number of biosamples and x_i represents the expression level in biosample i, normalized by the maximal expression value across all biosamples. This tissue specificity index ranges from 0 to 1, with higher values indicating greater tissue specificity.

Enrichment analysis of TE subfamilies and transcription factors for TE-derived TSSs
To check which TE subfamilies contribute to TE-derived TSSs, we calculated the fold enrichment for each TE subfamily in the formation of TE-derived TSSs over the genomic background. Because TE-derived TSSs are specifically enriched near TSSs (Supplementary Fig. 5A), fold enrichments were also normalized by promoter-proximal TEs (TEs in TSS ± 1 kb). To calculate FDRs, P-values from hypergeometric test and binomial test were combined by Stouffer’s Z-score method, and combined P-values were corrected using the Benjamini–Hochberg procedure.

ChIP-seq peaks of TFs from the ENCODE Consortium were downloaded from Factorbook38 (https://www.factorbook.org). Significant ChIP-seq peaks (FDR < 0.05) overlapping with TEs of TE-derived TSSs were counted, and promoter-proximal TEs (TEs in TSS ± 1 kb) were collected as the genomic control. Enrichment P-values were calculated using Fisher’s exact test, and FDR correction with the Benjamini–Hochberg procedure was used to select enriched transcription factors.

To evaluate enrichment of specific transcription factor motifs at TE-derived TSSs, we applied the AME algorithm61 from the MEME Suite to scan curated motifs from the Factorbook database38. Motif occurrences were quantified in both TE-derived TSS sequences and promoter-proximal TE sequences, and the enrichment was determined by comparing their frequencies between these two sets.

Evolutionary analysis of TE-derived TSSs and host genes
To categorize TE-derived TSSs by evolutionary age, TE-derived TSSs were aligned to 16 other genomes using the liftOver tool with whole-genome alignment-chain files downloaded from the UCSC. To reduce potential false positives resulting from alignments of paralogous loci between two genomes, a minimum liftOver mapping ratio of 90% (-minMatch=0.9) was required for TE-derived TSSs (TSS ± 10 bp) and a minimum liftOver mapping ratio of 50% (-minMatch=0.5) was needed for TE-derived TSS surrounding regions (TSS ± 100 bp), and we also required a minimum alignment chain size of 10 kb for both target and query genomes (-minChainT=10000 -minChainQ=10000 -multiple). TE-derived TSSs that could not be lifted over to another genome were considered missing in that genome. Evolutionary categories and the mammalian phylogeny were defined as previously reported35. According to the presence or absence patterns in specific genomes, TE-derived TSSs were classified into four categories of different evolutionary ages (Fig. 3C; Supplementary Data 3): 1. TSSs whose sequences could be lifted over to at least one non-primate genome were categorized into the “Mammals” category; 2. TSSs whose sequences occurred during early primate evolution but before the last common ancestor of Old World Anthropoids (OWA) were categorized into the “Primates” category; 3. TSSs whose sequences occurred during the evolution of OWA but before the last common ancestor of hominids were categorized into the “OWA” category; 4. TSSs whose sequences occurred since the emergence of hominids were categorized into the “Hominids” category.

Because TE-derived TSSs were enriched close to known TSSs (Supplementary Fig. 5A), we selected GENCODE TSSs and nearby TEs (TSS ± 1 kb, promoter-proximal TEs) for comparison. For GENCODE TSSs, the same criteria as TE-derived TSSs were used. For nearby TEs, we required a minimum liftOver mapping ratio of 50% (-minMatch=0.5) and a minimum alignment chain size of 10 kb for both target and query genomes (-minChainT=10000 -minChainQ=10000 -multiple). We used the same method as TE-derived TSSs to cluster GENCODE TSSs and promoter-proximal TEs into corresponding evolutionary categories.

To estimate the evolutionary age of host genes, we applied the same liftOver-based alignment strategy across the 16 mammalian genomes as described above. Assembled gene sequences of TE-derived TSS were lifted over to each target genome using liftOver with a minimum nucleotide mapping ratio of 80% (–minMatch=0.8), a minimum exon alignment ratio of 50% (–minBlocks=0.5), and allowance for multiple mappings (–multiple). Genes failing to meet these criteria in a given genome were considered absent. Based on their presence–absence patterns, each host gene was then assigned to one of four evolutionary age categories (mammals, primates, OWA or hominid).

Characterization of promoter features for TE-derived TSSs
TE-derived TSSs were classified into narrow peaks (size ≤ 10 bp) and broad peaks (size > 10 bp) according to the sizes of RAMPAGE peaks (Fig. 4A). To test whether a TE-derived TSS contained a TATA-box, we scanned the binding motif of TBP (MA0108.2 in JASPAR database62) within 25–35 bp upstream of the summit of TE-derived TSS RAMPAGE peaks using FIMO63 (parameter: --thresh 1e-2). TE-derived TSSs were overlapped with CpG island annotations (hg38 cpgIslandExtUnmasked.txt downloaded from UCSC) to determine whether one TE-derived TSS is located within a CpG island.

Luciferase reporter assay
Luciferase reporter assay was performed as previously described with some modifications27. Sequences of TE-derived TSSs and their antisense counterparts were cloned into pGL3-basic luciferase reporter plasmids. 293FT cells were co-transfected with the luciferase reporter plasmid, a FLAG expression plasmid, and the pRL-TK Renilla luciferase plasmid using Lipo8000 (Beyotime, C0533). After 24 h of transfection, cells were lysed and luciferase activity was measured using the Dual Luciferase Reporter Assay Kit (Vazyme, DL101). Relative luciferase signal was normalized to Renilla luciferase activity for each sample. Primers used for reporter vector construction are listed in Supplementary Data 4.

Cell culture and experimental validation of transcripts with TE-derived TSSs
K562 cells were cultured at 37 °C with 5% CO2 in complete medium. Total RNA was isolated using TRIzol. TE-derived TSSs were validated by 5′ RACE using the HiScript-TS 5′/3′ RACE Kit (Vazyme, RA101), and the resulting amplified fragments were confirmed by Sanger sequencing. Gene-specific primers (GSPs) for 5′ RACE were designed using the Vazyme GSP primer design website (http://appbi.vazyme.com:8085/) and are listed in Supplementary Data 4.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I co-reviewed this manuscript with an early-career researcher. This is part of the Nature Communications initiative to facilitate training in peer review. We first did a full review independently and after discussion I merged our reports with ChatGPT 5. We then both proof-checked the merged report, which is pasted below.\nCharles Plessy\nSummary\n-------\nThe manuscript by Zhang and Zhang presents a comprehensive atlas of more than 26,000 transposable element (TE)–derived transcription start sites (TSSs) across 115 human biosamples, generated using high-resolution RAMPAGE data.\nThe authors show that TE-derived TSSs are crucial for tissue-specific gene expression and often serve as unique promoters for host genes, including some missing from GENCODE due to their TE origin. The study extends previous work (Gu et al., 2024; Miao et al., 2020) with new data and evolutionary analyses, offering a resource for future studies of promoter evolution and TE biology. Overall, this atlas has the potential to serve as a solid foundation for genome-wide studies of TE contributions to gene regulation and evolution.\nMajor Points\n------------\n* High-confidence subset definition\n- Many figures compare high-confidence versus all TE-derived TSSs against controls, yet they often look very similar. This raises questions about the added value of the high-confidence set. Consider contrasting high-confidence versus non-high-confidence directly.\n- The thresholds for defining high-confidence TSSs (distance to other promoters, exon assignment fraction, gene length) are critical for readers to evaluate the subset’s quality. Supplemental Figure S1 panels on these thresholds should be moved to the main text. Please also clarify whether the three thresholding dimensions are correlated.\n* Clarification of results and methods\n- In the sentence about “roughly half of TE-derived TSSs map within 1 kb”, why is this only half? Is it due to RAMPAGE providing higher resolution than current annotations?\n- Please elaborate in the Methods how the tissue-specificity score was calculated (Figure 2D).\n* Evolutionary and functional implications\n- Are there any differences in the function of target genes of TE-derived TSS that are exclusive in: hominids, hominids and primates, as compared to other mammals? If gene expression data is available, do orthologous genes exhibit different gene expression patterns due to these differences in promoters?\n- Similarly, there is a difference in composition of TE subfamilies across different clades (Figure 3C), could this be linked to functional differences in target genes?\n* Figures\n- Consider separating Figure 3A into separate figures (or possibly removing the inset as can be represented by the “main” plot). Further, the numbers in the inset are different from the sum of the numbers at the “main” plot (e.g. SINE is 190 while Alu + MIR is 179). If the rest are other subfamilies, consider adding another boxplot, and thus the small inset could be omitted. The plot at the bottom can be separated into another plot as well (e.g. Figure 3B).\n- Supplemental Figure S1C: color coding for cell lines is inconsistent with main-text figures, and the density curves have little vertical resolution. Consider alternative plots.\n- Supplemental Figure S1D: the intron-exon diagram is misleading; log-log scaling might better illustrate distance thresholds.\nMinor Points\n------------\n- Use “RNA Pol II” instead of “POLR2A” (the latter refers to the gene rather than the protein).\n- In the sentence \"Next, we evaluated sequence patterns within the 1 kb downstream region of each TSS, in which canonical promoters display both enrichment of predicted 5′ splice-site motifs and depletion of polyadenylation signals\", the correct reference is Almada et al., Nature 2013.\n- Add “exon” before “assignment fraction” in all instances for clarity.\n- Check spelling, for example “FAMTOM” instead of “FANTOM.”\n- Figure 1E: the “TSS” acronym near the poly(A) motif may be confused with “transcription stop site.” Please clarify.\n- Figure 2A: the shape coding for sample type (tissue, primary cell, cell line) is hard to read; simplify or enhance.\n- Figures 4C,E: the “・Testis” labels are misaligned.\n- The sentence starting with “To distinguish TE-derived TSSs from other TE-overlapping TSSs…” is unclear. Please rephrase.\n- Some figure references seem mismatched (e.g., “Figure 1E,F” should be “1F,G”).\n- “TE-derived” is used throughout, but “TE-initiated” appears twice — please clarify or standardize.\n- Please add line numbers for ease of review.\n(Remarks on code availability)\nThe GitHub repository (https://github.com/kepbod/rampage_te_tss) currently provides only a subset of the code with minimal documentation. At a minimum, please improve documentation. Ideally, include scripts used to generate figures presented in the manuscript.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n(Remarks on code availability)", "reviewer_3": "This manuscript presents a comprehensive analysis of transcription start sites (TSSs) associated with transposable elements (TEs) in the human genome, primarily using RAMPAGE data. The topic is timely, relevant, and conceptually strong, as it addresses how repetitive sequences are exapted into regulatory functions. The work succeeds in assembling a large dataset and provides descriptive observations about tissue-restricted TSS usage and differences between younger and older TE subfamilies.\nHowever, in its current form the study remains primarily descriptive and heavily dependent on a single detection pipeline. It does not incorporate alternative computational tools (e.g., ChimeraTE, LIONS, TEProf) or sample-matched validation data, which weakens confidence in the generality of the conclusions. Some figures are overly dense, and several claims could be more cautiously phrased to avoid overstating the impact. Overall, while the dataset has value, the manuscript requires substantial revisions to meet the standards of Nature Communications.\nSpecific points\n1. Ambiguity in terminology: The use of “TE-overlapping TSSs” vs “TE-derived TSSs” is inconsistent and confusing. Clear definitions early in the text (or a glossary schematic) are needed.\n2. Dataset metadata: Table S1 lacks essential sample information (tissue type, donor demographics, sequencing depth, QC metrics, accession links). Without these, readers cannot properly assess dataset quality or representativeness.\n3. Methodological dependence on a single pipeline. The entire study relies on one RAMPAGE-based detection pipeline. Given the diversity of available TE-derived TSS detection methods (e.g. ChimeraTE, LIONS, TEProf), the conclusions would be more robust if cross-validated with at least one complementary approach. This would help address potential biases or artifacts unique to the chosen pipeline.\n4. Overcrowded pipeline figures: Figures 1 and Sup. Fig. 1 attempt to show too many methodological details at once. Simplifying the pipeline schematic and moving QC checks to supplementary material would improve readability.\n5. Validation strategy is limited. The validation analyses are restricted to GM12878 and K562, which are convenient but atypical immortalized cell lines. Their regulatory landscapes may not reflect primary tissues. The authors should either (a) incorporate validation in at least one additional primary tissue, or (b) more explicitly acknowledge this limitation.\n6. Tissue specificity claims: The manuscript interprets rare, single-tissue occurrences as evidence of “extensive tissue specificity.” Replicate consistency and prevalence across donors should be assessed; otherwise, the claim should be moderated.\n7. Contribution to gene expression: The heading “contribute substantially” is not well supported - TE-derived promoters account for ~5% of highly expressed genes, which is modest. A more cautious framing is needed.\n8. Clarity of age classification. The “young” versus “old” TE categories need clearer operational definitions. For example, are age estimates based on divergence from consensus sequences, comparative genomics, or other metrics? Without transparent criteria, it is difficult to evaluate the conclusions about “preserved” promoter motifs.\n9. Reliance on unmatched TF ChIP-seq: TF binding is inferred from datasets not matched to the RAMPAGE biosamples. Given context-specific binding, these analyses should be presented as suggestive rather than direct evidence.\n10. Young vs. old TE promoters: The contrast between precise “young” and dispersed “old” promoters may be overstated. TE “age” criteria should be clarified, and the pattern reframed as a continuum rather than a strict dichotomy.\n(Remarks on code availability)"}, "author_response": "Point-by-Point Responses to Reviewers:\nReviewer #1 (Remarks to the Author)\nI co-reviewed this manuscript with an early-career researcher. This is part of the Nature Communications initiative to facilitate training in peer review. We first did a full review independently and after discussion I merged our reports with ChatGPT 5. We then both proof-checked the merged report, which is pasted below.\nCharles Plessy\nSummary\n-------\nThe manuscript by Zhang and Zhang presents a comprehensive atlas of more than 26,000 transposable element (TE)–derived transcription start sites (TSSs) across 115 human biosamples, generated using high-resolution RAMPAGE data. The authors show that TE-derived TSSs are crucial for tissue-specific gene expression and often serve as unique promoters for host genes, including some missing from GENCODE due to their TE origin. The study extends previous work (Gu et al., 2024; Miao et al., 2020) with new data and evolutionary analyses, offering a resource for future studies of promoter evolution and TE biology. Overall, this atlas has the potential to serve as a solid foundation for genome-wide studies of TE contributions to gene regulation and evolution.\nResponse: We thank the reviewer for the positive and insightful summary of our study.\nMajor Points\n------------\n* High-confidence subset definition\nR1.1 Many figures compare high-confidence versus all TE-derived TSSs against controls, yet they often look very similar. This raises questions about the added value of the high-confidence set. Consider contrasting high-confidence versus non-high-confidence directly.\nResponse: We thank the reviewer for this helpful suggestion. In response to this comment, we directly compared high-confidence and non-high-confidence (RAMPAGE-only) TE-derived TSSs. The non-high-confidence set resembled high-confidence ones in several core promoter features: they exhibited a clear Inr motif (revised Figure 1C), characteristic downstream sequence context with enrichment of 5′ splice-site motifs and depletion of polyadenylation signals (revised Figure 1E), and generate predominantly multi-exonic transcript isoforms (revised Supplemental Figures 2J,K). Although non-high-confidence TE-derived TSSs show reduced autonomous transcriptional activity in SuRE assays (revised Figure 1D) and less overlap with other TSS-mapping assays and curated promoter catalogs compared to high-confidence sites (revised Supplemental Figures S2D–H), they remain significantly more active and promoter-like than other TE-overlapping TSSs. These results indicate that the high-confidence set is useful when stringent support is required, while the non-high-confidence set captures additional bona fide TE-derived TSSs and helps extend the overall catalog. We have incorporated these comparisons into the revised manuscript.\nR1.2 The thresholds for defining high-confidence TSSs (distance to other promoters, exon assignment fraction, gene length) are critical for readers to evaluate the subset’s quality. Supplemental Figure S1 panels on these thresholds should be moved to the main text. Please also clarify whether the three thresholding dimensions are correlated.\nResponse: We thank the reviewer for this insightful comment. We would like to clarify that the classification of high-confidence versus non-high-confidence TE-derived TSSs is based solely on their distance to other promoters, whereas the exon-assignment fraction and genomic span are general parameters used to define TE-derived TSSs. Following your suggestion, we have moved the discussion of these thresholds to the main text for greater clarity.\nBy comparing these three thresholding dimensions among mRNA TSSs, autonomous TE TSSs, and annotated TE-derived TSSs, we show that these parameters provide clear separation and effectively support the identification of both TE-derived and high-confidence TE-derived TSSs (revised Supplemental Figures S1B-D). We further note that the exon-assignment fraction and genomic span are largely uncorrelated across samples (Spearman’s ρ ranging from –0.20 to –0.10), indicating that they capture distinct transcript features rather than redundant information.\n* Clarification of results and methods\nR1.3 In the sentence about “roughly half of TE-derived TSSs map within 1 kb”, why is this only half? Is it due to RAMPAGE providing higher resolution than current annotations?\nResponse: We thank the reviewer for this question. The observation that roughly half of TE-derived TSSs map within 1 kb of annotated promoters reflects both the higher resolution of RAMPAGE and the presence of genuinely novel TE-driven initiation events. Because RAMPAGE captures precise transcription start sites at single-nucleotide resolution (Supplemental Figure S6A), some TE-derived TSSs show small positional shifts relative to existing annotations. Meanwhile, a substantial fraction of TE-derived TSSs occur in regions without any annotated promoters (Supplemental Figure S3A), consistent with exaptation of TEs as new transcription initiation sites. Thus, the “half within 1 kb” pattern arises from a combination of annotation differences and true novel promoter activity.\nR1.4 Please elaborate in the Methods how the tissue-specificity score was calculated (Figure 2D).\nResponse: We thank the reviewer for the helpful suggestion. We have now added a detailed description of the calculation method for the tissue-specificity score in the revised Methods section under “Tissue specificity analysis.”\n* Evolutionary and functional implications\nR1.5 Are there any differences in the function of target genes of TE-derived TSS that are exclusive in: hominids, hominids and primates, as compared to other mammals? If gene expression data is available, do orthologous genes exhibit different gene expression patterns due to these differences in promoters?\nResponse: We thank the reviewer for this insightful comment. We analyzed lineage-restricted TE-derived TSSs and found that hominid-specific sites are preferentially associated with genes involved in neural, immune, and cardiac/muscle development, whereas primate-specific sites are enriched for immune, developmental, and metabolic processes. These enrichments suggest potential functional specialization linked to recent regulatory innovations. Although we have not yet performed a systematic cross-species expression comparison for orthologous genes, due to the limited availability of matched datasets, we recognize this as an important direction for future work. The corresponding results and clarifications have been incorporated into the revised manuscript.\n“To assess whether lineage- and subfamily-specific TE-derived TSSs correspond to functional divergence in host genes, we performed GO enrichment analysis. Genes associated with hominid-specific TE-derived TSSs were enriched for neural and immune programs and for cardiac or muscle-related development (Supplemental Figure S4E). Primate-specific TE-derived TSSs showed enrichment for immune, developmental, and metabolic processes (Supplemental Figure S4E).”\nR1.6 Similarly, there is a difference in composition of TE subfamilies across different clades (Figure 3C), could this be linked to functional differences in target genes?\nResponse: We thank the reviewer for this constructive point. Indeed, TE subfamily composition differs markedly across evolutionary clades, and this divergence appears to parallel distinct functional enrichments of their target genes. Because ERV1 elements are strongly over-represented among primate-specific TE-derived TSSs and account for the majority of lineage-restricted promoters in this group, we further examined this subfamily as a representative example. We found that genes associated with primate-specific ERV1-derived TSSs were enriched for interferon signaling, cell adhesion, long-chain fatty acid metabolism, xenobiotic response, and transposable element silencing. These results suggest that evolutionary turnover of TE subfamilies has contributed to lineage-specific diversification of regulatory functions through recruitment of distinct promoter types. This addition has been incorporated into the revised manuscript.\n“Because ERV1 elements are strongly over-represented among primate-specific TSSs, we further examined genes associated with primate-specific ERV1-derived TSSs and found enrichment for interferon signaling, cell–cell adhesion, long-chain fatty acid metabolism, xenobiotic response, and transposable element silencing (Supplemental Figure S4F).”\n* Figures\nR1.7 Consider separating Figure 3A into separate figures (or possibly removing the inset as can be represented by the “main” plot). Further, the numbers in the inset are different from the sum of the numbers at the “main” plot (e.g. SINE is 190 while Alu + MIR is 179). If the rest are other subfamilies, consider adding another boxplot, and thus the small inset could be omitted. The plot at the bottom can be separated into another plot as well (e.g. Figure 3B).\nResponse: We thank the reviewer for the constructive suggestion. In the revised version, we have incorporated the other subfamilies into the main panel and removed the small inset accordingly.\nAdditionally, the plot previously shown at the bottom has been moved to a new panel (revised Figure 3B) to improve clarity.\nR1.8 Supplemental Figure S1C: color coding for cell lines is inconsistent with main-text figures, and the density curves have little vertical resolution. Consider alternative plots.\nResponse: We thank the reviewer for the suggestion. In the revised Supplemental Figure S1C, we have unified the color scheme to match the main-text figures: red denotes host genes with TE-derived TSSs, and gray denotes expressed genes. Additionally, we replaced the density curves with boxplots to improve vertical resolution and visual clarity.\nR1.9 Supplemental Figure S1D: the intron-exon diagram is misleading; log-log scaling might better illustrate distance thresholds.\nResponse: We thank the reviewer for the suggestion. In the revised Supplemental Figure S1D, we removed the intron-exon diagram and applied log–log scaling to better illustrate distance distributions and thresholds.\nMinor Points\n------------\nR1.10 Use “RNA Pol II” instead of “POLR2A” (the latter refers to the gene rather than the protein).\nResponse: We thank the reviewer for this suggestion. We have replaced all instances of “POLR2A” with “RNA Pol II” throughout the manuscript to accurately refer to the protein.\nR1.11 In the sentence \"Next, we evaluated sequence patterns within the 1 kb downstream region of each TSS, in which canonical promoters display both enrichment of predicted 5′ splice-site motifs and depletion of polyadenylation signals\", the correct reference is Almada et al., Nature 2013.\nResponse: We thank the reviewer for the correction. We have updated the reference to Almada et al., Nature, 2013 in the revised manuscript.\nR1.12 Add “exon” before “assignment fraction” in all instances for clarity.\nResponse: We thank the reviewer for the suggestion. We have added “exon” before “assignment fraction” throughout the manuscript for clarity.\nR1.13 Check spelling, for example “FAMTOM” instead of “FANTOM.”\nResponse: We thank the reviewer for pointing this out. We have corrected the spelling error (“FAMTOM” to “FANTOM”) in the revised manuscript.\nR1.14 Figure 1E: the “TSS” acronym near the poly(A) motif may be confused with “transcription stop site.” Please clarify.\nResponse: We thank the reviewer for this helpful comment. We have replaced “TSS” with “transcription start site” in Figure 1E (revised as Figure 1F) to avoid any potential confusion.\nR1.15 Figure 2A: the shape coding for sample type (tissue, primary cell, cell line) is hard to read; simplify or enhance.\nResponse: We thank the reviewer for the helpful suggestion. In the revised Figure 2A, we use solid circles to denote tissue samples, crosses to denote primary cells, and open circles to denote cell lines. To improve readability, the symbols have been enlarged and their spacing adjusted to enhance visual distinction.\nR1.16 Figures 4C,E: the “・Testis” labels are misaligned.\nResponse: We thank the reviewer for pointing this out. The “Testis” labels have been removed from Figures 4C and 4E and are now included in the figure legend to avoid misalignment and improve clarity.\nR1.17 The sentence starting with “To distinguish TE-derived TSSs from other TE-overlapping TSSs…” is unclear. Please rephrase.\nResponse: We thank the reviewer for the comment. The unclear sentence has been removed, and the related discussion of thresholds has been moved to the main text for greater clarity.\nR1.18 Some figure references seem mismatched (e.g., “Figure 1E,F” should be “1F,G”).\nResponse: We thank the reviewer for pointing out this mismatch. In the revised manuscript, the relevant panels have been moved to the supplementary figures, and the references have been updated accordingly (now referred to as Supplemental Figures S2J,K).\nR1.19 “TE-derived” is used throughout, but “TE-initiated” appears twice — please clarify or standardize.\nResponse: We thank the reviewer for pointing this out. We have standardized the terminology throughout the manuscript by replacing the two occurrences of “TE-initiated” with “TE-derived” to maintain consistency.\nR1.20 Please add line numbers for ease of review.\nResponse: We appreciate the reviewer’s suggestion. Line numbers have been added throughout the revised manuscript to facilitate review.\n(Remarks on code availability)\nR1.21 The GitHub repository (https://github.com/kepbod/rampage_te_tss) currently provides only a subset of the code with minimal documentation. At a minimum, please improve documentation. Ideally, include scripts used to generate figures presented in the manuscript.\nResponse: We thank the reviewer for this suggestion. We have updated the GitHub repository to improve documentation, including detailed instructions for running the provided code. In addition, we have now included the scripts used to generate the figures presented in the manuscript, enabling full reproducibility of our analyses.\nReviewer #2 (Remarks to the Author)\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We thank the reviewer for co-reviewing this manuscript.\n(Remarks on code availability)\nReviewer #3 (Remarks to the Author)\nThis manuscript presents a comprehensive analysis of transcription start sites (TSSs) associated with transposable elements (TEs) in the human genome, primarily using RAMPAGE data. The topic is timely, relevant, and conceptually strong, as it addresses how repetitive sequences are exapted into regulatory functions. The work succeeds in assembling a large dataset and provides descriptive observations about tissue-restricted TSS usage and differences between younger and older TE subfamilies.\nHowever, in its current form the study remains primarily descriptive and heavily dependent on a single detection pipeline. It does not incorporate alternative computational tools (e.g., ChimeraTE, LIONS, TEProf) or sample-matched validation data, which weakens confidence in the generality of the conclusions. Some figures are overly dense, and several claims could be more cautiously phrased to avoid overstating the impact. Overall, while the dataset has value, the manuscript requires substantial revisions to meet the standards of Nature Communications.\nResponse: We thank the reviewer for the overall assessment and constructive feedback. We appreciate the recognition of the timeliness and conceptual significance of our study, as well as the value of the large TE-derived TSS dataset. In response to the concerns raised, we have made several substantial revisions:\n1. Methodological robustness: We clarified our RAMPAGE-integrated pipeline, incorporated matched RNA-seq support for transcript validation, and performed cross-validation with TEProf2 on representative samples to demonstrate reproducibility and sensitivity.\n2. Terminology and clarity: Definitions of “TE-overlapping” versus “TE-derived” TSSs are now consistent, and we clarified TE evolutionary age and promoter activity trends as a continuous gradient rather than a strict dichotomy.\n3. Figure clarity: Overcrowded pipeline figures were simplified and quality-control steps moved to the supplementary material to improve readability.\n4. Tone moderation: Several claims, including TF binding interpretation and the contribution of TE-derived TSSs to gene expression, have been carefully toned down to avoid overstating the impact.\nWe hope that these revisions address the reviewer’s concerns regarding descriptive emphasis, pipeline dependence, and figure readability, while retaining the novel insights and value of our TE-derived TSS atlas across human tissues.\nSpecific points\nR3.1 Ambiguity in terminology: The use of “TE-overlapping TSSs” vs “TE-derived TSSs” is inconsistent and confusing. Clear definitions early in the text (or a glossary schematic) are needed.\nResponse: We thank the reviewer for this comment. We have clarified the terminology throughout the manuscript. Specifically, “TE-overlapping TSSs” now consistently refers to all RAMPAGE peaks that overlap annotated transposable elements, whereas “TE-derived TSSs” refers to a subset of TE-overlapping TSSs that meet additional criteria—namely, RAMPAGE read pairs linking the peak to exonic regions of annotated or de novo transcripts, genomic span > 1 kb, and exon-assignment fraction ≥ 0.5. These definitions are summarized in a schematic in Supplemental Figure S1A and described explicitly in the Methods section as list below.\n“Peaks overlapping transposon elements (hg38 rmsk.txt downloaded from UCSC) were defined as TE-overlapping TSSs.\nCandidate TE-derived TSSs were selected if (i) paired RAMPAGE reads linked the peak to exonic regions of annotated or de novo transcripts, (ii) the genomic span of the read pair was > 1 kb , and (iii) the exon-assignment fraction≥0.5 (Supplemental Figures S1B,C; Supplemental Table S2).”\nR3.2 Dataset metadata: Table S1 lacks essential sample information (tissue type, donor demographics, sequencing depth, QC metrics, accession links). Without these, readers cannot properly assess dataset quality or representativeness.\nResponse: We thank the reviewer for this valuable suggestion. We have updated Supplemental Table S1 to include comprehensive metadata for all 115 RAMPAGE biosamples, including tissue type, donor age, sequencing depth, number of uniquely mapped reads, mapping ratio, and direct ENCODE accession links. These additions ensure full transparency and allow readers to readily assess the quality, representativeness, and provenance of all datasets used in this study.\nR3.3 Methodological dependence on a single pipeline. The entire study relies on one RAMPAGE-based detection pipeline. Given the diversity of available TE-derived TSS detection methods (e.g. ChimeraTE, LIONS, TEProf), the conclusions would be more robust if cross-validated with at least one complementary approach. This would help address potential biases or artifacts unique to the chosen pipeline.\nResponse: We thank the reviewer for raising this important point. Our pipeline is not solely RAMPAGE-based and also integrates matched RNA-seq data to facilitate the identification of TE-derived TSSs. We used RNA-seq data to assemble transcripts with StringTie and required that RAMPAGE read pairs link each candidate TSS to downstream exonic regions of assembled transcripts, with an exon-assignment fraction of at least 0.5. This design leverages the single-nucleotide precision of RAMPAGE while using RNA-seq as supportive evidence for transcript structure.\nIn contrast, other available TE-derived TSS detection methods rely only on RNA-seq data. They perform de novo transcript assembly and read assignment to annotate putative TE-derived TSSs, and therefore cannot directly capture 5′-capped transcripts or achieve nucleotide-level TSS resolution.\nTo evaluate robustness and potential pipeline-specific biases, we applied TEProf2, which also uses StringTie assemblies that have been reported to outperform Cufflinks used by ChimeraTE and LIONS, to five representative samples (GM12878, K562, heart, liver, and testis). TEProf2 identified far fewer candidate sites than our RAMPAGE-integrated pipeline, yielding 30, 50, 34, 38, and 122 sites compared with 961, 861, 554, 1150, and 5052 sites, respectively. Most TEProf2 calls were recovered by our pipeline, with overlap counts of 24, 36, 30, 26, and 99, corresponding to 80.0%, 72.0%, 88.2%, 68.4%, and 81.1% of TEProf2 calls in each sample. In contrast, these overlapping sites represent only 2.5%, 4.2%, 5.4%, 2.3%, and 2.0% of our larger RAMPAGE catalogs. These results indicate that RNA-seq–only methods recover only a small subset of TE-derived promoters also detected by our approach, whereas the integration of RAMPAGE data markedly enhances promoter discovery sensitivity\nWe further assessed reproducibility across biological replicates and found that in 50 biosamples with biological replicates, the median Pearson correlation of TE-derived TSS signal between replicates was 0.90, demonstrating high reproducibility of our calls.\nTogether, these analyses demonstrate that TEProf2, which relies on RNA-seq data, captures a subset of sites largely encompassed by our RAMPAGE calls, that differences in call numbers primarily reflect assay sensitivity rather than artifacts, and that our RAMPAGE-integrated pipeline is both robust and reproducible.\nR3.4 Overcrowded pipeline figures: Figures 1 and Sup. Fig. 1 attempt to show too many methodological details at once. Simplifying the pipeline schematic and moving QC checks to supplementary material would improve readability.\nResponse: We thank the reviewer for this constructive suggestion. To address the concern of overcrowding, we have simplified the main pipeline schematic in Figure 1A to display only the essential steps for identifying TE-derived TSSs, while detailed quality control procedures are now presented in Supplemental Figure S1A. Additionally, transcript structure validations of TE-derived TSSs, previously shown in Figures 1F–G, have been moved to Supplemental Figures S2J,K. These revisions maintain full methodological transparency while ensuring that the main figure conveys the workflow clearly and is easier to interpret.\nR3.5 Validation strategy is limited. The validation analyses are restricted to GM12878 and K562, which are convenient but atypical immortalized cell lines. Their regulatory landscapes may not reflect primary tissues. The authors should either (a) incorporate validation in at least one additional primary tissue, or (b) more explicitly acknowledge this limitation.\nResponse: We thank the reviewer for this valuable comment. In addition to GM12878 and K562, we have now included validation analyses in three primary tissues (heart, liver, and testis) to strengthen the generality of our findings (revised Supplemental Figure 1 and 2).\nR3.6 Tissue specificity claims: The manuscript interprets rare, single-tissue occurrences as evidence of “extensive tissue specificity.” Replicate consistency and prevalence across donors should be assessed; otherwise, the claim should be moderated.\nResponse: We thank the reviewer for this helpful comment. We have assessed replicate consistency across 50 biosamples with biological replicates, and the median Pearson correlation coefficient of TE-derived TSS signals was 0.90 (revised Supplemental Figure S2C), confirming the strong reproducibility of our RAMPAGE calls. This high consistency supports the robustness of our tissue-specificity analysis.\nR3.7 Contribution to gene expression: The heading “contribute substantially” is not well supported - TE-derived promoters account for ~5% of highly expressed genes, which is modest. A more cautious framing is needed.\nResponse: We appreciate the reviewer’s careful reading. We fully acknowledge that TE-derived TSSs occur in a relatively small fraction of highly expressed genes (~5%), and we have clarified this point in the revised manuscript. At the same time, our analyses demonstrate that when TE-derived TSSs are present, they can make a substantial contribution at the level of individual genes. Specifically, more than one quarter of genes with TE-derived TSSs derive over half of their promoter activity from these sites, and roughly half exceed a ten percent usage threshold (Figure 4D–F; Supplemental Figure S7). Thus, while TE-derived TSSs are not globally prevalent across all highly expressed genes, they frequently act as dominant promoters for a notable subset of genes. We have therefore (i) softened the Results heading to “TE-derived TSSs are enriched for narrow-peak promoter architectures and contribute to gene expression”, and (ii) added sentences in the Discussion clarifying the distinction between global prevalence and per-gene contribution so that readers can correctly interpret both aspects.\n“Although TE-derived TSSs appear in only around five percent of highly expressed genes, their contribution at the level of individual genes can still be substantial. Approximately one quarter of genes with TE-derived TSSs derive more than half of their promoter activity from these sites, and roughly half show at least ten percent usage, with over thirty percent of TE-derived TSSs functioning as the predominant or unique promoter. These findings indicate that, despite their limited genome-wide prevalence, TE-derived promoters often act as dominant regulatory elements for a considerable subset of genes, thereby contributing to transcriptomic diversity and regulatory innovation in specific genomic contexts.”\nR3.8 Clarity of age classification. The “young” versus “old” TE categories need clearer operational definitions. For example, are age estimates based on divergence from consensus sequences, comparative genomics, or other metrics? Without transparent criteria, it is difficult to evaluate the conclusions about “preserved” promoter motifs.\nResponse: We thank the reviewer for this comment. To clarify, we define the evolutionary age of TE subfamilies based on the percent sequence divergence of individual TE copies from their consensus sequences, which serves as a continuous proxy for time since insertion. Lower sequence divergence corresponds to relatively younger TE insertions, whereas higher divergence indicates older elements. For clarity, we have added this description to the Results section in the revised manuscript.\n“To estimate evolutionary age, we used the percent divergence of each TE copy from its consensus sequence as a proxy for time since insertion.”\nR3.9 Reliance on unmatched TF ChIP-seq: TF binding is inferred from datasets not matched to the RAMPAGE biosamples. Given context-specific binding, these analyses should be presented as suggestive rather than direct evidence.\nResponse: We thank the reviewer for this comment. We have toned down the language regarding TF binding in the main text to reflect that these observations are suggestive rather than direct evidence. The revised paragraph emphasizes enrichment patterns without over-interpreting them.\n“Finally, to explore which transcription factors (TFs) are preferentially associated with TE-derived TSSs, we overlapped them with ChIP-seq peaks from 694 TFs curated by the ENCODE Consortium36 (Supplemental Figure S5A). TE-derived TSSs tend to bewere bound by a broader repertoire of TFs compared to promoter-proximal TEs (Chi-squared test, p < 2.2×10⁻¹⁶; Figure 3H). Several general TFs—including POLR2A, TBP, TAFs, and YY1—show enrichment across TE-derived TSSs from diverse subfamilies (Figure 3H). Aggregate ChIP-seq signal profiles in matched biosample further support stronger binding of these TFs at TE-derived TSSs relative to promoter-proximal TEs (Supplemental Figure S5B). In contrast, several TFs exhibited subfamily-specific patterns suggestive of preferential TF association, supported by both ChIP-seq signal and motif enrichment (Supplemental Figure S5C). For instance, ETS1 and ETV5, which recognize canonical ETS motifs37, were preferentially enriched at MIR-derived TSSs. Similarly, NFYA and NFYB, which bind CCAAT boxes38, showed selective enrichment at ERV1- and ERVL-MaLR-derived TSSs. USF1 and USF2, which recognize E-box motifs39, were enriched at ERVL and ERVL-MaLR TSSs. Together, these results suggest that specific TE subfamilies may retain recognizable TF motifs that could facilitate subfamily-specific regulatory activity. Notably, TF binding patterns at TE-derived TSSs appear distinct from those governing autonomous TE transcription. For example, we previously showed that AP-1 family members such as FOS and JUN preferentially bind Pol III-transcribed Alu elements to promote their expression24. However, this enrichment was absent from Alu-derived TSSs (Supplemental Figure S5D), which is suggestive of a transition from internal TE-driven transcription to host-mediated co-option of TE fragments as regulatory modules.”\nR3.10 Young vs. old TE promoters: The contrast between precise “young” and dispersed “old” promoters may be overstated. TE “age” criteria should be clarified, and the pattern reframed as a continuum rather than a strict dichotomy.\nResponse: We thank the reviewer for this suggestion. We have clarified that TE “age” is defined continuously based on sequence divergence from consensus sequences, and reframed the differences in promoter precision and strength as a gradient rather than a strict dichotomy. Younger, low-divergence TEs retain promoter motifs and drive tightly clustered, high-activity TSSs, whereas more diverged elements show broader TSS placement and reduced intrinsic activity. Corresponding revisions have been made in the Abstract, Results, and Discussion sections.\n“Phylogenetic analyses reveal a continuous gradient in promoter strength and transcriptional precision across TE subfamilies, with evolutionarily younger TEs retaining intrinsic promoter motifs that drive focused and robust transcription, whereas older, more diverged elements exhibit broader TSS usage and lower intrinsic activity.\nTogether, these findings indicate that promoter precision and activity form a continuous evolutionary gradient that parallels TE sequence divergence. Elements with low divergence preserve their intrinsic promoter architecture and drive strong, well-defined initiation, while those that have accumulated substitutions and deletions exhibit dispersed transcription initiation and increasing dependence on host regulatory environments.\nOur evolutionary analysis reveals a continuous gradient in the transcriptional precision and strength of TE-derived TSSs that parallels sequence divergence and phylogenetic age. Younger TE subfamilies restricted to primates and hominids retain intact core promoter motifs and drive tightly clustered initiation at conserved positions, resulting in precise and robust transcription. In contrast, older lineages such as MIR and L2 exhibit higher sequence divergence, more dispersed TSS placement, weaker intrinsic strength, and partial loss of structural integrity, reflecting gradual promoter decay through sequence erosion and selection18,33.\nPrevious reports have demonstrated that primate-specific TEs often harbor lineage-restricted transcription factor binding sites active during early development51,52, consistent with the longer retention of functional motifs in younger elements. The strong correlation between TSS clustering and promoter strength across Alu, L1, and ERVL–MaLR families further supports a model in which promoter exaptation follows a continuous evolutionary trajectory, shaped by the preservation of intrinsic sequence features in younger TEs and the surrounding genomic context in older insertions.”\n(Remarks on code availability)"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed my comments.\n(Remarks on code availability)", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n(Remarks on code availability)", "reviewer_3": "While the authors have satisfactorily addressed all my other concerns, I remain unconvinced by their response to this point:\nAlthough they clarify that their approach integrates RAMPAGE and RNA-seq data, the magnitude of discrepancy they report compared with TEProf2 (10- to 100-fold higher counts) suggests that their pipeline may be over-sensitive rather than uniquely accurate. Such inflation likely reflects permissive thresholds and residual mapping ambiguity within repetitive regions, where RAMPAGE read dispersion can generate artefactual TSS calls. Moreover, the cross-assay concordance reported with GRO-cap, Iso-Seq, and FANTOM CAGE represents general promoter overlap rather than true TE-initiated transcript confirmation, as these datasets share similar promoter-proximal biases. High replicate correlation (r = 0.90*) demonstrates reproducibility of signal but not necessarily specificity or accuracy. Overall, the evidence provided does not convincingly rule out that the much larger RAMPAGE-based catalogs reflect pipeline-driven sensitivity and permissive filtering, rather than genuine biological expansion of TE-derived promoters; more stringent FDR estimation, independent TE-specific benchmarking, or orthogonal validation (e.g., targeted CAGE or reporter assays) would be required to substantiate these conclusions.\n(Remarks on code availability)"}, "author_response": "Point-by-Point Responses to Reviewers:\nReviewer #1 (Remarks to the Author):\nThe authors have adequately addressed my comments.\nResponse: We thank the reviewer for their encouraging feedback and are pleased that the revisions have adequately addressed the concerns.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We thank the reviewer for co-reviewing this manuscript.\nReviewer #3 (Remarks to the Author):\nWhile the authors have satisfactorily addressed all my other concerns, I remain unconvinced by their response to this point: Although they clarify that their approach integrates RAMPAGE and RNA-seq data, the magnitude of discrepancy they report compared with TEProf2 (10- to 100-fold higher counts) suggests that their pipeline may be over-sensitive rather than uniquely accurate. Such inflation likely reflects permissive thresholds and residual mapping ambiguity within repetitive regions, where RAMPAGE read dispersion can generate artefactual TSS calls. Moreover, the cross-assay concordance reported with GRO-cap, Iso-Seq, and FANTOM CAGE represents general promoter overlap rather than true TE-initiated transcript confirmation, as these datasets share similar promoter-proximal biases. High replicate correlation (r = 0.90*) demonstrates reproducibility of signal but not necessarily specificity or accuracy. Overall, the evidence provided does not convincingly rule out that the much larger RAMPAGE-based catalogs reflect pipeline-driven sensitivity and permissive filtering, rather than genuine biological expansion of TE-derived promoters; more stringent FDR estimation, independent TE-specific benchmarking, or orthogonal validation (e.g., targeted CAGE or reporter assays) would be required to substantiate these conclusions.\nResponse: We thank the reviewer for raising this important point regarding potential over-sensitivity of our TE-derived TSS pipeline in repetitive regions. We would like to emphasize that multiple independent lines of evidence—including analyses and experiments newly added in this revision—support the accuracy and biological relevance of our TE-derived TSS annotations, rather than reflecting artefactual calls:\n1. Stringent read mapping and peak calling:\n• We exclusively used uniquely mapped RAMPAGE read pairs to call TE-derived TSSs and applied our previously developed entropy (E) metric to distinguish true TSS peaks from background noise in repetitive loci, which has been validated in our previous work (Zhang et al., Genome Research, 2019).\n• Notably, RAMPAGE has been independently shown to accurately identify transcription start sites of transposable elements (Batut et al., Genome Research, 2013; Zhang et al., Genome Research, 2019) as well as protein-coding genes (Moore et al., Genome Research, 2021).\n2. Evaluation of TE-derived TSS precision:\n• Overlapping our annotated TE-derived TSSs with RAMPAGE peaks from K562 and GM12878 showed that nearly all peaks fall within ±20 nt of their corresponding TE-derived TSSs (Supplemental Figure S6A; permutation test P-values < 1 × 10⁻⁴).\n• Furthermore, newly added analyses in this revision (new Supplemental Figure S6B; see below) show that the variability of RAMPAGE peak summits at TE-derived TSSs is comparable to that of mRNA TSSs and significantly lower than for other TE-overlapping TSSs, indicating precise and reproducible localization rather than dispersed or ambiguous initiation within repetitive regions.\n• In addition, we note that the majority of high-confidence TE-derived TSSs identified by our pipeline (~80%) overlap previously annotated transcription start sites (Supplemental Figure S3A), indicating that the expanded catalog largely reflects systematic recovery of bona fide promoters rather than artefactual inflation driven by permissive thresholds.\n3. Intrinsic promoter activity:\n• TE-derived TSSs exhibit significantly higher intrinsic transcriptional activity than other TE-overlapping TSSs in SuRE assays (Figure 1D).\n• In addition, luciferase reporter assays performed in this revision using four randomly selected TE-derived TSSs demonstrate robust, strand-specific promoter activity, with all tested TSSs driving luciferase expression significantly above the empty vector control and exceeding the activity of two previously reported TE-derived TSSs (new Figure 1E and revised Supplemental Figure S2A; see below).\n4. Evidence for genuine TE-initiated transcripts:\n• The 1 kb downstream regions of TE-derived TSSs recapitulate canonical promoter features, including enrichment of 5′ splice-site motifs and depletion of polyadenylation signals (Figure 1F).\n• Newly added in this revision, a significantly higher proportion of TE-derived TSSs are supported by splice-junction-spanning RAMPAGE read pairs, compared with other TE-overlapping TSSs (new Figure 1G; see below).\n• Independent de novo assembly of RAMPAGE reads confirms that TE-derived TSSs generate multi-exonic transcripts, whereas other TE-overlapping sites predominantly yield short, mono-exonic assemblies (Supplemental Figures S2C–D).\n• Finally, also newly added in this revision, 5′ RACE and Sanger sequencing of four randomly selected, previously unannotated TE-derived TSSs in K562 cells confirmed the transcription initiation events identified by our pipeline (new Figure 1H; revised Supplemental Figure S2E; see below).\nCollectively, these newly added analyses and experimental validations demonstrate that our TE-derived TSS catalog is accurate, reproducible, and biologically meaningful, and that the expanded number of TE-derived TSSs reflects genuine promoter activity rather than pipeline artefacts."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have now adequately addressed my comments.\n(Remarks on code availability)"}, "author_response": ""}]

Decision letter