Our genome consists of about two percent genes, while around 60 to 70 percent is made up of hundreds of thousands of copies of very similar DNA sequences. These repeats have accumulated over time due to specific genetic elements called transposons.

Transposons are often referred to as ‘jumping genes’, as they can move within the genome and thereby create mutations that may lead to cancer or other genetic diseases. LINE-1 is the only remaining active transposon in humans, and it expands by copying and pasting itself to new locations. To do so, it is first transcribed into RNA – the molecules that help to make proteins – and then converted back into identical DNA sequences.

In a never-ending battle, our cells have been fighting to keep LINE-1 and its ancestors from replicating, and so evolved various defense mechanisms. Yet, LINE-1 has learned to circumvent these barriers, and continues to replicate and cause disease. Our understanding of these defenses and of how LINE-1 evades them is limited.

Previous research has shown that the LINE-1 RNA and its two encoded proteins, called ORF1p and ORF2p, interact with a series of other proteins, with which they can form different types of complexes. Now, Taylor, Altukhov, Molloy et al. used human embryonic kidney cells grown in the laboratory with different LINE-1 mutations to identify how they affect the bound proteins and RNAs. The results showed that LINE-1 can form at least two different sets of complexes with other proteins.

The complex containing ORF1p and ORF2p and several other proteins was located in the cytoplasm, the fluid that fills the cells. However, the experiments also revealed a new complex in the cell nucleus, which contained ORF2p and proteins involved in DNA replication and repair, but not ORF1p. The results suggest ORF1p delivers RNPs to the nucleus around the time the cell divides. Another group of researchers has looked more closely at what happens during cell division.

A next step will be to study how exactly LINE-1 contributes to cancer. In the future, overactive LINE-1 proteins could be targeted to kill cancer cells, to identify cancer early, or to see if the cancer has come back. LINE-1 may also provide clues on how the genome has evolved.

Sequences resulting from retrotransposition constitute more than half of the human genome and are considered to be major change agents in eukaryotic genome evolution (Kazazian, 2004). L1 retrotransposons have been particularly active in mammals (Furano et al., 2004), comprising ~20% of the human genome (Lander et al., 2001); somatic retrotransposition has been widely implicated in cancer progression (Lee et al., 2012; Tubio et al., 2014) and may even play a role in neural development (Muotri et al., 2005). Despite the magnitude of their contributions to mammalian genomes, L1 genes are modest in size. A full-length L1 transcript is ~6 knt long and functions as a bicistronic mRNA that encodes two polypeptides, ORF1p and ORF2p (Ostertag and Kazazian, 2001), which respectively comprise a homotrimeric RNA binding protein with nucleic acid chaperone activity (Martin and Bushman, 2001) and a multifunctional protein with endonuclease and reverse transcriptase activities (Mathias et al., 1991; Feng et al., 1996). Recently, a putative primate-specific third ORF, named ORF0, has been identified on the Crick strand of the L1 gene; this ORF encodes a 71 amino acid peptide and may generate insertion-site-dependent ORFs via splicing (Denli et al., 2015). ORF1p and ORF2p are thought to interact preferentially with the L1 RNA from which they were translated (in cis), forming a ribonucleoprotein (RNP) (Kulpa and Moran, 2006; Taylor et al., 2013) considered to be the canonical direct intermediate of retrotransposition (Hohjoh and Singer, 1996; Kulpa and Moran, 2005; Martin, 1991; Kulpa and Moran, 2006; Doucet et al., 2010). L1 RNPs also require host factors to complete their lifecycle (Suzuki et al., 2009; Peddigari et al., 2013; Dai et al., 2012; Taylor et al., 2013) and, consistent with a fundamentally parasitic relationship (Beauregard et al., 2008), the host has responded by evolving mechanisms that suppress retrotransposition (Goodier et al., 2013; Arjan-Odedra et al., 2012; Goodier et al., 2012; Niewiadomska et al., 2007). It follows that as the host and the parasite compete, L1 expression is likely to produce a multiplicity of RNP forms engaged in discrete stages of retrotransposition, suppression, or degradation.

Although L1 DNA sequences are modestly sized compared to typical human genes, L1 intermediates are nevertheless RNPs with a substantially sized RNA component; e.g. larger than the ~5 knt 28S rRNA (Gonzalez et al., 1985) and approximately three to four times the size of a ‘typical’ mRNA transcript (Lander et al., 2001; Sommer and Cohen, 1980). Therefore, it is likely that many proteins within L1 RNPs form interactions influenced directly and indirectly by physical contacts with the L1 RNA. We previously reported that L1 RNA comprised an estimated ~25% of mapped RNA sequencing reads in ORF2p-3xFLAG affinity captured fractions (Taylor et al., 2013). We also observed that the retention of ORF1p and UPF1 within affinity captured L1 RNPs was reduced by treatment with RNases (Taylor et al., 2013). In the same study we observed that two populations of ORF2p-associated proteins could be separated by split-tandem affinity capture (ORF2p followed by ORF1p), a two-dimensional affinity enrichment procedure (Caspary et al., 1999; Taylor et al., 2013). Initial characterization of these two L1 populations by western blotting suggested that discrete L1 populations were likely primed for function in different stages of the lifecycle. We therefore expected additional uncharacterized complexity in the spectrum of L1-associated complexes present in our affinity enriched fractions.

In this study, we have used quantitative mass spectrometry (MS) to investigate the proteomic characteristics of endogenously assembled ectopic L1-derived macromolecules present in an assortment of affinity-enriched fractions. We revisited RNase treatment and split-tandem affinity capture approaches and complemented them with RNA sequencing, enzymatic analysis, and in-cell localization of ORF proteins by immunofluorescence microscopy (see also the companion manuscript by Mita et al., 2018). We additionally explored proteomes associated with catalytically-inactivated ORF2p point mutants and monitored the rates of protein exchange from L1 macromolecules in vitro. Taken together, our data support the existence of a variety of putative L1-related protein complexes.

Affinity proteomic experiments conducted in this study use quantitative MS based upon metabolic labeling (Oda et al., 1999). Two main experimental designs (and modifications thereof) facilitating quantitative cross-sample comparisons have been used: SILAC (Ong et al., 2002; Wang and Huang, 2008) and I-DIRT (Tackett et al., 2005; Taylor et al., 2013). In these approaches, cells are grown for several doublings in media containing amino acids composed either of naturally-occurring ‘light' isotopes or biologically identical 'heavy' isotopes (e.g. ¹³C, ¹⁵N lysine and arginine), such that the proteomes are thoroughly labeled. Protein fractions derived from the differently labeled cell populations, obtained e.g. before and after experimental manipulations are applied, are mixed and the relative differences in proteins contributed by each fraction are precisely measured by mass spectrometry. In addition to the above cited studies, these approaches have been adapted to numerous biological questions using a variety of analytical frameworks e.g. (Byrum et al., 2011; Luo et al., 2016; Trinkle-Mulcahy et al., 2008; Ohta et al., 2010; Kaake et al., 2010; Geiger et al., 2011). Because it is challenging to speculate on the potential physiological roles of protein interactions that form after extraction from the cell, we often use I-DIRT, which allows the discrimination of protein-protein interactions formed in-cell from those occurring post-extraction. Our prior affinity proteomic study, based on I-DIRT, identified 37 putative in vivo interactors (Taylor et al., 2013), described in Table 1. In this study we primarily analyze the behaviors of these ‘I-DIRT significant’ L1 interactors, in order to determine their molecular associations and ascertain the variety of distinctive macromolecular complexes formed in-cell that copurify with affinity-tagged ORF2p. The complete lists of proteins detected in each experiment are presented in the supplementary information (see Supplementary file 1). We have represented any ambiguous protein group, which occurs when the same peptides identify a group of homologous protein sequences, with a single, consistently applied gene symbol and a superscript 'a' in all figures. Supplementary file 1 contains the references to other proteins explaining the presence of the same peptides. For example, RPS27A, (ubiquitin) UBB, UBC, and (ribosomal Protein L40) UBA52 can be explained by common ubiquitin peptides shared by these genes. RPS27A-specific peptides were not identified in this study, but we retained the nomenclature for consistency with our previous work; HSPA1A is reported in this study, but cannot be distinguished from the essentially identical protein product of HSPA1B.

Except where noted otherwise, the presented experiments were conducted in suspension-cultured HEK-293T_LD cells, using a synthetic L1 construct - ORFeus-HS - driving the expression 3xFLAG-tagged L1 (ORF1; ORF2::3xFLAG; 3’-UTR) from a tetracycline inducible minimal-CMV promoter, harbored on a mammalian episome (pLD401 (Taylor et al., 2013; An et al., 2011; Dai et al., 2012)). All L1-related macromolecules described in this study were obtained by affinity capture of ORF2p-3xFLAG before further experimental manipulations were applied. We consider macromolecules containing L1 RNA (L1 RNPs, discussed throughout) and/or an L1 cDNA (i.e. L1 coding potential) to be L1s, as are their ectopic plasmid-borne and endogenous gDNA counterparts, reflecting the complexity and diversity of L1 forms arising from its lifecycle. In an effort to characterize this complexity, we have carried out RNA sequencing and enzymatic activity analyses on several affinity captured fractions, complementing the proteomic analyses.

L1 RNA and LEAP activity in affinity captured fractions

Because the L1 RNA is an integral component of proliferating L1s, and because we observed that interactions between ORF2p, ORF1p, and some host proteins were sensitive to treatment with RNases, we sought to characterize the RNAs present in our samples. We extracted RNAs from each of the three fractions produced by split-tandem affinity capture (Figure 1D) and carried out RNA sequencing; Figure 2A displays the sequence coverage observed across the entirety of our synthetic L1 construct in each fraction, revealing a normalized ~2 fold difference in abundance between the elution and supernatant fractions. Synthetic L1s constituted ~60% of the mapped, annotated sequence reads in the fractions eluted from the α-FLAG and α-ORF1p affinity media, and ~30% of the reads in the ORF1p-depleted supernatant fraction; sequencing reads mapping to protein coding genes made up the majority of the remaining annotated population in all fractions. We observed that a substantial number of reads mapped to unannotated regions of the human genome, in particular in the supernatant fraction, enriched for putative nuclear L1 complexes; the breakdown of mapped and annotated sequencing reads is summarized in Figure 2B and expanded in Supplementary file 4.

Figure 2

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Retrotransposition-competent L1 RNPs form in cis, with ORF proteins binding to the L1 RNA that encoded them (‘cis preference’), presumably at the site of translation in the cytoplasm (Kulpa and Moran, 2006; Wei et al., 2001). Given that ORF1/2p partitioned to the split-tandem elution fraction (cytoplasmic) along with the greater fraction of L1 RNA, yet only ORF2p and a lesser portion of the L1 RNA were observed in the supernatant (nuclear), an important consideration regarding these fractions is: to what extent they contain L1 macromolecules capable of proliferation. To address this question, we performed the LINE-1 element amplification protocol (LEAP) on split-tandem affinity captured fractions (Figure 2C; Supplementary file 4), including a ΔORF1 construct (pLD561) as a control (Taylor et al., 2013). LEAP is currently the best biochemical assay for functional co-assembly of L1 RNA and proteins (Kulpa and Moran, 2006); it measures the ability of ORF2p to amplify its associated L1 RNA by reverse transcription. To execute LEAP on the α-ORF1p affinity captured fraction, we developed a competitive di-peptide elution reagent based on the linear peptide sequence used to generate the α-ORF1p 4H1 monoclonal antibody: residues 35–44 in ORF1p ([Khazina et al., 2011; Taylor et al., 2013]; see Materials and methods). We were thus able to assay the partitioning of enzymatic activity within the different populations of copurifying proteins in a split-tandem affinity capture experiment. Our data showed robust LEAP activity in both nuclear and cytoplasmic split-tandem supernatant and elution fractions. We note that our 3xFLAG eluted fractions have been shown to possess ~70 fold higher specific activity than L1 RNPs obtained by sucrose cushion velocity sedimentation (Taylor et al., 2013), hence the activity levels detected far exceed those obtained by sedimentation.

ORF1p/ORF2p immunofluorescence protein localization

Although our proteomic and biochemical analyses supported the existence of distinctive nuclear and cytoplasmic L1 populations, our prior immunofluorescence (IF) analyses did not reveal an apparent nuclear population, leading us to revisit IF studies. Previously, IF of ORF1p and ORF2p in HeLa and HEK-293T cells yielded two striking observations: (i) ORF2 expression was seemingly stochastic, with ORF2p observed in ~30% of cells; and (ii) while ORF1p and ORF2p co-localized in cells that exhibited both, we did not observe an apparent nuclear population of either protein (Taylor et al., 2013). Subsequently, we noted an absence of mitotic cells from these preparations. Reasoning that these cells were lost due to selective adherence on glass slides, and noting that cell division has been reported to promote L1 transposition (Xie et al., 2013; Shi et al., 2007), we repeated the assays using puromycin-selected Tet-on HeLa cells grown on fibronectin coated coverslips. The results are shown in Figure 3.

Figure 3

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Figure 3—source data 1

Source data used in the analysis of ORF2p+ inter nuclear distance analysis: Figure 3 and Supplementary file 5.

https://doi.org/10.7554/eLife.30094.006

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The modified IF assay corroborated our prior results in that nearly all the cells exhibited cytoplasmic ORF1p and a minority subset of ~1/3^rd also exhibited co-localized cytoplasmic ORF2p (Figure 3A, top row). We also observed an uncommon and previously unrecognized subpopulation of cells, consisting of pairs exhibiting nuclear localized ORF2p (Figure 3A, middle row); because these cells occurred in proximal pairs, we presumed them to have recently gone through mitosis. Statistical analysis of microscopy images displaying cells with nuclear localized ORF2p confirmed their proximities to be significantly closer than those of randomly selected cells (Figure 3B; Supplementary file 5). Expression of ORF2 in the absence of ORF1 (ΔORF1; pLD561) resulted in the majority of cells exhibiting cytoplasmic ORF2p, consistent with our previous work (Taylor et al., 2013). We did not observe instances of nuclear ORF2p using the ΔORF1 construct (Figure 3A, bottom row), suggesting that ORF1p is required for ORF2p nuclear localization (see Discussion). In a separate study, including more detailed analyses of ORF protein localization, Mita et al., 2018 observed that both ORF proteins enter the nucleus of HeLa cells during mitosis, however, nuclear ORF1p does not seem to be physically associated with nuclear ORF2p (see Discussion). Taken together, the data obtained from the modified IF experiments aligned well with our proteomic and biochemical data; L1 expression resulted in at least two distinct populations: cytoplasmic complexes containing both ORF1p and ORF2p, and nuclear complexes containing ORF2p while potentially lacking ORF1p.

Multidataset integration

Having observed coordinated and distinctive behaviors exhibited by groups of L1 interacting proteins across several distinctive biochemical assays, we then integrated the data and calculated the behavioral similarity of the I-DIRT-significant interactors, producing a dendrogram; Figure 6 displays their relative similarities. A cluster containing the putative cytoplasmic L1 components (MOV10, UPF1, ZCCHC3, PABC1/4, ORF1p) was observed, as was a cluster containing PURA/B, PCNA, TOP1, PARP1, aligning with our assessments of the separated datasets (Figures 1, 4 and 5). In addition to these, we also observed three distinctive clusters derived from the nuclear L1 interactome. We believe that this is likely to reflect the presence of a collection of distinctive macromolecules.

Figure 4 with 1 supplement see all

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Figure 5

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Figure 6

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In this study we have characterized biochemical, interatomic, enzymatic, and cellular localization properties of ectopically expressed L1s. Through the assays explored, we observed discrete and coordinated behaviors, permitting us to refine our model of L1 intermediates, diagrammed in Figure 7. We propose a cytoplasmic L1, composed of ORF1/2 p, L1 RNA, PABPC1/4, MOV10, UPF1, and ZCCHC3, that constitutes an abundant, canonical RNP intermediate often referred to in the literature. MOV10, UPF1, and ZCCHC3 are apparently substoichiometric to ORF2p in our preparations, therefore it may be that only a subset of cytoplasmic intermediates engages these host restriction factors. On the other hand, this apparent relative abundance may simply reflect a lower in vitro stability of UPF1 and MOV10 within this complex (Figure 5). Although ORF1p is apparently required for efficient ORF2p nuclear entry, we also propose a second more complicated population, lacking (or with significantly less) ORF1p, that constitutes nuclear or pre- dominantly nuclear L1 macromolecules. We note that Alu elements exhibit ORF2p-dependent mobilization that does not require ORF1p, but appears to be enhanced by ORF1p in some contexts (Dewannieux et al., 2003; Wallace et al., 2008); this is not true for L1 or processed pseudogenes, and we conclude Alu RNPs likely exploit an alternate mechanism of nuclear entry. The nuclear L1 population is enriched for factors linked to DNA replication and repair, including PURA, PURB, PCNA, TOP1, and PARP1; we propose that these proteins, along with ORF2p, form part of a direct intermediate of TPRT, although these components may not all act in synergy. Our proposals are broadly supported by the findings of Mita et al., 2018, who present data to support the hypothesis that PCNA-associated ORF2p is not appreciably associated with ORF1p, and also identified TOP1 and PARP1 in complex with ORF2p/PCNA.

Figure 7

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Although the protein purification approach was similar, we observed an apparently larger proportion of L1 RNA in our recent preparations than in our previous study. We reported that L1 constituted ~25% of mapped reads previously (Taylor et al., 2013); a comparable result was obtained when we reanalyzed that data using the pipeline described here (see Materials and methods): ~93% of reads in our reanalyzed 2013 dataset mapped to the human genome, and L1 constituted ~20% of reads mapped to annotated features (‘annotated reads’) in 3xFLAG eluates. In this study we report that ~60% of annotated reads mapped to synthetic L1 in 3xFLAG eluates (Figure 2A). The higher proportion of L1 recovered may be due to the combination of higher fidelity RNA preparative methods and advanced sequencing technology used here; we observed ~10x more total reads mapping to L1 and comparatively improved, more uniform coverage across the entire L1 sequence, likely explaining the discrepancy. We also noted that the number of normalized reads mapped to L1 in our initial 3xFLAG elutions (‘input’) and subsequent tandem-purified α-ORF1p elutions were comparable, and yet ~1/2 as many were seen in the α-ORF1p supernatant fraction (Figure 2A,B). We suspect that this is due to saturation in library preparations or sequencing steps for the ‘input’ and ‘elution’ fractions, but conclude that more L1 RNA is in the ‘cytoplasmic’ elution fraction than the ‘nuclear’ supernatant.

We observed substantial and comparable LEAP activity in both our tandem-purified ORF1p+ (‘elution’) and ORF1p– (immuno-depleted ‘supernatant’) populations (Figure 2C, Supplementary file 4). To our knowledge, these represent the simplest and purest endogenously assembled L1 RNPs yet reported that exhibit robust signal in the LEAP assay. We note that, our results demonstrating robust activity in the nuclear-enriched supernatant fraction (depleted of ORF1p) may contrast with previous reports of reduced LEAP activity in constructs where ORF1p RNA binding was compromised (Kulpa and Moran, 2006), but our fractions merit further study and comparisons on the basis of ORF2p and RNA levels to determine specific activity.

Cytoplasmic L1 macromolecules

ORF1p, MOV10, UPF1, and ZCCHC3 are released from L1 RNPs by treatment with RNases (Figure 1), indicating the importance of the L1 RNA in the maintenance of these interactions. In this context, the L1 ORF and poly(A) binding proteins support L1 proliferation (Kulpa and Moran, 2006; Dai et al., 2012; Wei et al., 2001), whereas ZCCHC3 (Supplementary file 2) and MOV10 (Goodier et al., 2012; Arjan-Odedra et al., 2012) function in repressive capacities. Although UPF1 might also be expected to operate in a repressive capacity through its role in nonsense mediated decay (NMD), we previously demonstrated that UPF1's role does not apparently resemble that of canonical NMD and it acts as an enhancer of retrotransposition despite negatively affecting L1 RNA and protein levels, supporting the possibility of repressive activity in the cytoplasm and proliferative activity in the nucleus (Taylor et al., 2013). Notably, MOV10 has been implicated in the recruitment of UPF1 to mRNA targets through protein-protein interactions (Gregersen et al., 2014). However, we observed that MOV10 exhibited a greater degree of RNase-sensitivity than UPF1, indicating that, if MOV10 directly modulates the UPF1 interactions with L1, a sub-fraction of UPF1 exhibits a distinct behavior (UPF1 is ~62% as sensitive to RNase treatment as MOV10, Figure 1C). Bimodal UPF1 behavior can also be seen in split-tandem capture experiments, only about half of the UPF1 exhibited ORF1p-like partitioning with the canonical L1 RNP (Figure 1F). Moreover, UPF1 was recovered with L1s affinity captured from fractionated chromatin (further discussed below). Presumably, the RNase-sensitive fraction, released in concert with MOV10, is the same fraction observed in cytoplasmic L1s obtained by split-tandem capture. In contrast, PABPC1 and C4 exhibit strong ORF1p-like partitioning (comparable to MOV10), but appear wholly insensitive to RNase treatment. This is most likely due to the fact that neither RNase A nor T1 cleave RNA at adenosine residues (Volkin and Cohn, 1953; Yoshida, 2001); hence poly(A) binding proteins may not be ready targets for release from direct RNA binding by the assay implemented here (or generally, using these ribonucleases). Failure to release ORF2p into the supernatant upon RNase treatment is expected due to its immobilization upon the affinity medium (Dai et al., 2014). However, we note that ORF2p binding to the L1 RNA has also been proposed to occur at the poly(A) tail (Doucet et al., 2015), raising the related possibility of close physical association on the L1 RNA between ORF2p and PABPC1/4 in cytoplasmic L1 RNPs. ORF1p, PABPC1/4, MOV10, ZCCHC3, and UPF1, all behaved comparably in response to EN^- and RT^- catalytic mutations, decreasing together in EN^- mutants, and increasing together in RT^- mutants (Figure 4C). Moreover, when the exchange of proteins within L1 RNPs was monitored directly, PABPC1/4 and ZCCHC3 exhibited nearly identical stability, well above the background distribution; UPF1 and MOV10 also exhibited comparable kinetics to one another, falling into an intermediary stability cluster (Figure 5B,C).

Figure 1

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RNase-sensitivity was displayed by numerous proteins not previously identified as putative L1 interactors (Table 1, Figure 1; [Taylor et al., 2013]). A known limitation of I-DIRT (and many SILAC-based analyses) is that it cannot discriminate non-specific interactors from specific but rapidly exchanging interactors (Wang and Huang, 2008; Luo et al., 2016; Smart et al., 2009). Our samples likely contain rapidly exchanging, physiologically relevant factors that were not revealed by I-DIRT under the experimental conditions used. With this in mind, we note members of the exon junction complex (EJC), RBM8A (Y14), EIF4A3 (DDX48), and MAGOH, are among our RNase-sensitive constituents, all exhibiting a similar degree of RNase-sensitivity (Figure 1C, labeled black dots). Crucially, these proteins are physically and functionally connected to UPF1 (reviewed in [Schweingruber et al., 2013]), and physically to MOV10 (Gregersen et al., 2014), both validated L1 interactors. We therefore hypothesize that EJCs may constitute bona fide L1 interactors missed in our original screen. This may seem unexpected because canonical L1 RNAs are thought not to be spliced, but this assumption has been challenged by one group (Belancio et al., 2006), and splicing-independent recruitment of EJCs has also been demonstrated (Budiman et al., 2009). Perhaps more compelling, EJC proteins exhibited a striking similarity in RNase-sensitivity to MOV10 (Figure 1C). EIF4A3 has been suggested to form an RNA-independent interaction with MOV10 (Gregersen et al., 2014), and MOV10 is a known negative regulator of L1, making it attractive to speculate that these proteins were recruited and released in concert with MOV10 and/or UPF1.

Ectopically expressed canonical L1 RNPs have been shown to accumulate in cytoplasmic stress granules (Doucet et al., 2010; Goodier et al., 2010), and our observation of UPF1, MOV10, and MAGOH in the RNase-sensitive fraction is consistent with this characterization (Jain et al., 2016). However, the additional presence of EIF4A3 and RBM8A suggested that our RNPs may instead overlap with IGF2BP1 (IMP1) granules, reported to be distinct from stress granules (Jønson et al., 2007; Weidensdorfer et al., 2009). Consistent with this possibility, we observed IGF2BP1, YBX1, DHX9, and HNRNPU within the mixture of co-captured proteins (Supplementary file 1). We did not, however, observe canonical stress granule markers G3BP1 or TIA1 (Goodier et al., 2007; Jain et al., 2016; Doucet et al., 2010). Surprisingly, siRNA knockdown of IGF2BP1 substantially reduced L1 retrotransposition; however, we note that the cytotoxicity associated with knocking-down EJC components may confound interpretation (Supplementary file 2). Given the result obtained, IGF2BP1 appears to support L1 proliferation. Consistent with an established function (Bley et al., 2015; Weidensdorfer et al., 2009), IGF2BP1 granules may sequester and stabilize L1 RNPs in the cytoplasm, creating a balance of L1 supply and demand that favors proliferation over degradation. Although human L1 does not contain a known IRES, it is known that ORF2 is translated by a non-canonical mechanism (Alisch et al., 2006), and IGF2BP1 may promote this (Weinlich et al., 2009).

The preparation of L1 RNPs was carried out essentially as previously described (Taylor et al., 2013, Taylor et al., 2016), with modifications described here. Briefly, HEK-293T_LD cells (Dai et al., 2012) transfected with L1 expression vectors were cultured as previously described or using a modified suspension-growth SILAC strategy described below. L1 expression was induced with 1 μg/ml doxycycline for 24 hr, and the cells were harvested and extruded into liquid nitrogen. In all cases the cells were then cryogenically milled (LaCava et al., 2016) and used in affinity capture experiments and downstream assays. Custom computer code written in the R programming language was used in the analysis of mass spectrometry and RNA sequencing data; it has been published on https://bitbucket.org (Altukhov, 2017); a copy is archived at https://github.com/elifesciences-publications/altukhov-line-1.

All normalized affinity values, derived from H/(H + L) and L/(H + L) isotopic ratios, can be found in Supplementary file 1 on the appropriate sheet; pre-normalization values are located on the sheets named ‘Integration’ and ‘Raw.’

Modified SILAC strategy

Appendix 1—figure 1

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RNase sensitivity affinity capture

Data normalization

The RNase sensitivity data were rescaled and normalized such that proteins that did not change upon treatment with RNases were centered at the origin and those that were completely sensitive would give a value of 1.0. In a perfect experiment, unchanging proteins would yield a ratio of 0.5 when comparing the fraction of each protein present in the BSA-treated sample to the sum of both the BSA- and RNase-treated samples; i.e. 1 / (1 + 1). However, our data show some variability (below, left and also Supplementary file 1), with one replicate centering on ~0.4 (red) and another ~0.6 (blue). Therefore, we normalized the data such that the peaks at ~0.4 and~0.6 were both re-centered at 0.5. From this set, 0.5 was subtracted from the data (centering insensitive proteins at the origin, and completely sensitive proteins at 0.5), followed by multiplication by two to expand the data to cover the range from 0 (insensitive) to 1 (completely sensitive); depicted below, right. These latter two transformations are encompassed by the functions: g(x)=x + b [where b = −0.5] and f(g(x))=a(x + b) [where a = 2].

Appendix 1—figure 2

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RNase normality test The distances from the (0,0) point to protein coordinates were calculated. Proteins with distance less than two median distances were selected. The Shapiro-Wilk normality test (the null-hypothesis of this test is that the population is normally distributed) was applied for the distances (p-value=0.29). The distribution of the distances was plotted as a histogram displaying the frequency (y-axis) versus RNase sensitivity (x-axis) of a simulation of normally distributed data (shown in black) and the actual data (Supplementary file 1) shown in blue. A Q-Q plot was also drawn.

Appendix 1—figure 3

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Split-tandem affinity capture

Data normalization The data were treated as follows: a and b coefficients were calculated as solutions of equation 1; the normalized values were calculated using the equation 2.

(1) ${\displaystyle \left(\begin{array}{c}\mathrm{a}\\ \mathrm{b}\end{array}\right)\left(\begin{array}{cc}\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}& 1\\ \mathrm{O}\mathrm{R}\mathrm{F}1& 1\end{array}\right)=\left(\begin{array}{c}\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}\\ 1\end{array}\right)}$

(2) ${\displaystyle {\displaystyle {\mathrm{x}}_{{\displaystyle \mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}}={\displaystyle ({\mathrm{a}}^{\ast }{\mathrm{x}}_{{\displaystyle \mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l}}})+\mathrm{b}}}}$

Appendix 1—figure 4

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Calculate the distances between node pairs

Distance between two points A and B with coordinates (A_x, A_y) and (B_x, B_y) was calculated as: $\sqrt{{({\mathit{A}}_{\mathit{x}}-{\mathit{B}}_{\mathit{x}})}^2+\mathit{}{({\mathit{A}}_{\mathit{y}}-{\mathit{B}}_{\mathit{y}})}^2}$

For each three points, the mean paired distance was calculated. The distributions of mean values are presented in the histograms below.

Appendix 1—figure 5

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Appendix 1—figure 6

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Associated likelihoods of selected clusters

Here, likelihood is defined as the frequency with which the same mean distance or less is observed within the distribution of clusters with the same number of nodes (above).

PURA/PURB/PCNA: Likelihood = 3.2 × 10⁻⁷

PABPC1/PABPC4: Likelihood = 0.0008388427

HSPA8/HSPA1A: Likelihood = 0.0001991309

NAP1L1/IPO7: Likelihood = 0.0075885198

Efficacy elution from α-ORF1 4H1 affinity medium using ORF1p peptides

Appendix 1—figure 7

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ORF1p-FLAG was purified from 25 mg of cryo-milled HEK-293T_LD expressing pLD288 using α-ORF1 affinity medium, essentially as previously described (Taylor et al., 2013), and then eluted either eluted directly with 15 μl of 1x LDS, 70°C for 5 min (Ctrl LDS), with 2 mM monomeric ORF1 peptide (Mono pep), or 2 mM dimeric ORF1 peptide (Di pep) (in both cases for 15 min at room temperature). After elution with peptide, the affinity medium was further eluted with 1x LDS at 70°C for 5 min (Mono and Di LDS, respectively).

Retrotransposition mutants affinity capture

The distributions of normalized affinities for the two sets of experiments are shown below.

Appendix 1—figure 8

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Protein in vitro exchange

The distributions of H/(H + L) values present at each time point are shown.

Appendix 1—figure 9

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1. 1. John LaCava

   (2017) Dissection of affinity captured LINE-1 macromolecular complexes

   Publicly available at ProteomeXchange (accession no: PXD008542).

   http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD008542
2. 1. John LaCava

   (2017) RNAs associated with affinity captured LINE-1 ribonucleoproteins

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE108270).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108270

1. 1. Alisch RS
   2. Garcia-Perez JL
   3. Muotri AR
   4. Gage FH
   5. Moran JV

   (2006) Unconventional translation of mammalian LINE-1 retrotransposons

   Genes & Development 20:210–224.

   https://doi.org/10.1101/gad.1380406

   • PubMed
   • Google Scholar
2. Software

   1. Altukhov I

   (2017) LINE-1, version 1e90c62

   Bitbucket.

   https://bitbucket.org/altukhov/line-1/
3. 1. An W
   2. Dai L
   3. Niewiadomska AM
   4. Yetil A
   5. O'Donnell KA
   6. Han JS
   7. Boeke JD

   (2011) Characterization of a synthetic human LINE-1 retrotransposon ORFeus-Hs

   Mobile DNA 2:2.

   https://doi.org/10.1186/1759-8753-2-2

   • PubMed
   • Google Scholar
4. 1. Arjan-Odedra S
   2. Swanson CM
   3. Sherer NM
   4. Wolinsky SM
   5. Malim MH

   (2012) Endogenous MOV10 inhibits the retrotransposition of endogenous retroelements but not the replication of exogenous retroviruses

   Retrovirology 9:53.

   https://doi.org/10.1186/1742-4690-9-53

   • PubMed
   • Google Scholar
5. 1. Arlander SJ
   2. Eapen AK
   3. Vroman BT
   4. McDonald RJ
   5. Toft DO
   6. Karnitz LM

   (2003) Hsp90 inhibition depletes Chk1 and sensitizes tumor cells to replication stress

   Journal of Biological Chemistry 278:52572–52577.

   https://doi.org/10.1074/jbc.M309054200

   • PubMed
   • Google Scholar
6. 1. Azzalin CM
   2. Lingner J

   (2006) The human RNA surveillance factor UPF1 is required for S phase progression and genome stability

   Current Biology 16:433–439.

   https://doi.org/10.1016/j.cub.2006.01.018

   • PubMed
   • Google Scholar
7. 1. Baltz AG
   2. Munschauer M
   3. Schwanhäusser B
   4. Vasile A
   5. Murakawa Y
   6. Schueler M
   7. Youngs N
   8. Penfold-Brown D
   9. Drew K
   10. Milek M
   11. Wyler E
   12. Bonneau R
   13. Selbach M
   14. Dieterich C
   15. Landthaler M

   (2012) The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts

   Molecular Cell 46:674–690.

   https://doi.org/10.1016/j.molcel.2012.05.021

   • PubMed
   • Google Scholar
8. 1. Beauregard A
   2. Curcio MJ
   3. Belfort M

   (2008) The take and give between retrotransposable elements and their hosts

   Annual Review of Genetics 42:587–617.

   https://doi.org/10.1146/annurev.genet.42.110807.091549

   • PubMed
   • Google Scholar
9. 1. Belancio VP
   2. Hedges DJ
   3. Deininger P

   (2006) LINE-1 RNA splicing and influences on mammalian gene expression

   Nucleic Acids Research 34:1512–1521.

   https://doi.org/10.1093/nar/gkl027

   • PubMed
   • Google Scholar
10. 1. Bley N
    2. Lederer M
    3. Pfalz B
    4. Reinke C
    5. Fuchs T
    6. Glaß M
    7. Möller B
    8. Hüttelmaier S

    (2015) Stress granules are dispensable for mRNA stabilization during cellular stress

    Nucleic Acids Research 43:e26.

    https://doi.org/10.1093/nar/gku1275

    • PubMed
    • Google Scholar
11. 1. Bolger AM
    2. Lohse M
    3. Usadel B

    (2014) Trimmomatic: a flexible trimmer for Illumina sequence data

    Bioinformatics 30:2114–2120.

    https://doi.org/10.1093/bioinformatics/btu170

    • PubMed
    • Google Scholar
12. 1. Bryant HE
    2. Petermann E
    3. Schultz N
    4. Jemth AS
    5. Loseva O
    6. Issaeva N
    7. Johansson F
    8. Fernandez S
    9. McGlynn P
    10. Helleday T

    (2009) PARP is activated at stalled forks to mediate Mre11-dependent replication restart and recombination

    The EMBO Journal 28:2601–2615.

    https://doi.org/10.1038/emboj.2009.206

    • PubMed
    • Google Scholar
13. 1. Budiman ME
    2. Bubenik JL
    3. Miniard AC
    4. Middleton LM
    5. Gerber CA
    6. Cash A
    7. Driscoll DM

    (2009) Eukaryotic initiation factor 4a3 is a selenium-regulated RNA-binding protein that selectively inhibits selenocysteine incorporation

    Molecular Cell 35:479–489.

    https://doi.org/10.1016/j.molcel.2009.06.026

    • PubMed
    • Google Scholar
14. 1. Byrum S
    2. Mackintosh SG
    3. Edmondson RD
    4. Cheung WL
    5. Taverna SD
    6. Tackett AJ

    (2011) Analysis of Histone Exchange during Chromatin Purification

    Journal of integrated OMICS 1:61–65.

    https://doi.org/10.5584/jiomics.v1i1.26

    • PubMed
    • Google Scholar
15. 1. Candiano G
    2. Bruschi M
    3. Musante L
    4. Santucci L
    5. Ghiggeri GM
    6. Carnemolla B
    7. Orecchia P
    8. Zardi L
    9. Righetti PG

    (2004) Blue silver: a very sensitive colloidal Coomassie G-250 staining for proteome analysis

    Electrophoresis 25:1327–1333.

    https://doi.org/10.1002/elps.200305844

    • PubMed
    • Google Scholar
16. 1. Caspary F
    2. Shevchenko A
    3. Wilm M
    4. Séraphin B

    (1999) Partial purification of the yeast U2 snRNP reveals a novel yeast pre-mRNA splicing factor required for pre-spliceosome assembly

    The EMBO Journal 18:3463–3474.

    https://doi.org/10.1093/emboj/18.12.3463

    • PubMed
    • Google Scholar
17. 1. Castello A
    2. Fischer B
    3. Eichelbaum K
    4. Horos R
    5. Beckmann BM
    6. Strein C
    7. Davey NE
    8. Humphreys DT
    9. Preiss T
    10. Steinmetz LM
    11. Krijgsveld J
    12. Hentze MW

    (2012) Insights into RNA biology from an atlas of mammalian mRNA-binding proteins

    Cell 149:1393–1406.

    https://doi.org/10.1016/j.cell.2012.04.031

    • PubMed
    • Google Scholar
18. 1. Cost GJ
    2. Feng Q
    3. Jacquier A
    4. Boeke JD

    (2002) Human L1 element target-primed reverse transcription in vitro

    The EMBO Journal 21:5899–5910.

    https://doi.org/10.1093/emboj/cdf592

    • PubMed
    • Google Scholar
19. 1. Cox J
    2. Mann M

    (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification

    Nature Biotechnology 26:1367–1372.

    https://doi.org/10.1038/nbt.1511

    • PubMed
    • Google Scholar
20. 1. Dai L
    2. LaCava J
    3. Taylor MS
    4. Boeke JD

    (2014) Expression and detection of LINE-1 ORF-encoded proteins

    Mobile Genetic Elements 4:e29319.

    https://doi.org/10.4161/mge.29319

    • PubMed
    • Google Scholar
21. 1. Dai L
    2. Taylor MS
    3. O'Donnell KA
    4. Boeke JD

    (2012) Poly(A) binding protein C1 is essential for efficient L1 retrotransposition and affects L1 RNP formation

    Molecular and Cellular Biology 32:4323–4336.

    https://doi.org/10.1128/MCB.06785-11

    • PubMed
    • Google Scholar
22. 1. Denli AM
    2. Narvaiza I
    3. Kerman BE
    4. Pena M
    5. Benner C
    6. Marchetto MC
    7. Diedrich JK
    8. Aslanian A
    9. Ma J
    10. Moresco JJ
    11. Moore L
    12. Hunter T
    13. Saghatelian A
    14. Gage FH

    (2015) Primate-specific ORF0 contributes to retrotransposon-mediated diversity

    Cell 163:583–593.

    https://doi.org/10.1016/j.cell.2015.09.025

    • PubMed
    • Google Scholar
23. 1. Dewannieux M
    2. Esnault C
    3. Heidmann T

    (2003) LINE-mediated retrotransposition of marked Alu sequences

    Nature Genetics 35:41–48.

    https://doi.org/10.1038/ng1223

    • PubMed
    • Google Scholar
24. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
25. 1. Doucet AJ
    2. Hulme AE
    3. Sahinovic E
    4. Kulpa DA
    5. Moldovan JB
    6. Kopera HC
    7. Athanikar JN
    8. Hasnaoui M
    9. Bucheton A
    10. Moran JV
    11. Gilbert N

    (2010) Characterization of LINE-1 ribonucleoprotein particles

    PLoS Genetics 6:e1001150–19.

    https://doi.org/10.1371/journal.pgen.1001150

    • PubMed
    • Google Scholar
26. 1. Doucet AJ
    2. Wilusz JE
    3. Miyoshi T
    4. Liu Y
    5. Moran JV

    (2015) A 3' Poly(A) Tract Is Required for LINE-1 Retrotransposition

    Molecular Cell 60:728–741.

    https://doi.org/10.1016/j.molcel.2015.10.012

    • PubMed
    • Google Scholar
27. 1. Engeland CE
    2. Brown NP
    3. Börner K
    4. Schümann M
    5. Krause E
    6. Kaderali L
    7. Müller GA
    8. Kräusslich HG

    (2014) Proteome analysis of the HIV-1 Gag interactome

    Virology 460-461:194–206.

    https://doi.org/10.1016/j.virol.2014.04.038

    • PubMed
    • Google Scholar
28. 1. Fassati A
    2. Görlich D
    3. Harrison I
    4. Zaytseva L
    5. Mingot JM

    (2003) Nuclear import of HIV-1 intracellular reverse transcription complexes is mediated by importin 7

    The EMBO Journal 22:3675–3685.

    https://doi.org/10.1093/emboj/cdg357

    • PubMed
    • Google Scholar
29. 1. Feng Q
    2. Moran JV
    3. Kazazian HH
    4. Boeke JD

    (1996) Human L1 retrotransposon encodes a conserved endonuclease required for retrotransposition

    Cell 87:905–916.

    https://doi.org/10.1016/S0092-8674(00)81997-2

    • PubMed
    • Google Scholar
30. 1. Furano AV
    2. Duvernell DD
    3. Boissinot S

    (2004) L1 (LINE-1) retrotransposon diversity differs dramatically between mammals and fish

    Trends in Genetics 20:9–14.

    https://doi.org/10.1016/j.tig.2003.11.006

    • PubMed
    • Google Scholar
31. 1. Geiger T
    2. Wisniewski JR
    3. Cox J
    4. Zanivan S
    5. Kruger M
    6. Ishihama Y
    7. Mann M

    (2011) Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics

    Nature Protocols 6:147–157.

    https://doi.org/10.1038/nprot.2010.192

    • PubMed
    • Google Scholar
32. 1. Gonzalez IL
    2. Gorski JL
    3. Campen TJ
    4. Dorney DJ
    5. Erickson JM
    6. Sylvester JE
    7. Schmickel RD

    (1985) Variation among human 28S ribosomal RNA genes

    PNAS 82:7666–7670.

    https://doi.org/10.1073/pnas.82.22.7666

    • PubMed
    • Google Scholar
33. 1. Goodier JL
    2. Cheung LE
    3. Kazazian HH

    (2012) MOV10 RNA helicase is a potent inhibitor of retrotransposition in cells

    PLoS Genetics 8:e1002941.

    https://doi.org/10.1371/journal.pgen.1002941

    • PubMed
    • Google Scholar
34. 1. Goodier JL
    2. Cheung LE
    3. Kazazian HH

    (2013) Mapping the LINE1 ORF1 protein interactome reveals associated inhibitors of human retrotransposition

    Nucleic Acids Research 41:7401–7419.

    https://doi.org/10.1093/nar/gkt512

    • Google Scholar
35. 1. Goodier JL
    2. Mandal PK
    3. Zhang L
    4. Kazazian HH

    (2010) Discrete subcellular partitioning of human retrotransposon RNAs despite a common mechanism of genome insertion

    Human Molecular Genetics 19:1712–1725.

    https://doi.org/10.1093/hmg/ddq048

    • PubMed
    • Google Scholar
36. 1. Goodier JL
    2. Zhang L
    3. Vetter MR
    4. Kazazian HH

    (2007) LINE-1 ORF1 protein localizes in stress granules with other RNA-binding proteins, including components of RNA interference RNA-induced silencing complex

    Molecular and Cellular Biology 27:6469–6483.

    https://doi.org/10.1128/MCB.00332-07

    • PubMed
    • Google Scholar
37. 1. Gregersen LH
    2. Schueler M
    3. Munschauer M
    4. Mastrobuoni G
    5. Chen W
    6. Kempa S
    7. Dieterich C
    8. Landthaler M

    (2014) MOV10 Is a 5' to 3' RNA helicase contributing to UPF1 mRNA target degradation by translocation along 3' UTRs

    Molecular Cell 54:573–585.

    https://doi.org/10.1016/j.molcel.2014.03.017

    • PubMed
    • Google Scholar
38. 1. Ha K
    2. Fiskus W
    3. Rao R
    4. Balusu R
    5. Venkannagari S
    6. Nalabothula NR
    7. Bhalla KN

    (2011) Hsp90 inhibitor-mediated disruption of chaperone association of ATR with hsp90 sensitizes cancer cells to DNA damage

    Molecular Cancer Therapeutics 10:1194–1206.

    https://doi.org/10.1158/1535-7163.MCT-11-0094

    • PubMed
    • Google Scholar
39. 1. Hakhverdyan Z
    2. Domanski M
    3. Hough LE
    4. Oroskar AA
    5. Oroskar AR
    6. Keegan S
    7. Dilworth DJ
    8. Molloy KR
    9. Sherman V
    10. Aitchison JD
    11. Fenyö D
    12. Chait BT
    13. Jensen TH
    14. Rout MP
    15. LaCava J

    (2015) Rapid, optimized interactomic screening

    Nature Methods 12:553–560.

    https://doi.org/10.1038/nmeth.3395

    • PubMed
    • Google Scholar
40. 1. Halász L
    2. Karányi Z
    3. Boros-Oláh B
    4. Kuik-Rózsa T
    5. Sipos É
    6. Nagy É
    7. Mosolygó-L Á
    8. Mázló A
    9. Rajnavölgyi É
    10. Halmos G
    11. Székvölgyi L

    (2017) RNA-DNA hybrid (R-loop) immunoprecipitation mapping: an analytical workflow to evaluate inherent biases

    Genome Research 27:1063–1073.

    https://doi.org/10.1101/gr.219394.116

    • PubMed
    • Google Scholar
41. 1. Hampf M
    2. Gossen M

    (2007) Promoter crosstalk effects on gene expression

    Journal of Molecular Biology 365:911–920.

    https://doi.org/10.1016/j.jmb.2006.10.009

    • PubMed
    • Google Scholar
42. 1. Havugimana PC
    2. Hart GT
    3. Nepusz T
    4. Yang H
    5. Turinsky AL
    6. Li Z
    7. Wang PI
    8. Boutz DR
    9. Fong V
    10. Phanse S
    11. Babu M
    12. Craig SA
    13. Hu P
    14. Wan C
    15. Vlasblom J
    16. Dar VU
    17. Bezginov A
    18. Clark GW
    19. Wu GC
    20. Wodak SJ
    21. Tillier ER
    22. Paccanaro A
    23. Marcotte EM
    24. Emili A

    (2012) A census of human soluble protein complexes

    Cell 150:1068–1081.

    https://doi.org/10.1016/j.cell.2012.08.011

    • PubMed
    • Google Scholar
43. 1. Hohjoh H
    2. Singer MF

    (1996)

    Cytoplasmic ribonucleoprotein complexes containing human LINE-1 protein and RNA

    The EMBO journal 15:630–639.

    • PubMed
    • Google Scholar
44. 1. Jain S
    2. Wheeler JR
    3. Walters RW
    4. Agrawal A
    5. Barsic A
    6. Parker R

    (2016) ATPase-Modulated Stress Granules Contain a Diverse Proteome and Substructure

    Cell 164:487–498.

    https://doi.org/10.1016/j.cell.2015.12.038

    • PubMed
    • Google Scholar
45. 1. Joshi P
    2. Greco TM
    3. Guise AJ
    4. Luo Y
    5. Yu F
    6. Nesvizhskii AI
    7. Cristea IM

    (2013) The functional interactome landscape of the human histone deacetylase family

    Molecular Systems Biology 9:672.

    https://doi.org/10.1038/msb.2013.26

    • PubMed
    • Google Scholar
46. 1. Jønson L
    2. Vikesaa J
    3. Krogh A
    4. Nielsen LK
    5. Hansen T
    6. Borup R
    7. Johnsen AH
    8. Christiansen J
    9. Nielsen FC

    (2007) Molecular composition of IMP1 ribonucleoprotein granules

    Molecular & Cellular Proteomics 6:798–811.

    https://doi.org/10.1074/mcp.M600346-MCP200

    • PubMed
    • Google Scholar
47. 1. Kaake RM
    2. Wang X
    3. Huang L

    (2010) Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry

    Molecular & Cellular Proteomics 9:1650–1665.

    https://doi.org/10.1074/mcp.R110.000265

    • PubMed
    • Google Scholar
48. Book

    1. Kates SA
    2. Albericio F

    (2000)

    Solid-Phase Synthesis

    CRC Press.

    • Google Scholar
49. 1. Katzenellenbogen RA
    2. Egelkrout EM
    3. Vliet-Gregg P
    4. Gewin LC
    5. Gafken PR
    6. Galloway DA

    (2007) NFX1-123 and poly(A) binding proteins synergistically augment activation of telomerase in human papillomavirus type 16 E6-expressing cells

    Journal of Virology 81:3786–3796.

    https://doi.org/10.1128/JVI.02007-06

    • PubMed
    • Google Scholar
50. 1. Kazazian HH

    (2004) Mobile elements: drivers of genome evolution

    Science 303:1626–1632.

    https://doi.org/10.1126/science.1089670

    • PubMed
    • Google Scholar
51. 1. Kelm RJ
    2. Cogan JG
    3. Elder PK
    4. Strauch AR
    5. Getz MJ

    (1999) Molecular interactions between single-stranded DNA-binding proteins associated with an essential MCAT element in the mouse smooth muscle alpha-actin promoter

    Journal of Biological Chemistry 274:14238–14245.

    https://doi.org/10.1074/jbc.274.20.14238

    • PubMed
    • Google Scholar
52. 1. Khazina E
    2. Truffault V
    3. Büttner R
    4. Schmidt S
    5. Coles M
    6. Weichenrieder O

    (2011) Trimeric structure and flexibility of the L1ORF1 protein in human L1 retrotransposition

    Nature Structural & Molecular Biology 18:1006–1014.

    https://doi.org/10.1038/nsmb.2097

    • PubMed
    • Google Scholar
53. 1. Kimberland ML
    2. Divoky V
    3. Prchal J
    4. Schwahn U
    5. Berger W
    6. Kazazian HH

    (1999) Full-length human L1 insertions retain the capacity for high frequency retrotransposition in cultured cells

    Human Molecular Genetics 8:1557–1560.

    https://doi.org/10.1093/hmg/8.8.1557

    • PubMed
    • Google Scholar
54. 1. Knapp AM
    2. Ramsey JE
    3. Wang SX
    4. Godburn KE
    5. Strauch AR
    6. Kelm RJ

    (2006) Nucleoprotein interactions governing cell type-dependent repression of the mouse smooth muscle alpha-actin promoter by single-stranded DNA-binding proteins Pur alpha and Pur beta

    Journal of Biological Chemistry 281:7907–7918.

    https://doi.org/10.1074/jbc.M509682200

    • PubMed
    • Google Scholar
55. 1. Kubota T
    2. Nishimura K
    3. Kanemaki MT
    4. Donaldson AD

    (2013) The Elg1 replication factor C-like complex functions in PCNA unloading during DNA replication

    Molecular Cell 50:273–280.

    https://doi.org/10.1016/j.molcel.2013.02.012

    • PubMed
    • Google Scholar
56. 1. Kulpa DA
    2. Moran JV

    (2005) Ribonucleoprotein particle formation is necessary but not sufficient for LINE-1 retrotransposition

    Human Molecular Genetics 14:3237–3248.

    https://doi.org/10.1093/hmg/ddi354

    • PubMed
    • Google Scholar
57. 1. Kulpa DA
    2. Moran JV

    (2006) Cis-preferential LINE-1 reverse transcriptase activity in ribonucleoprotein particles

    Nature Structural & Molecular Biology 13:655–660.

    https://doi.org/10.1038/nsmb1107

    • PubMed
    • Google Scholar
58. 1. LaCava J
    2. Jiang H
    3. Rout MP

    (2016) Protein complex affinity capture from cryomilled mammalian cells

    Journal of Visualized Experiments e54518.

    https://doi.org/10.3791/54518

    • PubMed
    • Google Scholar
59. 1. Lander ES
    2. Linton LM
    3. Birren B
    4. Nusbaum C
    5. Zody MC
    6. Baldwin J
    7. Devon K
    8. Dewar K
    9. Doyle M
    10. FitzHugh W
    11. Funke R
    12. Gage D
    13. Harris K
    14. Heaford A
    15. Howland J
    16. Kann L
    17. Lehoczky J
    18. LeVine R
    19. McEwan P
    20. McKernan K
    21. Meldrim J
    22. Mesirov JP
    23. Miranda C
    24. Morris W
    25. Naylor J
    26. Raymond C
    27. Rosetti M
    28. Santos R
    29. Sheridan A
    30. Sougnez C
    31. Stange-Thomann Y
    32. Stojanovic N
    33. Subramanian A
    34. Wyman D
    35. Rogers J
    36. Sulston J
    37. Ainscough R
    38. Beck S
    39. Bentley D
    40. Burton J
    41. Clee C
    42. Carter N
    43. Coulson A
    44. Deadman R
    45. Deloukas P
    46. Dunham A
    47. Dunham I
    48. Durbin R
    49. French L
    50. Grafham D
    51. Gregory S
    52. Hubbard T
    53. Humphray S
    54. Hunt A
    55. Jones M
    56. Lloyd C
    57. McMurray A
    58. Matthews L
    59. Mercer S
    60. Milne S
    61. Mullikin JC
    62. Mungall A
    63. Plumb R
    64. Ross M
    65. Shownkeen R
    66. Sims S
    67. Waterston RH
    68. Wilson RK
    69. Hillier LW
    70. McPherson JD
    71. Marra MA
    72. Mardis ER
    73. Fulton LA
    74. Chinwalla AT
    75. Pepin KH
    76. Gish WR
    77. Chissoe SL
    78. Wendl MC
    79. Delehaunty KD
    80. Miner TL
    81. Delehaunty A
    82. Kramer JB
    83. Cook LL
    84. Fulton RS
    85. Johnson DL
    86. Minx PJ
    87. Clifton SW
    88. Hawkins T
    89. Branscomb E
    90. Predki P
    91. Richardson P
    92. Wenning S
    93. Slezak T
    94. Doggett N
    95. Cheng JF
    96. Olsen A
    97. Lucas S
    98. Elkin C
    99. Uberbacher E
    100. Frazier M
    101. Gibbs RA
    102. Muzny DM
    103. Scherer SE
    104. Bouck JB
    105. Sodergren EJ
    106. Worley KC
    107. Rives CM
    108. Gorrell JH
    109. Metzker ML
    110. Naylor SL
    111. Kucherlapati RS
    112. Nelson DL
    113. Weinstock GM
    114. Sakaki Y
    115. Fujiyama A
    116. Hattori M
    117. Yada T
    118. Toyoda A
    119. Itoh T
    120. Kawagoe C
    121. Watanabe H
    122. Totoki Y
    123. Taylor T
    124. Weissenbach J
    125. Heilig R
    126. Saurin W
    127. Artiguenave F
    128. Brottier P
    129. Bruls T
    130. Pelletier E
    131. Robert C
    132. Wincker P
    133. Smith DR
    134. Doucette-Stamm L
    135. Rubenfield M
    136. Weinstock K
    137. Lee HM
    138. Dubois J
    139. Rosenthal A
    140. Platzer M
    141. Nyakatura G
    142. Taudien S
    143. Rump A
    144. Yang H
    145. Yu J
    146. Wang J
    147. Huang G
    148. Gu J
    149. Hood L
    150. Rowen L
    151. Madan A
    152. Qin S
    153. Davis RW
    154. Federspiel NA
    155. Abola AP
    156. Proctor MJ
    157. Myers RM
    158. Schmutz J
    159. Dickson M
    160. Grimwood J
    161. Cox DR
    162. Olson MV
    163. Kaul R
    164. Raymond C
    165. Shimizu N
    166. Kawasaki K
    167. Minoshima S
    168. Evans GA
    169. Athanasiou M
    170. Schultz R
    171. Roe BA
    172. Chen F
    173. Pan H
    174. Ramser J
    175. Lehrach H
    176. Reinhardt R
    177. McCombie WR
    178. de la Bastide M
    179. Dedhia N
    180. Blöcker H
    181. Hornischer K
    182. Nordsiek G
    183. Agarwala R
    184. Aravind L
    185. Bailey JA
    186. Bateman A
    187. Batzoglou S
    188. Birney E
    189. Bork P
    190. Brown DG
    191. Burge CB
    192. Cerutti L
    193. Chen HC
    194. Church D
    195. Clamp M
    196. Copley RR
    197. Doerks T
    198. Eddy SR
    199. Eichler EE
    200. Furey TS
    201. Galagan J
    202. Gilbert JG
    203. Harmon C
    204. Hayashizaki Y
    205. Haussler D
    206. Hermjakob H
    207. Hokamp K
    208. Jang W
    209. Johnson LS
    210. Jones TA
    211. Kasif S
    212. Kaspryzk A
    213. Kennedy S
    214. Kent WJ
    215. Kitts P
    216. Koonin EV
    217. Korf I
    218. Kulp D
    219. Lancet D
    220. Lowe TM
    221. McLysaght A
    222. Mikkelsen T
    223. Moran JV
    224. Mulder N
    225. Pollara VJ
    226. Ponting CP
    227. Schuler G
    228. Schultz J
    229. Slater G
    230. Smit AF
    231. Stupka E
    232. Szustakowki J
    233. Thierry-Mieg D
    234. Thierry-Mieg J
    235. Wagner L
    236. Wallis J
    237. Wheeler R
    238. Williams A
    239. Wolf YI
    240. Wolfe KH
    241. Yang SP
    242. Yeh RF
    243. Collins F
    244. Guyer MS
    245. Peterson J
    246. Felsenfeld A
    247. Wetterstrand KA
    248. Patrinos A
    249. Morgan MJ
    250. de Jong P
    251. Catanese JJ
    252. Osoegawa K
    253. Shizuya H
    254. Choi S
    255. Chen YJ
    256. Szustakowki J
    257. International Human Genome Sequencing Consortium

    (2001) Initial sequencing and analysis of the human genome

    Nature 409:860–921.

    https://doi.org/10.1038/35057062

    • PubMed
    • Google Scholar
60. 1. Lee E
    2. Iskow R
    3. Yang L
    4. Gokcumen O
    5. Haseley P
    6. Luquette LJ
    7. Lohr JG
    8. Harris CC
    9. Ding L
    10. Wilson RK
    11. Wheeler DA
    12. Gibbs RA
    13. Kucherlapati R
    14. Lee C
    15. Kharchenko PV
    16. Park PJ
    17. Network C
    18. Cancer Genome Atlas Research Network

    (2012) Landscape of somatic retrotransposition in human cancers

    Science 337:967–971.

    https://doi.org/10.1126/science.1222077

    • PubMed
    • Google Scholar
61. 1. Li Q
    2. Brass AL
    3. Ng A
    4. Hu Z
    5. Xavier RJ
    6. Liang TJ
    7. Elledge SJ

    (2009) A genome-wide genetic screen for host factors required for hepatitis C virus propagation

    PNAS 106:16410–16415.

    https://doi.org/10.1073/pnas.0907439106

    • PubMed
    • Google Scholar
62. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
63. 1. Luan DD
    2. Korman MH
    3. Jakubczak JL
    4. Eickbush TH

    (1993) Reverse transcription of R2Bm RNA is primed by a nick at the chromosomal target site: a mechanism for non-LTR retrotransposition

    Cell 72:595–605.

    https://doi.org/10.1016/0092-8674(93)90078-5

    • PubMed
    • Google Scholar
64. 1. Luo Y
    2. Jacobs EY
    3. Greco TM
    4. Mohammed KD
    5. Tong T
    6. Keegan S
    7. Binley JM
    8. Cristea IM
    9. Fenyö D
    10. Rout MP
    11. Chait BT
    12. Muesing MA

    (2016) HIV-host interactome revealed directly from infected cells

    Nature Microbiology 1:16068.

    https://doi.org/10.1038/nmicrobiol.2016.68

    • PubMed
    • Google Scholar
65. 1. Martin SL
    2. Bushman FD

    (2001) Nucleic acid chaperone activity of the ORF1 protein from the mouse LINE-1 retrotransposon

    Molecular and Cellular Biology 21:467–475.

    https://doi.org/10.1128/MCB.21.2.467-475.2001

    • PubMed
    • Google Scholar
66. 1. Martin SL

    (1991) Ribonucleoprotein particles with LINE-1 RNA in mouse embryonal carcinoma cells

    Molecular and Cellular Biology 11:4804–4807.

    https://doi.org/10.1128/MCB.11.9.4804

    • PubMed
    • Google Scholar
67. 1. Mathias SL
    2. Scott AF
    3. Kazazian HH
    4. Boeke JD
    5. Gabriel A

    (1991) Reverse transcriptase encoded by a human transposable element

    Science 254:1808–1810.

    https://doi.org/10.1126/science.1722352

    • PubMed
    • Google Scholar
68. 1. Mi H
    2. Huang X
    3. Muruganujan A
    4. Tang H
    5. Mills C
    6. Kang D
    7. Thomas PD

    (2017) PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements

    Nucleic Acids Research 45:D183.

    https://doi.org/10.1093/nar/gkw1138

    • PubMed
    • Google Scholar
69. 1. Mi H
    2. Muruganujan A
    3. Casagrande JT
    4. Thomas PD

    (2013) Large-scale gene function analysis with the PANTHER classification system

    Nature Protocols 8:1551–1566.

    https://doi.org/10.1038/nprot.2013.092

    • PubMed
    • Google Scholar
70. 1. Min W
    2. Bruhn C
    3. Grigaravicius P
    4. Zhou ZW
    5. Li F
    6. Krüger A
    7. Siddeek B
    8. Greulich KO
    9. Popp O
    10. Meisezahl C
    11. Calkhoven CF
    12. Bürkle A
    13. Xu X
    14. Wang ZQ

    (2013) Poly(ADP-ribose) binding to Chk1 at stalled replication forks is required for S-phase checkpoint activation

    Nature Communications 4:2993.

    https://doi.org/10.1038/ncomms3993

    • PubMed
    • Google Scholar
71. 1. Mita P
    2. Wudzinska A
    3. Sun X
    4. Andrade J
    5. Nayak S
    6. Kahler DJ
    7. Badri S
    8. LaCava J
    9. Ueberheide B
    10. Yun CY
    11. Fenyö D
    12. Boeke JD

    (2018) LINE-1 protein localization and functional dynamics during the cell cycle

    eLife 7:e30058.

    https://doi.org/10.7554/eLife.30058

    • PubMed
    • Google Scholar
72. 1. Mittler G
    2. Butter F
    3. Mann M

    (2009) A SILAC-based DNA protein interaction screen that identifies candidate binding proteins to functional DNA elements

    Genome Research 19:284–293.

    https://doi.org/10.1101/gr.081711.108

    • PubMed
    • Google Scholar
73. 1. Muotri AR
    2. Chu VT
    3. Marchetto MC
    4. Deng W
    5. Moran JV
    6. Gage FH

    (2005) Somatic mosaicism in neuronal precursor cells mediated by L1 retrotransposition

    Nature 435:903–910.

    https://doi.org/10.1038/nature03663

    • PubMed
    • Google Scholar
74. 1. Nellist M
    2. Burgers PC
    3. van den Ouweland AM
    4. Halley DJ
    5. Luider TM

    (2005) Phosphorylation and binding partner analysis of the TSC1-TSC2 complex

    Biochemical and Biophysical Research Communications 333:818–826.

    https://doi.org/10.1016/j.bbrc.2005.05.175

    • PubMed
    • Google Scholar
75. 1. Niewiadomska AM
    2. Tian C
    3. Tan L
    4. Wang T
    5. Sarkis PT
    6. Yu XF
    7. Xf Y

    (2007) Differential inhibition of long interspersed element 1 by APOBEC3 does not correlate with high-molecular-mass-complex formation or P-body association

    Journal of Virology 81:9577–9583.

    https://doi.org/10.1128/JVI.02800-06

    • PubMed
    • Google Scholar
76. 1. Oda Y
    2. Huang K
    3. Cross FR
    4. Cowburn D
    5. Chait BT

    (1999) Accurate quantitation of protein expression and site-specific phosphorylation

    PNAS 96:6591–6596.

    https://doi.org/10.1073/pnas.96.12.6591

    • PubMed
    • Google Scholar
77. 1. Ohta S
    2. Bukowski-Wills JC
    3. Sanchez-Pulido L
    4. Alves FL
    5. Wood L
    6. Chen ZA
    7. Platani M
    8. Fischer L
    9. Hudson DF
    10. Ponting CP
    11. Fukagawa T
    12. Earnshaw WC
    13. Rappsilber J

    (2010) The protein composition of mitotic chromosomes determined using multiclassifier combinatorial proteomics

    Cell 142:810–821.

    https://doi.org/10.1016/j.cell.2010.07.047

    • PubMed
    • Google Scholar
78. 1. Ong SE
    2. Blagoev B
    3. Kratchmarova I
    4. Kristensen DB
    5. Steen H
    6. Pandey A
    7. Mann M

    (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics

    Molecular & Cellular Proteomics 1:376–386.

    https://doi.org/10.1074/mcp.M200025-MCP200

    • PubMed
    • Google Scholar
79. 1. Ostertag EM
    2. Kazazian HH

    (2001) Biology of mammalian L1 retrotransposons

    Annual Review of Genetics 35:501–538.

    https://doi.org/10.1146/annurev.genet.35.102401.091032

    • PubMed
    • Google Scholar
80. 1. Ostertag EM
    2. Prak ET
    3. DeBerardinis RJ
    4. Moran JV
    5. Kazazian HH

    (2000) Determination of L1 retrotransposition kinetics in cultured cells

    Nucleic Acids Research 28:1418–1423.

    https://doi.org/10.1093/nar/28.6.1418

    • PubMed
    • Google Scholar
81. 1. Peattie DA
    2. Harding MW
    3. Fleming MA
    4. DeCenzo MT
    5. Lippke JA
    6. Livingston DJ
    7. Benasutti M

    (1992) Expression and characterization of human FKBP52, an immunophilin that associates with the 90-kDa heat shock protein and is a component of steroid receptor complexes

    PNAS 89:10974–10978.

    https://doi.org/10.1073/pnas.89.22.10974

    • PubMed
    • Google Scholar
82. 1. Peddigari S
    2. Li PW
    3. Rabe JL
    4. Martin SL

    (2013) hnRNPL and nucleolin bind LINE-1 RNA and function as host factors to modulate retrotransposition

    Nucleic Acids Research 41:575–585.

    https://doi.org/10.1093/nar/gks1075

    • PubMed
    • Google Scholar
83. 1. Qin Y
    2. Ouyang H
    3. Liu J
    4. Xie Y

    (2013) Proteome identification of proteins interacting with histone methyltransferase SET8

    Acta Biochimica et Biophysica Sinica 45:303–308.

    https://doi.org/10.1093/abbs/gmt011

    • PubMed
    • Google Scholar
84. 1. Rappsilber J
    2. Mann M
    3. Ishihama Y

    (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips

    Nature Protocols 2:1896–1906.

    https://doi.org/10.1038/nprot.2007.261

    • PubMed
    • Google Scholar
85. 1. Rodriguez P
    2. Munroe D
    3. Prawitt D
    4. Chu LL
    5. Bric E
    6. Kim J
    7. Reid LH
    8. Davies C
    9. Nakagama H
    10. Loebbert R
    11. Winterpacht A
    12. Petruzzi MJ
    13. Higgins MJ
    14. Nowak N
    15. Evans G
    16. Shows T
    17. Weissman BE
    18. Zabel B
    19. Housman DE
    20. Pelletier J

    (1997) Functional characterization of human nucleosome assembly protein-2 (NAP1L4) suggests a role as a histone chaperone

    Genomics 44:253–265.

    https://doi.org/10.1006/geno.1997.4868

    • PubMed
    • Google Scholar
86. 1. Schweingruber C
    2. Rufener SC
    3. Zünd D
    4. Yamashita A
    5. Mühlemann O

    (2013) Nonsense-mediated mRNA decay - mechanisms of substrate mRNA recognition and degradation in mammalian cells

    Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1829:612–623.

    https://doi.org/10.1016/j.bbagrm.2013.02.005

    • PubMed
    • Google Scholar
87. 1. Shi X
    2. Seluanov A
    3. Gorbunova V

    (2007) Cell divisions are required for L1 retrotransposition

    Molecular and Cellular Biology 27:1264–1270.

    https://doi.org/10.1128/MCB.01888-06

    • PubMed
    • Google Scholar
88. 1. Simon HU
    2. Mills GB
    3. Kozlowski M
    4. Hogg D
    5. Branch D
    6. Ishimi Y
    7. Siminovitch KA

    (1994) Molecular characterization of hNRP, a cDNA encoding a human nucleosome-assembly-protein-I-related gene product involved in the induction of cell proliferation

    Biochemical Journal 297 (Pt 2):389–397.

    https://doi.org/10.1042/bj2970389

    • PubMed
    • Google Scholar
89. 1. Smart SK
    2. Mackintosh SG
    3. Edmondson RD
    4. Taverna SD
    5. Tackett AJ

    (2009) Mapping the local protein interactome of the NuA3 histone acetyltransferase

    Protein Science 18:1987–1997.

    https://doi.org/10.1002/pro.212

    • PubMed
    • Google Scholar
90. 1. Sommer SS
    2. Cohen JE

    (1980) The size distributions of proteins, mRNA, and nuclear RNA

    Journal of Molecular Evolution 15:37–57.

    https://doi.org/10.1007/BF01732582

    • PubMed
    • Google Scholar
91. 1. Suzuki J
    2. Yamaguchi K
    3. Kajikawa M
    4. Ichiyanagi K
    5. Adachi N
    6. Koyama H
    7. Takeda S
    8. Okada N

    (2009) Genetic evidence that the non-homologous end-joining repair pathway is involved in LINE retrotransposition

    PLoS Genetics 5:e1000461.

    https://doi.org/10.1371/journal.pgen.1000461

    • PubMed
    • Google Scholar
92. 1. Tackett AJ
    2. DeGrasse JA
    3. Sekedat MD
    4. Oeffinger M
    5. Rout MP
    6. Chait BT

    (2005) I-DIRT, a general method for distinguishing between specific and nonspecific protein interactions

    Journal of Proteome Research 4:1752–1756.

    https://doi.org/10.1021/pr050225e

    • PubMed
    • Google Scholar
93. 1. Takasaki Y
    2. Kogure T
    3. Takeuchi K
    4. Kaneda K
    5. Yano T
    6. Hirokawa K
    7. Hirose S
    8. Shirai T
    9. Hashimoto H

    (2001) Reactivity of anti-proliferating cell nuclear antigen (PCNA) murine monoclonal antibodies and human autoantibodies to the PCNA multiprotein complexes involved in cell proliferation

    The Journal of Immunology 166:4780–4787.

    https://doi.org/10.4049/jimmunol.166.7.4780

    • PubMed
    • Google Scholar
94. 1. Takizawa Y
    2. Binshtein E
    3. Erwin AL
    4. Pyburn TM
    5. Mittendorf KF
    6. Ohi MD

    (2017) While the revolution will not be crystallized, biochemistry reigns supreme

    Protein Science 26:69–81.

    https://doi.org/10.1002/pro.3054

    • PubMed
    • Google Scholar
95. 1. Taylor MS
    2. LaCava J
    3. Dai L
    4. Mita P
    5. Burns KH
    6. Rout MP
    7. Boeke JD

    (2016) Characterization of L1-Ribonucleoprotein Particles

    Methods in Molecular Biology 1400:311–338.

    https://doi.org/10.1007/978-1-4939-3372-3_20

    • PubMed
    • Google Scholar
96. 1. Taylor MS
    2. LaCava J
    3. Mita P
    4. Molloy KR
    5. Huang CR
    6. Li D
    7. Adney EM
    8. Jiang H
    9. Burns KH
    10. Chait BT
    11. Rout MP
    12. Boeke JD
    13. Dai L

    (2013) Affinity proteomics reveals human host factors implicated in discrete stages of LINE-1 retrotransposition

    Cell 155:1034–1048.

    https://doi.org/10.1016/j.cell.2013.10.021

    • PubMed
    • Google Scholar
97. 1. Trinkle-Mulcahy L
    2. Boulon S
    3. Lam YW
    4. Urcia R
    5. Boisvert FM
    6. Vandermoere F
    7. Morrice NA
    8. Swift S
    9. Rothbauer U
    10. Leonhardt H
    11. Lamond A

    (2008) Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes

    The Journal of Cell Biology 183:223–239.

    https://doi.org/10.1083/jcb.200805092

    • PubMed
    • Google Scholar
98. 1. Tubio JMC
    2. Li Y
    3. Ju YS
    4. Martincorena I
    5. Cooke SL
    6. Tojo M
    7. Gundem G
    8. Pipinikas CP
    9. Zamora J
    10. Raine K
    11. Menzies A
    12. Roman-Garcia P
    13. Fullam A
    14. Gerstung M
    15. Shlien A
    16. Tarpey PS
    17. Papaemmanuil E
    18. Knappskog S
    19. Van Loo P
    20. Ramakrishna M
    21. Davies HR
    22. Marshall J
    23. Wedge DC
    24. Teague JW
    25. Butler AP
    26. Nik-Zainal S
    27. Alexandrov L
    28. Behjati S
    29. Yates LR
    30. Bolli N
    31. Mudie L
    32. Hardy C
    33. Martin S
    34. McLaren S
    35. O'Meara S
    36. Anderson E
    37. Maddison M
    38. Gamble S
    39. Foster C
    40. Warren AY
    41. Whitaker H
    42. Brewer D
    43. Eeles R
    44. Cooper C
    45. Neal D
    46. Lynch AG
    47. Visakorpi T
    48. Isaacs WB
    49. Veer LV
    50. Caldas C
    51. Desmedt C
    52. Sotiriou C
    53. Aparicio S
    54. Foekens JA
    55. Eyfjörd JE
    56. Lakhani SR
    57. Thomas G
    58. Myklebost O
    59. Span PN
    60. Børresen-Dale AL
    61. Richardson AL
    62. Van de Vijver M
    63. Vincent-Salomon A
    64. Van den Eynden GG
    65. Flanagan AM
    66. Futreal PA
    67. Janes SM
    68. Bova GS
    69. Stratton MR
    70. McDermott U
    71. Campbell PJ
    72. ICGC Breast Cancer Group
    73. ICGC Bone Cancer Group
    74. ICGC Prostate Cancer Group

    (2014) Mobile DNA in cancer. Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes

    Science 345:1251343.

    https://doi.org/10.1126/science.1251343

    • PubMed
    • Google Scholar
99. 1. Volkin E
    2. Cohn WE

    (1953)

    On the structure of ribonucleic acids. II. The products of ribonuclease action

    The Journal of Biological Chemistry 205:767–849.

    • PubMed
    • Google Scholar
100. 1. Wallace N
     2. Wagstaff BJ
     3. Deininger PL
     4. Roy-Engel AM

     (2008) LINE-1 ORF1 protein enhances Alu SINE retrotransposition

     Gene 419:1–6.

     https://doi.org/10.1016/j.gene.2008.04.007

     • PubMed
     • Google Scholar
101. 1. Wang X
     2. Huang L

     (2008) Identifying dynamic interactors of protein complexes by quantitative mass spectrometry

     Molecular & Cellular Proteomics 7:46–57.

     https://doi.org/10.1074/mcp.M700261-MCP200

     • PubMed
     • Google Scholar
102. 1. Wei W
     2. Gilbert N
     3. Ooi SL
     4. Lawler JF
     5. Ostertag EM
     6. Kazazian HH
     7. Boeke JD
     8. Moran JV

     (2001) Human L1 retrotransposition: cis preference versus trans complementation

     Molecular and Cellular Biology 21:1429–1439.

     https://doi.org/10.1128/MCB.21.4.1429-1439.2001

     • PubMed
     • Google Scholar
103. 1. Weidensdorfer D
     2. Stöhr N
     3. Baude A
     4. Lederer M
     5. Köhn M
     6. Schierhorn A
     7. Buchmeier S
     8. Wahle E
     9. Hüttelmaier S

     (2009) Control of c-myc mRNA stability by IGF2BP1-associated cytoplasmic RNPs

     RNA 15:104–115.

     https://doi.org/10.1261/rna.1175909

     • PubMed
     • Google Scholar
104. 1. Weinlich S
     2. Hüttelmaier S
     3. Schierhorn A
     4. Behrens SE
     5. Ostareck-Lederer A
     6. Ostareck DH

     (2009) IGF2BP1 enhances HCV IRES-mediated translation initiation via the 3'UTR

     RNA 15:1528–1542.

     https://doi.org/10.1261/rna.1578409

     • PubMed
     • Google Scholar
105. 1. Wyers F
     2. Sentenac A
     3. Fromageot P

     (1973) Role of DNA-RNA hybrids in eukaryotes. Ribonuclease H in yeast

     European Journal of Biochemistry 35:270–281.

     https://doi.org/10.1111/j.1432-1033.1973.tb02835.x

     • PubMed
     • Google Scholar
106. 1. Xie Y
     2. Mates L
     3. Ivics Z
     4. Izsvák Z
     5. Martin SL
     6. An W

     (2013) Cell division promotes efficient retrotransposition in a stable L1 reporter cell line

     Mobile DNA 4:10.

     https://doi.org/10.1186/1759-8753-4-10

     • PubMed
     • Google Scholar
107. 1. Ying S
     2. Chen Z
     3. Medhurst AL
     4. Neal JA
     5. Bao Z
     6. Mortusewicz O
     7. McGouran J
     8. Song X
     9. Shen H
     10. Hamdy FC
     11. Kessler BM
     12. Meek K
     13. Helleday T

     (2016) DNA-PKcs and PARP1 Bind to Unresected Stalled DNA Replication Forks Where They Recruit XRCC1 to Mediate Repair

     Cancer Research 76:1078–1088.

     https://doi.org/10.1158/0008-5472.CAN-15-0608

     • PubMed
     • Google Scholar
108. 1. Yoshida H

     (2001) The ribonuclease T1 family

     Methods in Enzymology 341:28–41.

     https://doi.org/10.1016/S0076-6879(01)41143-8

     • PubMed
     • Google Scholar

Author details

1. Martin S Taylor

   Department of Pathology, Massachusetts General Hospital, Boston, United States

   Contribution

   Conceptualization, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Ilya Altukhov and Kelly R Molloy

   For correspondence

   MSTAYLOR@PARTNERS.ORG

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5824-142X

2. Ilya Altukhov

   Moscow Institute of Physics and Technology, Dolgoprudny, Russia

   Contribution

   Data curation, Software, Formal analysis, Visualization, Methodology

   Contributed equally with

   Martin S Taylor and Kelly R Molloy

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9821-1890

3. Kelly R Molloy

   Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, United States

   Contribution

   Data curation, Investigation, Methodology

   Contributed equally with

   Martin S Taylor and Ilya Altukhov

   For correspondence

   kmolloy@rockefeller.edu

   Competing interests

   No competing interests declared

4. Paolo Mita

   Department of Biochemistry and Molecular Pharmacology, Institute for Systems Genetics, NYU Langone Health, New York, United States

   Contribution

   Investigation, Writing—review and editing

   For correspondence

   paolo.mita@nyumc.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2093-4906

5. Hua Jiang

   Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, United States

   Contribution

   Investigation

   For correspondence

   hjiang01@rockefeller.edu

   Competing interests

   No competing interests declared

6. Emily M Adney

   1. Department of Biochemistry and Molecular Pharmacology, Institute for Systems Genetics, NYU Langone Health, New York, United States
   2. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Investigation

   For correspondence

   eadney@gmail.com

   Competing interests

   No competing interests declared

7. Aleksandra Wudzinska

   McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Investigation

   For correspondence

   aleksandra.wudzinska@nyumc.org

   Competing interests

   No competing interests declared

8. Sana Badri

   Department of Pathology, NYU Langone Health, New York, United States

   Contribution

   Software, Formal analysis

   For correspondence

   Sana.Badri@nyumc.org

   Competing interests

   No competing interests declared

9. Dmitry Ischenko

   Moscow Institute of Physics and Technology, Dolgoprudny, Russia

   Contribution

   Formal analysis

   For correspondence

   ischenko.dmitry@gmail.com

   Competing interests

   No competing interests declared

10. George Eng

    Department of Pathology, Massachusetts General Hospital, Boston, United States

    Contribution

    Software

    For correspondence

    george_eng@mgh.harvard.edu

    Competing interests

    No competing interests declared

11. Kathleen H Burns

    1. Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, United States
    2. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, United States

    Contribution

    Resources, Funding acquisition, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1620-3761

12. David Fenyö

    Department of Biochemistry and Molecular Pharmacology, Institute for Systems Genetics, NYU Langone Health, New York, United States

    Contribution

    Resources, Formal analysis

    For correspondence

    david@fenyolab.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5049-3825

13. Brian T Chait

    Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, United States

    Contribution

    Resources, Funding acquisition

    For correspondence

    chait@mail.rockefeller.edu

    Competing interests

    No competing interests declared

14. Dmitry Alexeev

    Novosibirsk State University, Novosibirsk, Russia

    Contribution

    Resources, Data curation, Formal analysis, Supervision, Funding acquisition

    For correspondence

    exappeal@gmail.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0783-1176

15. Michael P Rout

    Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, United States

    Contribution

    Resources, Funding acquisition, Writing—review and editing

    For correspondence

    Michael.Rout@rockefeller.edu

    Competing interests

    No competing interests declared

16. Jef D Boeke

    Department of Biochemistry and Molecular Pharmacology, Institute for Systems Genetics, NYU Langone Health, New York, United States

    Contribution

    Conceptualization, Resources, Funding acquisition, Writing—review and editing

    For correspondence

    jef.boeke@nyumc.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5322-4946

17. John LaCava

    1. Department of Biochemistry and Molecular Pharmacology, Institute for Systems Genetics, NYU Langone Health, New York, United States
    2. Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, United States

    Contribution

    Conceptualization, Data curation, Supervision, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Funding Acquisition, Writing—review and editing

    For correspondence

    jlacava@rockefeller.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6307-7713

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