Identification of Novel Regulators of LINE-1 Expression via CRISPR/Cas9 Screening

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These elements can copy and insert themselves into new genomic locations [1]. Typically, LINE-1 is repressed in healthy tissues but may become activated in various human diseases. LINE-1 expression has been associated with aging [2–4], neurodegenerative disorders [5–7], cancer [8–10], and autoimmune diseases [11,12]. Despite the strong association between LINE-1 expression and disease, the regulatory mechanisms controlling the expression of LINE-1-encoded ORF1p and ORF2p and the link between LINE-1 activity and cancer cell survival remain poorly understood. Elucidating these mechanisms will deepen our understanding of how LINE-1 contributes to disease pathogenesis. Results To identify upstream regulators of LINE-1 and genes associated with LINE-1 activity-dependent lethality, we developed a dual-reporter system that simultaneously monitors the protein levels of LINE-1-encoded ORF1p and ORF2p (wild-type or catalytically inactive EN/RT mutant). Using genome-wide CRISPR/Cas9-based screens with this reporter system, we identified genes that control LINE-1 expression through multiple potential mechanisms, including their regulation at both RNA and protein levels. Besides known regulators like the HUSH complex, our screening uncovered previously unknown regulators of ORF1p and ORF2p, and revealed distinct mechanisms regulating expression of these proteins. We also identified genes whose disruption contributes to LINE-1 activity-dependent lethality. Conclusion This study offers a valuable resource for the retrotransposon field, providing new insights into the distinct molecular mechanisms regulating LINE-1-encoded ORF1p and ORF2p, and highlighting potential therapeutic targets for diseases driven by LINE-1 dysregulation. LINE-1 (L1) Transposable elements Retrotransposon ORF1p ORF2p CRISPR screen Transposable elements DNA repair stress granules gene regulation translation transcription Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Long interspersed nuclear element-1 (LINE-1 or L1) is a retrotransposon that constitutes approximately 17% of the human genome. LINE-1 consists of a 6-kb polycistronic RNA that encodes two proteins: ORF1p, an RNA-binding protein with chaperone activity [ 13 ], and ORF2p, which contains reverse transcriptase (RT) and endonuclease (EN) domains [ 14 – 16 ]. Despite the presence of over 500,000 genomic LINE-1 copies in the human genome, only about 150 are full-length and capable of encoding both ORF1p and ORF2p [ 1 ]. In the nucleus, LINE-1 RT can “copy and paste” itself into the genome through target-primed reverse transcription (TPRT) [ 17 – 20 ], which can lead to DNA damage and genomic instability [ 8 , 21 – 23 ]. In the cytoplasm, LINE-1 RT can actively reverse transcribe poly-adenylated RNAs into DNAs, which can lead to activation of nucleic acid sensing and innate inflammatory pathways [ 2 , 3 , 24 , 25 ]. In normal somatic cells, LINE-1 RNA expression is tightly repressed, but it can be reactivated under disease conditions such as cancer [ 8 – 10 ], neurodegeneration [ 5 – 7 ], and autoimmune disorders [ 11 , 12 ] due to exogenous stress or epigenetic changes. Notably, while ORF1p protein levels correlate with RNA expression and are readily detectable [ 26 ], ORF2p protein levels remain exceptionally low and are not detectable using classical biochemical and proteomic methods [ 27 – 31 ], although its activity is evident in various disease contexts [ 32 – 34 ]. This disparity may be attributed to translational inefficiency, the presence of two STOP codons within the linker between ORF1p and ORF2p, or the potential instability or degradation of ORF2p following synthesis. These challenges make studying ORF2p particularly difficult and have led to a poor understanding of how ORF1p and ORF2p are regulated. Although significant progress has been made in understanding the epigenetic regulation of LINE-1 RNA [ 35 – 37 ] and identification of LINE-1 ORF1p regulators [ 38 , 39 ], systematic studies investigating the regulation of LINE-1-encoded ORF1p and ORF2p at both the RNA and protein levels are still lacking. To address these gaps, we developed an unbiased, genome-wide loss-of-function CRISPR screen using a dual-reporter system that simultaneously tracks the expression of LINE-1-encoded ORF1p and ORF2p. This reporter system enabled the identification of putative upstream regulators influencing LINE-1 RNA, ORF1p, and ORF2p protein expression. By comparing wild-type and catalytically inactive (EN/RT mutant) versions of the reporter, we also uncovered genes linked to LINE-1 activity-dependent lethality. Hits identified from the primary screen were further validated through a targeted secondary screen. This approach reveals distinct regulatory mechanisms controlling LINE-1-encoded ORF1p, and ORF2p expression, and highlights genes whose disruption impacts LINE-1-induced lethality. Collectively, these findings offer new perspectives on how LINE-1 activity can be upregulated in diseases and identifies potential therapeutic targets for conditions linked to LINE-1 dysregulation. Results A dual-reporter system for identifying LINE-1 regulators through CRISPR screening To identify putative upstream regulators of LINE-1 expression we developed a reporter system incorporating the full consensus sequence of LINE-1, called L1RP [ 40 ] as a dual-labeled construct (Fig. 1 A). In this design, ORF1p was tagged with a C-terminal GFP, and ORF2p was tagged with a C-terminal mCherry (referred to as ‘RFP’ in the figures for simplicity), enabling simultaneous detection of ORF1p and ORF2p protein levels. A downstream Hygromycin resistance gene tagged with a C-terminal BFP was included as a control to account for global changes in gene expression and normalization by viability. Because increased expression of wild-type ORF2p can be lethal to cells [ 24 , 41 ], two versions of this construct were generated: one containing wild-type ORF2p and another with point mutations in the reverse transcriptase and endonuclease domains of ORF2p, rendering it catalytically inactive (Fig. 1 A). Stable cell lines were created by randomly integrating these constructs into the genome of HCT-116 colorectal cancer cells expressing Cas9. This cell line was selected due to its suitability for CRISPR screening and features potentially linked to LINE-1 regulation, such as an active p53 pathway [ 26 ]. ORF1p expression was confirmed by western blot and flow cytometry in both wild-type (WT) and catalytically inactive (MT) LINE-1-expressing cells, while ORF2p expression remained undetectable, consistent with previous reports of low ORF2p levels in cell lines [ 31 , 32 ] (Fig. 1 B-C). Additionally, over 98% of cells were confirmed to be BFP-positive (data not shown). To confirm that the reporter constructs did not introduce global transcriptional changes, we performed gene expression profiling of cells expressing either WT or EN/RT MT ORF2p. Only a limited number of differentially expressed genes were observed; 118 between no reporter and L1 EN/RT MT, 60 between no reporter and L1 WT, and 28 between L1 EN/RT MT and L1 WT. This result indicates minimal perturbation and allows for downstream screening without complications (Fig. 1 D). We then performed a genome-wide CRISPR screen using both WT and EN/RT MT reporter cell lines. The primary goal was to identify genes whose loss altered ORF1p-GFP or ORF2p-RFP expression without affecting BFP levels (Fig. 2 A). In parallel, we aimed to uncover genes associated with LINE-1 activity-dependent lethality by comparing the WT and MT reporter lines. Enrichment or depletion of gRNAs in sorted populations was assessed by next-generation sequencing of the gRNA libraries (Fig. 2 B). Identification of Regulators of ORF1p and ORF2p The genome-wide CRISPR screening, comprising over 80,000 gRNAs covering 20,000 genes with 4 gRNAs per gene, identified cell populations with modulation of ORF1p-GFP and/or ORF2p-RFP levels in both ORF2p WT and MT cell lines compared to parental lines lacking the reporter. Since ORF2p-RFP expression was not initially detectable, no cell populations with decreased ORF2p-RFP levels were observed. The cells were then sorted using FACS into three populations for both WT and MT cells: ORF1p decreased, ORF1p increased, and ORF2p increased. These sorted populations were subsequently subjected to gRNA sequencing and enrichment analysis (Fig. 2 ). To facilitate data interpretation, LINE-1 regulators were categorized as either positive or negative regulators (Fig. S1 A). Positive regulators, when targeted (e.g., genetically knocked down) by gRNAs, resulted in the downregulation of ORF1p or ORF2p, whereas knockdown of negative regulators led to their upregulation. To validate the quality of the screening, we examined whether previously reported LINE-1 regulators were identified. Notably, all core components of the HUSH complex, known to restrict LINE-1 transcriptional activity through heterochromatin formation [ 39 , 42 ], were identified as ORF1p negative regulators (Fig. S1 B). This validation prompted further investigation into the list of newly identified LINE-1 regulators. The genome-wide screen identified 398 ORF1p positive regulators, 646 ORF1p negative regulators, and 93 ORF2p negative regulators in both WT and MT LINE-1 reporter cell lines, using a p-value cutoff of 1, and filtering genes with TPM < 2 (Fig. 3 A, Table S1 - S3 ). To validate these LINE-1 regulators, a secondary CRISPR screen was performed using a more focused gRNA library. The selected gRNAs included potential hits from the primary screen (see Methods), known LINE-1 regulators, LINE-1 interactors, and controls such as essential genes and negative gRNAs. The library comprised a total of 15,000 gRNAs representing 1,490 genes, with 10 gRNAs per gene (Table S4 ). The same screening strategy outlined in Fig. 2 was applied. The secondary screen successfully validated 13 ORF1p positive regulators, 194 ORF1p negative regulators, and 14 ORF2p negative regulators (Fig. 3 A- 3 B, Table S5 - S6 ). Pathway analysis of these validated hits revealed an enrichment of diverse pathways among ORF1p negative regulator hits (Fig. S2 ). Many of these pathways have been linked to regulation of LINE-1 levels and retrotransposition activity, including transcriptional regulation by TP53, epigenetic regulation, and viral infection [ 24 , 43 – 46 ]. In addition, RNA metabolism proteins involved in stress granule formation and DNA repair proteins were identified among ORF1p negative regulators (Fig. 3 B), suggesting a potential link to ORF1p in the regulation of cellular stress responses. However, no specific pathway enrichment was observed for ORF1p positive regulator or ORF2p negative regulator hits, potentially due to the low number of hits identified in these categories. ORF1p positive regulator hits included proteins related to RNA binding, transcription, and signaling, suggesting that its levels can be regulated at multiple levels (Fig. 3 B). Furthermore, ORF2p negative regulators included proteins involved in protein degradation, translation, signaling, and transcription. These hits suggest that ORF2p regulation may occur at the protein level and could be linked to signal transduction events in response to environmental cues (Fig. 3 B). Genes Associated with LINE-1 Activity-Dependent Lethality Since ORF2p expression is toxic to cells, we hypothesized that any perturbation leading to WT ORF2p upregulation would result in a loss of cell viability, whereas this effect would not occur in MT ORF2p-expressing cells (Fig. 4 A). To test this, we conducted a lethality screen in parallel with the reporter screen. We reasoned that this approach would be more sensitive than the ORF2p-RFP reporter screening, as cells might still be vulnerable to slight ORF2p activation even when the increase is not prominent enough to be detected by the reporter system. The lethality screen identified 415 hits from the genome-wide screen, 57 of which were validated by the secondary screen as described above (Fig. 4 B). Interestingly, although no clear pathways were enriched within this list (potentially due to the low number of hits and genetic/cellular background), several protein classes similar to those observed in ORF2p negative regulators were identified, including protein degradation, translation, and transcription (Fig. 4 C). Additionally, proteins related to the mitochondrial electron transport chain (ETC) were identified, suggesting a potential link between energy metabolism and ORF2p expression. Some of these proteins also had relevant connections to cancer biology, consistent with previous reports suggesting a potential link between LINE-1 activity and cancer progression [ 26 , 32 , 47 ]. ORF1p and ORF2p are differentially controlled by non-overlapping negative regulators Identifying regulators of both ORF1p and ORF2p enabled us to compare their regulatory landscapes, challenging the common assumption that these proteins are co-regulated [ 26 ]. Strikingly, we observed no overlap between ORF1p and ORF2p regulators (Fig. 5 ), with the sole exception of UROD, a gene previously shown to cause autofluorescence upon knockout, and thus excluded from analysis [ 48 ]. This lack of overlap may help explain the disproportionate production of ORF1p and ORF2p from the LINE-1 transcript [ 27 , 32 ], with ORF1p produced at levels approximately 180-fold higher than ORF2p [ 32 ]. Such imbalance likely reflects a regulatory mechanism in which cells limit ORF2p production through translational or post-translational repression while allowing robust ORF1p expression. These findings also suggest that ORF2p activity may be selectively induced under specific conditions to promote retrotransposition [ 10 , 45 , 49 ]. Together, our results support the conclusion that although ORF1p and ORF2p are derived from a single transcript, they are subject to distinct regulatory controls at the translational and post-translational level. Discussion The insights from this study have broad implications for understanding disease mechanisms and advancing therapeutic development of LINE-1 modulators (Fig. 6). The identification of distinct regulatory mechanisms for ORF1p and ORF2p provides new avenues for targeting LINE-1 in disease contexts where its activity is dysregulated, such as cancer, autoimmune disorders, and neurodegeneration. Importantly, this study demonstrates the potential to target ORF1p and ORF2p separately, recognizing that their contributions to disease may differ. For example, ORF1p’s RNA-binding and chaperone activity may be involved in regulating RNA dependent interferon signaling [50,51], while ORF2p’s enzymatic functions are more directly linked to genomic instability and cytoplasmic DNA-dependent interferon signaling [8,21–23]. This distinction enables precision targeting of either protein based on their specific roles in disease progression. These findings not only enhance our understanding of LINE-1 biology but also pave the way for developing targeted therapies to address diseases where LINE-1 dysregulation plays a critical role. The feature of regulators identified in this study offers a better understanding of LINE-1 biology and its relevance to diseases. The enrichment of RNA metabolism (stress granule) and DNA repair factors among ORF1p negative regulators underscores the importance of cellular stress response pathways in regulating LINE-1 ORF1p expression. However, no specific pathways were identified for the positive regulation of ORF1p or the negative regulation of ORF2p. This lack of enrichment may be due to the lower number of hits identified in these sets, or it may reflect the incidental nature of these regulatory mechanisms, which are likely context-dependent and influenced by cellular stress conditions and genetic backgrounds. For instance, the upregulation of ORF1p or the downregulation of ORF2p may not be actively controlled processes but rather byproducts of broader cellular responses to environmental or intrinsic stressors. In contrast, LINE-1 RNA, which correlates with ORF1p protein levels, appears to be tightly regulated, particularly under stress conditions. Our identification of genes associated with LINE-1 activity-induced lethality further highlights the potential vulnerability of cells to unregulated LINE-1 function, particularly in the context of ORF2p expression. While ORF2p protein levels are typically tightly suppressed, even modest increases can compromise cell viability [31]. This finding suggests that cells may have evolved layers of regulatory safeguards to prevent toxic accumulation of ORF2p, whose endonuclease and reverse transcriptase activities can drive DNA damage and trigger cytoplasmic DNA sensing pathways [2,3,24,25]. The enrichment of factors related to protein degradation, transcriptional regulation, translation, and the mitochondrial electron transport chain among lethality-associated hits points to a complex interplay between LINE-1 activity and fundamental cellular homeostasis. These vulnerabilities could potentially be exploited therapeutically in cancers where LINE-1 activity is aberrantly activated, offering a strategy to selectively target cells with elevated LINE-1 activity through synthetic lethality approach. The regulators identified in this study provide a valuable resource for the retrotransposon field, offering significant insights into the molecular mechanisms governing LINE-1 levels and activity. As with any high-throughput screen, this study has some limitations, and potential false positives must be considered. For example, autofluorescence-related artifacts, such as those associated with UROD knockout [48], can confound fluorescence-based readouts and yield spurious results. This underscores the importance of validating the identified hits individually through controlled experiments in different cell types to ensure that the findings are robust and broadly applicable. This study serves as a foundation for further investigations. Abbreviations LINE-1 (L1): Long Interspersed Nuclear Element-1 ORF1p: Open Reading Frame 1 Protein ORF2p: Open Reading Frame 2 Protein RT: Reverse Transcriptase EN: Endonuclease GFP: Green Fluorescent Protein RFP: Red Fluorescent Protein (mCherry) BFP: Blue Fluorescent Protein WT: Wild-Type MT: Mutant HUSH: Human Silencing Hub FACS: Fluorescence-Activated Cell Sorting CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats Declarations At the time of the work, all authors were full-time employees of, and hold stock options of, ROME Therapeutics. Funding Declaration: No funding to declare Ethics and Consent to Participate declarations: not applicable Consent to Publish declaration: not applicable Competing Interest declaration: None Author Contributions Conceptualization, O.O., S.K.; methodology, O.O., C.C., C.A. software, C.C., C.A.; investigation, O.O., C.C., S.K.; writing – original draft, O.O., C.C., S.K.; visualization, O.O., C.C.; supervision, O.O., L.D., D.Z., K.K.L., H.K., S.K., reviewing - original draft, all authors Acknowledgements We thank Ethan Leef and Erika Whyte (ROME Therapeutics) for their support in logistics and coordination for the study, Eric Zigon (Wyss Institute) for his assistance with cell sorting, and Hakan Guney (ROME Therapeutics) for his expertise and support in flow cytometry. We also thank Rosana Kapeller (ROME Therapeutics) for critically reviewing the manuscript. Methods Cell Culture HCT116 cells (human colorectal carcinoma) were maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, and 1 mM L-glutamine at 37°C in a humidified incubator with 5% CO₂. Cells were routinely passaged every 2–3 days at 70–80% confluence to ensure healthy growth. Cells stably expressing Cas9 and dual-reporter constructs were selected with 100 µg/mL hygromycin and expanded for use in downstream experiments. Reporter Cell Line Development HCT116 cells stably expressing Cas9 were transfected with piggyBac-compatible dual-reporter constructs encoding LINE-1 wild-type (WT) or catalytically inactive (MT) versions along with piggyBac transposase S100 (System Biosciences). In these constructs, ORF1p was tagged with GFP and ORF2p with RFP. A BFP-tagged hygromycin resistance gene was included to control for global transcriptional changes. Cells were selected with 100 µg/mL hygromycin to generate stable cell lines and the reporter expression in these lines was validated by flow cytometry and western blot. Genome-Wide CRISPR Screen CRISPR libraries containing ~80,000 sgRNAs (targeting ~20,000 genes, 4 sgRNAs per gene) and packaged into lentivirus were purchased from Cellecta (KOHGW-80K). They were transduced at a multiplicity of infection (MOI) of ~0.3 to achieve ~500-fold coverage. After transduction, cells were selected with 0.5 µg/mL puromycin for 5 days to eliminate non-transduced cells. The surviving cells were cultured for 14-18 days to maintain library representation. Cells were sorted by FACS into populations with increased or decreased ORF1p-GFP or ORF2p-RFP levels. Genomic DNA (gDNA) was extracted from sorted populations and subjected to next-generation sequencing to identify enriched sgRNAs. For the LINE-1 activity-dependent lethality screen, the unsorted cells 18 days post-infection was used for gRNA sequencing to identify depleted sgRNAs. Secondary CRISPR Screen Hits from the primary screen that appeared in either LINE-1 activity-dependent lethality screen or in the L1 regulation screen performed in both WT or MT L1 reporter cell lines, including some select hits that appeared in only one replicate, as well as reported LINE-1 regulators, LINE-1 interactors, and additional controls targeting essential genes or negative gRNAs, were compiled into a custom library containing 15,000 sgRNAs (10 sgRNAs per gene) and cloned into the same vector used in the genome-wide screen, pRSGEP-U6-sg-EF1-Puro. HCT116 WT and MT reporter cells were infected at an MOI of ~0.3 with ~1,000-fold library coverage. Cells were selected with 0.5 µg/mL puromycin for 5 days, cultured for 12 days, and sorted into distinct reporter expression populations by FACS. The sorted populations were analyzed via sequencing to validate hits from the primary screen. For the LINE-1 activity-dependent lethality screen, the unsorted cells 18 days post-infection was used for gRNA sequencing to identify depleted sgRNAs and validate hits from the primary screen. Next-Generation Sequencing of gRNAs All steps of next-generation sequencing were performed at Cellecta according to the manufacturer’s instructions. Briefly, genomic DNA (gDNA) was extracted from FACS-sorted populations using a column-based kit or phenol-chloroform extraction, depending on the number of cells. DNA quality and quantity were assessed using a Nanodrop and agarose gel electrophoresis. sgRNAs were then amplified using two sequential PCRs with outside primer sets containing Illumina adapters, as described in the Cellecta HTS6C (pRSG16/17) manual. Approximately 5–10 µg of gDNA per reaction was used with ~25–28 cycles. Products (~200–300 bp) were verified by gel electrophoresis. PCR products were purified with AMPure XP beads and quantified using a Qubit fluorometer. Library size and quality were confirmed using a Bioanalyzer. Libraries were pooled equimolarly and sequenced on an Illumina platform (HiSeq or NovaSeq) using paired-end 50 bp or single-end 75 bp reads to achieve ~500–1,000 reads per sgRNA. Cell Sorting Cell sorting was performed using fluorescence-activated cell sorting (FACS) to isolate populations based on reporter expression with a Sony SH800 cell sorter. After genome-wide and secondary CRISPR screens, HCT116 cells expressing dual-reporter constructs were harvested and resuspended in FACS buffer (phosphate-buffered saline supplemented with 2% fetal bovine serum and 2 mM EDTA) at a concentration of 10 million cells per mL. Baseline fluorescence signals were determined using L1 reporter cell lines that were not infected with the CRISPR library. The CRISPR library-infected cell populations were then gated based on relative intensities compared to uninfected cells. Cell populations that did not alter Hygro-BFP expression were selected for sorting, and cells were sorted based on decreased ORF1p-GFP levels, increased ORF1p-GFP levels, and increased ORF2p-RFP levels. Each sorted population was collected into tubes containing complete RPMI medium (10% FBS, 1% penicillin-streptomycin) to maintain cell viability under sterile conditions. Sorted cells were flash-frozen and stored at -80°C for downstream analyses, including sgRNA amplification and next-generation sequencing. Flow Cytometry Analysis Flow cytometry was performed on a Cytek Northern Lights to analyze ORF1p-GFP, ORF2p-RFP, and BFP control expression in HCT116 cells. Cells were harvested, washed with PBS, and resuspended in FACS buffer (PBS with 2% FBS). GFP and mCherry beads (Invitrogen) were used for compensation. Data were acquired for 20,000–50,000 events per sample and analyzed using FlowJo software. CRISPR screen analysis Sequencing reads were demultiplexed, adapters were trimmed, and sgRNA sequences were mapped to the reference library. sgRNA abundance and differential enrichment were analyzed to identify candidate regulators. The Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK) [52] (conda version, downloaded in Oct. 2023) was used to process sgRNA read counts. Initially, sgRNA read counts for individual samples were obtained using the “count” command in MAGeCK. A total of 580 negative and 40 positive control sgRNAs were included in both the primary and secondary screens. Differential selection analysis was performed using the “test” command in MAGeCK, with read counts normalized via total count normalization to account for sequencing depth. The “--control-sgrna” parameter was set to specify negative control sgRNAs. The robust rank aggregation (α-RRA) algorithm was applied to compute the RRA score and p-value, reflecting the degree of positive or negative selection. Additionally, the number of “good sgRNAs” (sgRNA abundance differs significantly following a negative binomial model) per gene was determined. For the LINE-1 ORF1p positive and negative regulator screens, as well as the ORF2p negative regulator screen, positively selected genes from the MAGeCK output were used. For the LINE-1 ORF2p lethality screen, negatively selected genes from the MAGeCK output were analyzed. In both the primary and secondary screens, genes meeting the criteria of p-value ≤ 0.05 and ≥ 2 good sgRNAs were considered significant. RNA-seq analysis Raw RNA-seq reads in FASTQ format were first aligned to the human reference genome (hg38) using STAR (v2.7.11) [53] with default parameters unless otherwise specified. The resulting BAM files were sorted and indexed using Samtools (v1.21) [54]. Gene-level expression quantification was performed using Salmon (v1.10.2) [55] in quasi-mapping mode with transcript-level TPM normalization. Differential expression analysis was conducted using DESeq2 (v1.30.1) [56] in R, where gene expression counts were normalized, and differentially expressed genes (DEGs) were identified based on a threshold of |log2FoldChange| > 1 and adjusted p-value < 0.01 (Benjamini-Hochberg procedure used to calculate the adjusted p-value). Pathway analysis Functional enrichment of gene sets was analyzed using Enrichr67 webserver (https://maayanlab.cloud/Enrichr/) against the Hallmark and GO biological process datasets. Pathways/GO terms with FDR < 0.1 were regarded as significantly enriched. Gene set enrichment analysis (GSEA) analysis was performed using function GSEA() from fgsea [57] package and visualized by gseaplot2() from enrichplot [58] packages. References Kazazian HH Jr, Moran JV. Mobile DNA in Health and Disease. N Engl J Med. 2017;377: 361–370. 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Genes Dev. 2006;20: 210–224. You E, Patel BK, Rojas AS, Sun S, Danaher P, Ho NI, et al. LINE-1 ORF1p mimics viral innate immune evasion mechanisms in pancreatic ductal adenocarcinoma. Cancer Discov. 2025. doi:10.1158/2159-8290.CD-24-1317 Ishak CA, Marhon SA, Tchrakian N, Hodgson A, Loo Yau H, Gonzaga IM, et al. Chronic viral mimicry induction following p53 loss promotes immune evasion. Cancer Discov. 2025. doi:10.1158/2159-8290.CD-24-0094 Li W, Xu H, Xiao T, Cong L, Love MI, Zhang F, et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 2014;15: 554. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29: 15–21. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25: 2078–2079. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14: 417–419. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15: 550. Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. bioRxiv; 2016. doi:10.1101/060012 Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16: 284–287. Additional Declarations No competing interests reported. Supplementary Files Fig.S1.jpg Figure S1.Screening identified known LINE-1 regulators A. The genome-wide primary CRISPR screen identified both positive and negative regulators of LINE-1. The knockdown of positive regulators led to the downregulation of LINE-1, whereas the knockdown of negative regulators resulted in the upregulation of LINE-1. B. The screen successfully identified all core HUSH complex members, which are known negative regulators of LINE-1. Fig.S2.jpg Figure S2.Pathways analysis of ORF1p negative regulator hits Pathway analysis of 194 ORF1p negative regulators identified in both the primary and secondary screens using Gene Ontology (GO) analysis. TableS1.PrimaryscreenrawdataforORF1pregulators.xlsx Table S1. Primary screen raw data for ORF1p regulators TableS2.PrimaryscreenrawdataforORF2pregulators.xlsx Table S2. Primary screen raw data for ORF2p regulators TableS3.PrimaryscreenMAGeCKanalysis.xlsx Table S3. Primary screen MAGeCK analysis TableS4.SecondaryscreenrawdataforORF1pandORF2pregulators.xlsx Table S4. Secondary screen raw data for ORF1p and ORF2p regulators TableS5.SecondaryscreenMAGeCKanalysis.xlsx Table S5. Secondary screen MAGeCK analysis TableS6.Commonhitsbetweenprimaryandsecondaryscreen.xlsx Table S6. Common hits between primary and secondary screen Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Mobile DNA → Version 1 posted Editorial decision: Revision requested 27 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 28 May, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 28 May, 2025 First submitted to journal 22 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6726282","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463064158,"identity":"41ca2a84-58be-4aea-9781-a4bc5f10c0a7","order_by":0,"name":"Ozgur Oksuz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACHjBpA8SMjQdI0ZIG0tJAkpbDYJI4Lfw8x59J/qg4b7e2/TDQlhqbaIJaJHt7zCQkztxO3nYmEajlWFpuAyEtBud52CQM224nmx0AamFsOEyMFvZnEolt55LNzj8kVsvZBjOJg20H7MxuEGuLZM8ZY8uGM8kJZjeAtiQQ4xd+nvSHN39U2NmbnU9/+OBDjQ1hLTCQCFaZQKxyELAnRfEoGAWjYBSMMAAAIkdFm/4u5OAAAAAASUVORK5CYII=","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":true,"prefix":"","firstName":"Ozgur","middleName":"","lastName":"Oksuz","suffix":""},{"id":463064160,"identity":"7a5f2c06-75eb-4d77-a4f6-79b7ce252f75","order_by":1,"name":"Chong Chu","email":"","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Chu","suffix":""},{"id":463064161,"identity":"444d61f1-ed66-4289-87c5-9caf84c8f6bc","order_by":2,"name":"Cedric Arisdakessian","email":"","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":false,"prefix":"","firstName":"Cedric","middleName":"","lastName":"Arisdakessian","suffix":""},{"id":463064163,"identity":"0daa3ed5-83de-4e26-bf00-1ceba655718f","order_by":3,"name":"Liyang Diao","email":"","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":false,"prefix":"","firstName":"Liyang","middleName":"","lastName":"Diao","suffix":""},{"id":463064164,"identity":"9409aa5a-b249-4405-af3c-af57ea07c95d","order_by":4,"name":"Dennis Zaller","email":"","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":false,"prefix":"","firstName":"Dennis","middleName":"","lastName":"Zaller","suffix":""},{"id":463064166,"identity":"5359917b-a241-4e25-9b15-f5a5940fc114","order_by":5,"name":"Kimberly K. Long","email":"","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":false,"prefix":"","firstName":"Kimberly","middleName":"K.","lastName":"Long","suffix":""},{"id":463064167,"identity":"7000a424-1bda-4e56-b87c-9a606f9296cd","order_by":6,"name":"Heike Keilhack","email":"","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":false,"prefix":"","firstName":"Heike","middleName":"","lastName":"Keilhack","suffix":""},{"id":463064168,"identity":"46c762cd-b4e0-477a-914e-33ef797bb3e6","order_by":7,"name":"Sarah Knutson","email":"","orcid":"","institution":"ROME Therapeutics","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Knutson","suffix":""}],"badges":[],"createdAt":"2025-05-22 15:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6726282/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6726282/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13100-025-00386-5","type":"published","date":"2025-12-05T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83892474,"identity":"24e1fa81-2246-49a6-88bd-5395ac026ccd","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346091,"visible":true,"origin":"","legend":"\u003cp\u003eDesign and validation of the LINE-1 (L1) dual-reporter system\u003c/p\u003e\n\u003cp\u003eA.\tA reporter system was designed, incorporating a C-terminal GFP tag on ORF1p and a C-terminal mCherry (referred to as ‘RFP’) tag on ORF2p, utilizing the consensus sequence of L1RP from the 5’ UTR to the 3’ UTR. Downstream of this construct, a pGK promoter-driven hygromycin-resistant C-terminal BFP was included as a control for non-specific global changes in gene expression. The construct described in (A) was inserted into HCT-116 cells expressing Cas9 using the piggyBac transposon system, enabling the cells to express ORF1p-GFP, ORF2p-RFP, and Hygro-BFP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB.\tWestern blot analysis showing the expression of exogenous (exo) and endogenous (endo) ORF1p.\u003c/p\u003e\n\u003cp\u003eC.\tFlow cytometry analysis showing an increase in GFP-positive cell populations in both L1 wild-type (WT) and L1 EN/RT-dead cells. No significant increase in RFP-positive cell populations was observed in L1 WT or L1 EN/RT-dead cells.\u003c/p\u003e\n\u003cp\u003eD.\tRNA-seq analysis indicating no major global changes in gene expression in L1 WT or L1 EN/RT-dead cells compared to the parental cells (without the reporter). Differential expression was considered significant with |log₂FoldChange| \u0026gt; 1 and adjusted p-value \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003eEN: Endonuclease, RT: Reverse Transcriptase\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/20fc9d7bfb6aa599ac26d386.png"},{"id":83892487,"identity":"6cfdffc2-cfd7-44d3-b07f-729e7ee564df","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114543,"visible":true,"origin":"","legend":"\u003cp\u003eDesign of the CRISPR screen to identify LINE-1 regulators\u003c/p\u003e\n\u003cp\u003eA.\tUsing the LINE-1 dual reporter system, a genome-wide or targeted CRISPR screen was employed to identify genes regulating ORF1p-GFP or ORF2p-RFP expression without affecting Hygro-BFP levels.\u003c/p\u003e\n\u003cp\u003eB.\tThe sorted cell populations were subjected to next-generation sequencing (NGS) to identify and quantify enriched gRNAs within the cell populations.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/e8f008cd68eff1f1cee10bd0.png"},{"id":83893560,"identity":"f78c6cac-9ca4-462f-b75a-b7b1c54924cf","added_by":"auto","created_at":"2025-06-04 08:26:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":382946,"visible":true,"origin":"","legend":"\u003cp\u003eScreening identified novel regulators of ORF1p and ORF2p that are validated by a secondary screen\u003c/p\u003e\n\u003cp\u003eA.\tOverlapping hits were identified between the primary and secondary screens. Analysis of the CRISPR secondary screen highlights significant hits with a p-value \u0026lt; 0.05 for ORF1p positive regulators and ORF1/2p negative regulators.\u003c/p\u003e\n\u003cp\u003eB.\tA representative list of genes includes the validated hits from the secondary screen. SG: Stress Granule\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/299134c91b557924a95a25b3.png"},{"id":83892484,"identity":"89e5b79e-438e-43e3-9f89-d386281509a0","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174111,"visible":true,"origin":"","legend":"\u003cp\u003eGenes associated with LINE-1 activity-dependent lethality\u003c/p\u003e\n\u003cp\u003eA.\tThe CRISPR screening, which led to an increase in ORF2p levels or activity, may result in selective cell death in L1 ORF2p wild-type (WT) cells but not in EN-RT ORF2p mutant (MT) cells.\u003c/p\u003e\n\u003cp\u003eB.\tOverlapping hits were identified between the primary and secondary screens. Analysis of the CRISPR secondary screen highlights significant hits with a p-value \u0026lt; 0.05 for genes that are selectively lethal in L1 ORF2p WT cells.\u003c/p\u003e\n\u003cp\u003eC.\tA representative list of genes includes the validated hits from the secondary screen. ETC: Electron transport chain\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/fb75837f37c80ad0b1728934.png"},{"id":83893565,"identity":"065e2ffd-408e-4cf7-b573-64fcd41d97c8","added_by":"auto","created_at":"2025-06-04 08:26:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42037,"visible":true,"origin":"","legend":"\u003cp\u003eORF1p and ORF2p are differentially controlled by non-overlapping negative regulators\u003c/p\u003e\n\u003cp\u003eOverlap analysis of negative regulator hits for ORF1p and ORF2p was performed. Hits that show lethality in ORF2p WT was also included in the analysis. The analysis used hits confirmed by the secondary screen, no overlaps have been observed between both sets.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/74fa16c3132a4510f4345058.png"},{"id":83893958,"identity":"7a6b9c86-924d-4908-a485-f3d32b8f7e47","added_by":"auto","created_at":"2025-06-04 08:34:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126216,"visible":true,"origin":"","legend":"\u003cp\u003eUnderstanding LINE-1 biology and identifying opportunities to target upstream regulators of LINE-1\u003c/p\u003e\n\u003cp\u003eUncovering upstream regulators of LINE-1 may help us understand how LINE-1 is upregulated in human diseases such as autoimmune, neurodegenerative, and cancer . Targeting upstream regulators of LINE-1 may provide a new therapeutic approach for human diseases linked to LINE-1 dysregulation\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/81c3b4ece22a72628f653c1e.png"},{"id":97724144,"identity":"6c3e1ecc-ba47-4357-acc4-852108fed6ce","added_by":"auto","created_at":"2025-12-08 16:12:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1688310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/ec6a8abf-92b3-403a-9114-177ba4fd8e92.pdf"},{"id":83892477,"identity":"bd5fc543-cbd2-498a-bfff-ffd04c5356cf","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003eScreening identified known LINE-1 regulators\u003c/p\u003e\n\u003cp\u003eA. The genome-wide primary CRISPR screen identified both positive and negative regulators of LINE-1. The knockdown of positive regulators led to the downregulation of LINE-1, whereas the knockdown of negative regulators resulted in the upregulation \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;of LINE-1.\u003c/p\u003e\n\u003cp\u003eB. The screen successfully identified all core HUSH complex members, which are \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;known negative regulators of LINE-1.\u003c/p\u003e","description":"","filename":"Fig.S1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/ff9d714d7a95452fa44f7562.jpg"},{"id":83892475,"identity":"dc85bcd8-4e36-4c67-b87e-4c293eb3b407","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":57159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2.\u003c/strong\u003ePathways analysis of ORF1p negative regulator hits\u003c/p\u003e\n\u003cp\u003ePathway analysis of 194 ORF1p negative regulators identified in both the primary and secondary screens using Gene Ontology (GO) analysis.\u003c/p\u003e","description":"","filename":"Fig.S2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/8459a024a74f7b207f5cacf5.jpg"},{"id":83892497,"identity":"b474d5c1-3e5b-47c6-9ae7-c1d7a01fe1c2","added_by":"auto","created_at":"2025-06-04 08:18:48","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13878272,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. Primary screen raw data for ORF1p regulators\u003c/p\u003e","description":"","filename":"TableS1.PrimaryscreenrawdataforORF1pregulators.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/ede1cfe14c4be16e31cdb8f6.xlsx"},{"id":83892495,"identity":"ff036706-4b4b-4a45-a445-a22f23decf7b","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6295340,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2. Primary screen raw data for ORF2p regulators\u003c/p\u003e","description":"","filename":"TableS2.PrimaryscreenrawdataforORF2pregulators.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/9b502bc2f6f5ec66369f1415.xlsx"},{"id":83892494,"identity":"441e32f7-661f-4284-95b9-2fee6cc211e8","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1527938,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3. Primary screen MAGeCK analysis\u003c/p\u003e","description":"","filename":"TableS3.PrimaryscreenMAGeCKanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/0458c7ba9716de92f1a2b830.xlsx"},{"id":83892496,"identity":"13c11609-59bd-44f2-a071-f8cbce1f943f","added_by":"auto","created_at":"2025-06-04 08:18:47","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1806385,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4. Secondary screen raw data for ORF1p and ORF2p regulators\u003c/p\u003e","description":"","filename":"TableS4.SecondaryscreenrawdataforORF1pandORF2pregulators.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/e1389a5e459005a6278b3cbd.xlsx"},{"id":83893570,"identity":"79587bcd-c215-4f8e-871c-d1bab44fffc7","added_by":"auto","created_at":"2025-06-04 08:26:47","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":135042,"visible":true,"origin":"","legend":"\u003cp\u003eTable S5. Secondary screen MAGeCK analysis\u003c/p\u003e","description":"","filename":"TableS5.SecondaryscreenMAGeCKanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/f42b4c3b30371cc0d8de6e8a.xlsx"},{"id":83893567,"identity":"9d3617d7-113b-4cf5-9eb5-f6cdaee32d20","added_by":"auto","created_at":"2025-06-04 08:26:47","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":82632,"visible":true,"origin":"","legend":"\u003cp\u003eTable S6. Common hits between primary and secondary screen\u003c/p\u003e","description":"","filename":"TableS6.Commonhitsbetweenprimaryandsecondaryscreen.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6726282/v1/073716e4e53b6ded169d565c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Novel Regulators of LINE-1 Expression via CRISPR/Cas9 Screening","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLong interspersed nuclear element-1 (LINE-1 or L1) is a retrotransposon that constitutes approximately 17% of the human genome. LINE-1 consists of a 6-kb polycistronic RNA that encodes two proteins: ORF1p, an RNA-binding protein with chaperone activity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and ORF2p, which contains reverse transcriptase (RT) and endonuclease (EN) domains [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite the presence of over 500,000 genomic LINE-1 copies in the human genome, only about 150 are full-length and capable of encoding both ORF1p and ORF2p [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the nucleus, LINE-1 RT can \u0026ldquo;copy and paste\u0026rdquo; itself into the genome through target-primed reverse transcription (TPRT) [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which can lead to DNA damage and genomic instability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the cytoplasm, LINE-1 RT can actively reverse transcribe poly-adenylated RNAs into DNAs, which can lead to activation of nucleic acid sensing and innate inflammatory pathways [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn normal somatic cells, LINE-1 RNA expression is tightly repressed, but it can be reactivated under disease conditions such as cancer [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], neurodegeneration [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and autoimmune disorders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] due to exogenous stress or epigenetic changes. Notably, while ORF1p protein levels correlate with RNA expression and are readily detectable [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], ORF2p protein levels remain exceptionally low and are not detectable using classical biochemical and proteomic methods [\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], although its activity is evident in various disease contexts [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This disparity may be attributed to translational inefficiency, the presence of two STOP codons within the linker between ORF1p and ORF2p, or the potential instability or degradation of ORF2p following synthesis. These challenges make studying ORF2p particularly difficult and have led to a poor understanding of how ORF1p and ORF2p are regulated. Although significant progress has been made in understanding the epigenetic regulation of LINE-1 RNA [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and identification of LINE-1 ORF1p regulators [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], systematic studies investigating the regulation of LINE-1-encoded ORF1p and ORF2p at both the RNA and protein levels are still lacking.\u003c/p\u003e \u003cp\u003eTo address these gaps, we developed an unbiased, genome-wide loss-of-function CRISPR screen using a dual-reporter system that simultaneously tracks the expression of LINE-1-encoded ORF1p and ORF2p. This reporter system enabled the identification of putative upstream regulators influencing LINE-1 RNA, ORF1p, and ORF2p protein expression. By comparing wild-type and catalytically inactive (EN/RT mutant) versions of the reporter, we also uncovered genes linked to LINE-1 activity-dependent lethality. Hits identified from the primary screen were further validated through a targeted secondary screen. This approach reveals distinct regulatory mechanisms controlling LINE-1-encoded ORF1p, and ORF2p expression, and highlights genes whose disruption impacts LINE-1-induced lethality. Collectively, these findings offer new perspectives on how LINE-1 activity can be upregulated in diseases and identifies potential therapeutic targets for conditions linked to LINE-1 dysregulation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eA dual-reporter system for identifying LINE-1 regulators through CRISPR screening\u003c/h2\u003e \u003cp\u003eTo identify putative upstream regulators of LINE-1 expression we developed a reporter system incorporating the full consensus sequence of LINE-1, called L1RP [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] as a dual-labeled construct (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In this design, ORF1p was tagged with a C-terminal GFP, and ORF2p was tagged with a C-terminal mCherry (referred to as \u0026lsquo;RFP\u0026rsquo; in the figures for simplicity), enabling simultaneous detection of ORF1p and ORF2p protein levels. A downstream Hygromycin resistance gene tagged with a C-terminal BFP was included as a control to account for global changes in gene expression and normalization by viability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBecause increased expression of wild-type ORF2p can be lethal to cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], two versions of this construct were generated: one containing wild-type ORF2p and another with point mutations in the reverse transcriptase and endonuclease domains of ORF2p, rendering it catalytically inactive (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Stable cell lines were created by randomly integrating these constructs into the genome of HCT-116 colorectal cancer cells expressing Cas9. This cell line was selected due to its suitability for CRISPR screening and features potentially linked to LINE-1 regulation, such as an active p53 pathway [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. ORF1p expression was confirmed by western blot and flow cytometry in both wild-type (WT) and catalytically inactive (MT) LINE-1-expressing cells, while ORF2p expression remained undetectable, consistent with previous reports of low ORF2p levels in cell lines [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). Additionally, over 98% of cells were confirmed to be BFP-positive (data not shown).\u003c/p\u003e \u003cp\u003eTo confirm that the reporter constructs did not introduce global transcriptional changes, we performed gene expression profiling of cells expressing either WT or EN/RT MT ORF2p. Only a limited number of differentially expressed genes were observed; 118 between no reporter and L1 EN/RT MT, 60 between no reporter and L1 WT, and 28 between L1 EN/RT MT and L1 WT. This result indicates minimal perturbation and allows for downstream screening without complications (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eWe then performed a genome-wide CRISPR screen using both WT and EN/RT MT reporter cell lines. The primary goal was to identify genes whose loss altered ORF1p-GFP or ORF2p-RFP expression without affecting BFP levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In parallel, we aimed to uncover genes associated with LINE-1 activity-dependent lethality by comparing the WT and MT reporter lines. Enrichment or depletion of gRNAs in sorted populations was assessed by next-generation sequencing of the gRNA libraries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of Regulators of ORF1p and ORF2p\u003c/h3\u003e\n\u003cp\u003eThe genome-wide CRISPR screening, comprising over 80,000 gRNAs covering 20,000 genes with 4 gRNAs per gene, identified cell populations with modulation of ORF1p-GFP and/or ORF2p-RFP levels in both ORF2p WT and MT cell lines compared to parental lines lacking the reporter. Since ORF2p-RFP expression was not initially detectable, no cell populations with decreased ORF2p-RFP levels were observed. The cells were then sorted using FACS into three populations for both WT and MT cells: ORF1p decreased, ORF1p increased, and ORF2p increased. These sorted populations were subsequently subjected to gRNA sequencing and enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo facilitate data interpretation, LINE-1 regulators were categorized as either positive or negative regulators (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Positive regulators, when targeted (e.g., genetically knocked down) by gRNAs, resulted in the downregulation of ORF1p or ORF2p, whereas knockdown of negative regulators led to their upregulation. To validate the quality of the screening, we examined whether previously reported LINE-1 regulators were identified. Notably, all core components of the HUSH complex, known to restrict LINE-1 transcriptional activity through heterochromatin formation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], were identified as ORF1p negative regulators (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). This validation prompted further investigation into the list of newly identified LINE-1 regulators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe genome-wide screen identified 398 ORF1p positive regulators, 646 ORF1p negative regulators, and 93 ORF2p negative regulators in both WT and MT LINE-1 reporter cell lines, using a p-value cutoff of \u0026lt;\u0026thinsp;0.05 and a gRNA count\u0026thinsp;\u0026gt;\u0026thinsp;1, and filtering genes with TPM\u0026thinsp;\u0026lt;\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). To validate these LINE-1 regulators, a secondary CRISPR screen was performed using a more focused gRNA library. The selected gRNAs included potential hits from the primary screen (see Methods), known LINE-1 regulators, LINE-1 interactors, and controls such as essential genes and negative gRNAs. The library comprised a total of 15,000 gRNAs representing 1,490 genes, with 10 gRNAs per gene (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The same screening strategy outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was applied. The secondary screen successfully validated 13 ORF1p positive regulators, 194 ORF1p negative regulators, and 14 ORF2p negative regulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e-\u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePathway analysis of these validated hits revealed an enrichment of diverse pathways among ORF1p negative regulator hits (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Many of these pathways have been linked to regulation of LINE-1 levels and retrotransposition activity, including transcriptional regulation by TP53, epigenetic regulation, and viral infection [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, RNA metabolism proteins involved in stress granule formation and DNA repair proteins were identified among ORF1p negative regulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), suggesting a potential link to ORF1p in the regulation of cellular stress responses. However, no specific pathway enrichment was observed for ORF1p positive regulator or ORF2p negative regulator hits, potentially due to the low number of hits identified in these categories. ORF1p positive regulator hits included proteins related to RNA binding, transcription, and signaling, suggesting that its levels can be regulated at multiple levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Furthermore, ORF2p negative regulators included proteins involved in protein degradation, translation, signaling, and transcription. These hits suggest that ORF2p regulation may occur at the protein level and could be linked to signal transduction events in response to environmental cues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGenes Associated with LINE-1 Activity-Dependent Lethality\u003c/h3\u003e\n\u003cp\u003eSince ORF2p expression is toxic to cells, we hypothesized that any perturbation leading to WT ORF2p upregulation would result in a loss of cell viability, whereas this effect would not occur in MT ORF2p-expressing cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). To test this, we conducted a lethality screen in parallel with the reporter screen. We reasoned that this approach would be more sensitive than the ORF2p-RFP reporter screening, as cells might still be vulnerable to slight ORF2p activation even when the increase is not prominent enough to be detected by the reporter system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe lethality screen identified 415 hits from the genome-wide screen, 57 of which were validated by the secondary screen as described above (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Interestingly, although no clear pathways were enriched within this list (potentially due to the low number of hits and genetic/cellular background), several protein classes similar to those observed in ORF2p negative regulators were identified, including protein degradation, translation, and transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Additionally, proteins related to the mitochondrial electron transport chain (ETC) were identified, suggesting a potential link between energy metabolism and ORF2p expression. Some of these proteins also had relevant connections to cancer biology, consistent with previous reports suggesting a potential link between LINE-1 activity and cancer progression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eORF1p and ORF2p are differentially controlled by non-overlapping negative regulators\u003c/h3\u003e\n\u003cp\u003eIdentifying regulators of both ORF1p and ORF2p enabled us to compare their regulatory landscapes, challenging the common assumption that these proteins are co-regulated [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Strikingly, we observed no overlap between ORF1p and ORF2p regulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with the sole exception of UROD, a gene previously shown to cause autofluorescence upon knockout, and thus excluded from analysis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This lack of overlap may help explain the disproportionate production of ORF1p and ORF2p from the LINE-1 transcript [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], with ORF1p produced at levels approximately 180-fold higher than ORF2p [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Such imbalance likely reflects a regulatory mechanism in which cells limit ORF2p production through translational or post-translational repression while allowing robust ORF1p expression. These findings also suggest that ORF2p activity may be selectively induced under specific conditions to promote retrotransposition [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Together, our results support the conclusion that although ORF1p and ORF2p are derived from a single transcript, they are subject to distinct regulatory controls at the translational and post-translational level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe insights from this study have broad implications for understanding disease mechanisms and advancing therapeutic development of LINE-1 modulators (Fig. 6). The identification of distinct regulatory mechanisms for ORF1p and ORF2p provides new avenues for targeting LINE-1 in disease contexts where its activity is dysregulated, such as cancer, autoimmune disorders, and neurodegeneration. Importantly, this study demonstrates the potential to target ORF1p and ORF2p separately, recognizing that their contributions to disease may differ. For example, ORF1p’s RNA-binding and chaperone activity may be involved in regulating RNA dependent interferon signaling [50,51], while ORF2p’s enzymatic functions are more directly linked to genomic instability and cytoplasmic DNA-dependent \u0026nbsp;interferon signaling [8,21–23]. This distinction enables precision targeting of either protein based on their specific roles in disease progression. These findings not only enhance our understanding of LINE-1 biology but also pave the way for developing targeted therapies to address diseases where LINE-1 dysregulation plays a critical role.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe feature of regulators identified in this study offers a better understanding of LINE-1 biology and its relevance to diseases. The enrichment of RNA metabolism (stress granule) and DNA repair factors among ORF1p negative regulators underscores the importance of cellular stress response pathways in regulating LINE-1 ORF1p expression. However, no specific pathways were identified for the positive regulation of ORF1p or the negative regulation of ORF2p. This lack of enrichment may be due to the lower number of hits identified in these sets, or it may reflect the incidental nature of these regulatory mechanisms, which are likely context-dependent and influenced by cellular stress conditions and genetic backgrounds. For instance, the upregulation of ORF1p or the downregulation of ORF2p may not be actively controlled processes but rather byproducts of broader cellular responses to environmental or intrinsic stressors. In contrast, LINE-1 RNA, which correlates with ORF1p protein levels, appears to be tightly regulated, particularly under stress conditions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur identification of genes associated with LINE-1 activity-induced lethality further highlights the potential vulnerability of cells to unregulated LINE-1 function, particularly in the context of ORF2p expression. While ORF2p protein levels are typically tightly suppressed, even modest increases can compromise cell viability [31]. This finding suggests that cells may have evolved layers of regulatory safeguards to prevent toxic accumulation of ORF2p, whose endonuclease and reverse transcriptase activities can drive DNA damage and trigger cytoplasmic DNA sensing pathways [2,3,24,25]. The enrichment of factors related to protein degradation, transcriptional regulation, translation, and the mitochondrial electron transport chain among lethality-associated hits points to a complex interplay between LINE-1 activity and fundamental cellular homeostasis. These vulnerabilities could potentially be exploited therapeutically in cancers where LINE-1 activity is aberrantly activated, offering a strategy to selectively target cells with elevated LINE-1 activity through synthetic lethality approach.\u003c/p\u003e\n\u003cp\u003eThe regulators identified in this study provide a valuable resource for the retrotransposon field, offering significant insights into the molecular mechanisms governing LINE-1 levels and activity. As with any high-throughput screen, this study has some limitations, and potential false positives must be considered. For example, autofluorescence-related artifacts, such as those associated with UROD knockout [48], can confound fluorescence-based readouts and yield spurious results. This underscores the importance of validating the identified hits individually through controlled experiments in different cell types to ensure that the findings are robust and broadly applicable. This study serves as a foundation for further investigations.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLINE-1 (L1): Long Interspersed Nuclear Element-1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eORF1p: Open Reading Frame 1 Protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eORF2p: Open Reading Frame 2 Protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRT: Reverse Transcriptase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEN: Endonuclease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGFP: Green Fluorescent Protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRFP: Red Fluorescent Protein (mCherry)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBFP: Blue Fluorescent Protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWT: Wild-Type\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMT: Mutant\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHUSH: Human Silencing Hub\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFACS: Fluorescence-Activated Cell Sorting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRISPR: Clustered Regularly Interspaced Short Palindromic Repeats\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAt the time of the work, all authors were full-time employees of, and hold stock options of, ROME Therapeutics.\u003c/p\u003e\n\u003cp\u003eFunding Declaration: No funding to declare\u003c/p\u003e\n\u003cp\u003eEthics and Consent to Participate declarations: not applicable\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable\u003c/p\u003e\n\u003cp\u003eCompeting Interest declaration: None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, O.O., S.K.; methodology, O.O., C.C., C.A.\u003c/p\u003e\n\u003cp\u003esoftware, C.C., C.A.; investigation, O.O., C.C., S.K.; writing – original draft,\u003c/p\u003e\n\u003cp\u003eO.O., C.C., S.K.; visualization, O.O., C.C.; supervision, O.O., L.D., D.Z., K.K.L., H.K., S.K., reviewing \u0026nbsp;- original draft, all authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank Ethan Leef and Erika Whyte (ROME Therapeutics) for their support in logistics and coordination for the study, Eric Zigon (Wyss Institute) for his assistance with cell sorting, and Hakan Guney (ROME Therapeutics) for his expertise and support in flow cytometry. We also thank Rosana Kapeller (ROME Therapeutics) for critically reviewing the manuscript.\u003c/p\u003e"},{"header":"Methods ","content":"\u003cp\u003e\u003cstrong\u003eCell Culture\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCT116 cells (human colorectal carcinoma) were maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, and 1 mM L-glutamine at 37°C in a humidified incubator with 5% CO₂. Cells were routinely passaged every 2–3 days at 70–80% confluence to ensure healthy growth. Cells stably expressing Cas9 and dual-reporter constructs were selected with 100 µg/mL hygromycin and expanded for use in downstream experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReporter Cell Line Development\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCT116 cells stably expressing Cas9 were transfected with piggyBac-compatible dual-reporter constructs encoding LINE-1 wild-type (WT) or catalytically inactive (MT) versions along with piggyBac transposase S100 (System Biosciences). In these constructs, ORF1p was tagged with GFP and ORF2p with RFP. A BFP-tagged hygromycin resistance gene was included to control for global transcriptional changes. Cells were selected with 100 µg/mL hygromycin to generate stable cell lines and the reporter expression in these lines was validated by flow cytometry and western blot.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-Wide CRISPR Screen\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRISPR libraries containing ~80,000 sgRNAs (targeting ~20,000 genes, 4 sgRNAs per gene) and packaged into lentivirus were purchased from Cellecta (KOHGW-80K). They were transduced at a multiplicity of infection (MOI) of ~0.3 to achieve ~500-fold coverage. After transduction, cells were selected with 0.5 µg/mL puromycin for 5 days to eliminate non-transduced cells. The surviving cells were cultured for 14-18 days to maintain library representation. Cells were sorted by FACS into populations with increased or decreased ORF1p-GFP or ORF2p-RFP levels. Genomic DNA (gDNA) was extracted from sorted populations and subjected to next-generation sequencing to identify enriched sgRNAs. For the LINE-1 activity-dependent lethality screen, the unsorted cells 18 days post-infection was used for gRNA sequencing to identify depleted sgRNAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary CRISPR Screen\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHits from the primary screen that appeared in either LINE-1 activity-dependent lethality screen or in the L1 regulation screen performed in both WT or MT L1 reporter cell lines, including some select hits that appeared in only one replicate, as well as reported LINE-1 regulators, LINE-1 interactors, and additional controls targeting essential genes or negative gRNAs, were compiled into a custom library containing 15,000 sgRNAs (10 sgRNAs per gene) and cloned into the same vector used in the genome-wide screen, pRSGEP-U6-sg-EF1-Puro. HCT116 WT and MT reporter cells were infected at an MOI of ~0.3 with ~1,000-fold library coverage. Cells were selected with 0.5 µg/mL puromycin for 5 days, cultured for 12 days, and sorted into distinct reporter expression populations by FACS. The sorted populations were analyzed via sequencing to validate hits from the primary screen. For the LINE-1 activity-dependent lethality screen, the unsorted cells 18 days post-infection was used for gRNA sequencing to identify depleted sgRNAs and validate hits from the primary screen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNext-Generation Sequencing of gRNAs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll steps of next-generation sequencing were performed at Cellecta according to the manufacturer’s instructions. Briefly, genomic DNA (gDNA) was extracted from FACS-sorted populations using a column-based kit or phenol-chloroform extraction, depending on the number of cells. DNA quality and quantity were assessed using a Nanodrop and agarose gel electrophoresis. sgRNAs were then amplified using two sequential PCRs with outside primer sets containing Illumina adapters, as described in the Cellecta HTS6C (pRSG16/17) manual.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApproximately 5–10 µg of gDNA per reaction was used with ~25–28 cycles. Products (~200–300 bp) were verified by gel electrophoresis. PCR products were purified with AMPure XP beads and quantified using a Qubit fluorometer. Library size and quality were confirmed using a Bioanalyzer. Libraries were pooled equimolarly and sequenced on an Illumina platform (HiSeq or NovaSeq) using paired-end 50 bp or single-end 75 bp reads to achieve ~500–1,000 reads per sgRNA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Sorting\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCell sorting was performed using fluorescence-activated cell sorting (FACS) to isolate populations based on reporter expression with a Sony SH800 cell sorter. After genome-wide and secondary CRISPR screens, HCT116 cells expressing dual-reporter constructs were harvested and resuspended in FACS buffer (phosphate-buffered saline supplemented with 2% fetal bovine serum and 2 mM EDTA) at a concentration of 10 million cells per mL. Baseline fluorescence signals were determined using L1 reporter cell lines that were not infected with the CRISPR library. The CRISPR library-infected cell populations were then gated based on relative intensities compared to uninfected cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCell populations that did not alter Hygro-BFP expression were selected for sorting, and cells were sorted based on decreased ORF1p-GFP levels, increased ORF1p-GFP levels, and increased ORF2p-RFP levels. Each sorted population was collected into tubes containing complete RPMI medium (10% FBS, 1% penicillin-streptomycin) to maintain cell viability under sterile conditions. Sorted cells were flash-frozen and stored at -80°C for downstream analyses, including sgRNA amplification and next-generation sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow Cytometry Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFlow cytometry was performed on a Cytek Northern Lights to analyze ORF1p-GFP, ORF2p-RFP, and BFP control expression in HCT116 cells. Cells were harvested, washed with PBS, and resuspended in FACS buffer (PBS with 2% FBS). GFP and mCherry beads (Invitrogen) were used for compensation. Data were acquired for 20,000–50,000 events per sample and analyzed using FlowJo software.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRISPR screen analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing reads were demultiplexed, adapters were trimmed, and sgRNA sequences were mapped to the reference library. sgRNA abundance and differential enrichment were analyzed to identify candidate regulators. The Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK)\u0026nbsp;[52] (conda version, downloaded in Oct. 2023) was used to process sgRNA read counts. Initially, sgRNA read counts for individual samples were obtained using the “count” command in MAGeCK. A total of 580 negative and 40 positive control sgRNAs were included in both the primary and secondary screens.\u003c/p\u003e\n\u003cp\u003eDifferential selection analysis was performed using the “test” command in MAGeCK, with read counts normalized via total count normalization to account for sequencing depth. The “--control-sgrna” parameter was set to specify negative control sgRNAs. The robust rank aggregation (α-RRA) algorithm was applied to compute the RRA score and p-value, reflecting the degree of positive or negative selection. Additionally, the number of “good sgRNAs” (sgRNA abundance differs significantly following a negative binomial model) per gene was determined.\u003c/p\u003e\n\u003cp\u003eFor the LINE-1 ORF1p positive and negative regulator screens, as well as the ORF2p negative regulator screen, positively selected genes from the MAGeCK output were used. For the LINE-1 ORF2p lethality screen, negatively selected genes from the MAGeCK output were analyzed. In both the primary and secondary screens, genes meeting the criteria of p-value\u0026nbsp;≤\u0026nbsp;0.05 and\u0026nbsp;≥\u0026nbsp;2 good sgRNAs were considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-seq analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw RNA-seq reads in FASTQ format were first aligned to the human reference genome (hg38) using STAR (v2.7.11)\u0026nbsp;[53] with default parameters unless otherwise specified. The resulting BAM files were sorted and indexed using Samtools (v1.21)\u0026nbsp;[54]. Gene-level expression quantification was performed using Salmon (v1.10.2)\u0026nbsp;[55] in quasi-mapping mode with transcript-level TPM normalization. Differential expression analysis was conducted using DESeq2 (v1.30.1)\u0026nbsp;[56]\u0026nbsp;in R, where gene expression counts were normalized, and differentially expressed genes (DEGs) were identified based on a threshold of |log2FoldChange| \u0026gt; 1 and adjusted p-value \u0026lt; 0.01 (Benjamini-Hochberg procedure used to calculate the adjusted p-value).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional enrichment of gene sets was analyzed using Enrichr67 webserver (https://maayanlab.cloud/Enrichr/) against the Hallmark and GO biological process datasets. Pathways/GO terms with FDR \u0026lt; 0.1 were regarded as significantly enriched. Gene set enrichment analysis (GSEA) analysis was performed using function GSEA() from \u003cem\u003efgsea\u003c/em\u003e [57] package and visualized by gseaplot2() from \u003cem\u003eenrichplot\u003c/em\u003e [58] packages.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKazazian HH Jr, Moran JV. Mobile DNA in Health and Disease. N Engl J Med. 2017;377: 361\u0026ndash;370.\u003c/li\u003e\n\u003cli\u003eSimon M, Van Meter M, Ablaeva J, Ke Z, Gonzalez RS, Taguchi T, et al. LINE1 derepression in aged wild-type and SIRT6-deficient mice drives inflammation. Cell Metab. 2019;29: 871\u0026ndash;885.e5.\u003c/li\u003e\n\u003cli\u003eDe Cecco M, Ito T, Petrashen AP, Elias AE, Skvir NJ, Criscione SW, et al. L1 drives IFN in senescent cells and promotes age-associated inflammation. Nature. 2019;566: 73\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eDella Valle F, Reddy P, Aguirre Vazquez A, Izpisua Belmonte JC. 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LINE-1 promotes tumorigenicity and exacerbates tumor progression via stimulating metabolism reprogramming in non-small cell lung cancer. Mol Cancer. 2022;21: 147.\u003c/li\u003e\n\u003cli\u003eSherman EJ, Mirabelli C, Tang VT, Khan TG, Leix K, Kennedy AA, et al. Identification of cell type specific ACE2 modifiers by CRISPR screening. PLoS Pathog. 2022;18: e1010377.\u003c/li\u003e\n\u003cli\u003eAlisch RS, Garcia-Perez JL, Muotri AR, Gage FH, Moran JV. Unconventional translation of mammalian LINE-1 retrotransposons. Genes Dev. 2006;20: 210\u0026ndash;224.\u003c/li\u003e\n\u003cli\u003eYou E, Patel BK, Rojas AS, Sun S, Danaher P, Ho NI, et al. LINE-1 ORF1p mimics viral innate immune evasion mechanisms in pancreatic ductal adenocarcinoma. Cancer Discov. 2025. doi:10.1158/2159-8290.CD-24-1317\u003c/li\u003e\n\u003cli\u003eIshak CA, Marhon SA, Tchrakian N, Hodgson A, Loo Yau H, Gonzaga IM, et al. Chronic viral mimicry induction following p53 loss promotes immune evasion. 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Genome Biol. 2014;15: 550.\u003c/li\u003e\n\u003cli\u003eKorotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. bioRxiv; 2016. doi:10.1101/060012\u003c/li\u003e\n\u003cli\u003eYu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16: 284\u0026ndash;287.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"mobile-dna","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mdna","sideBox":"Learn more about [Mobile DNA](http://mobilednajournal.biomedcentral.com/)","snPcode":"13100","submissionUrl":"https://submission.nature.com/new-submission/13100/3","title":"Mobile DNA","twitterHandle":"@MobDNAjournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"LINE-1 (L1), Transposable elements, Retrotransposon, ORF1p, ORF2p, CRISPR screen, Transposable elements, DNA repair, stress granules, gene regulation, translation, transcription ","lastPublishedDoi":"10.21203/rs.3.rs-6726282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6726282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLong Interspersed Nuclear Elements-1 (LINE-1, L1) are transposable elements that make up roughly 17% of the human genome. These elements can copy and insert themselves into new genomic locations [1]. Typically, LINE-1 is repressed in healthy tissues but may become activated in various human diseases. LINE-1 expression has been associated with aging [2–4], neurodegenerative disorders [5–7], cancer [8–10], and autoimmune diseases [11,12]. Despite the strong association between LINE-1 expression and disease, the regulatory mechanisms controlling the expression of LINE-1-encoded ORF1p and ORF2p and the link between LINE-1 activity and cancer cell survival remain poorly understood. Elucidating these mechanisms will deepen our understanding of how LINE-1 contributes to disease pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify upstream regulators of LINE-1 and genes associated with LINE-1 activity-dependent lethality, we developed a dual-reporter system that simultaneously monitors the protein levels of LINE-1-encoded ORF1p and ORF2p (wild-type or catalytically inactive EN/RT mutant). Using genome-wide CRISPR/Cas9-based screens with this reporter system, we identified genes that control LINE-1 expression through multiple potential mechanisms, including their regulation at both RNA and protein levels. Besides known regulators like the HUSH complex, our screening uncovered previously unknown regulators of ORF1p and ORF2p, and revealed distinct mechanisms regulating expression of these proteins. We also identified genes whose disruption contributes to LINE-1 activity-dependent lethality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study offers a valuable resource for the retrotransposon field, providing new insights into the distinct molecular mechanisms regulating LINE-1-encoded ORF1p and ORF2p, and highlighting potential therapeutic targets for diseases driven by LINE-1 dysregulation.\u003c/p\u003e","manuscriptTitle":"Identification of Novel Regulators of LINE-1 Expression via CRISPR/Cas9 Screening","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 08:18:42","doi":"10.21203/rs.3.rs-6726282/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"decision","content":"Revision requested","date":"2025-06-27T12:40:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T18:27:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T15:34:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233798772348299124916288318685384321557","date":"2025-05-28T11:41:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180236866325659036971333797190605599492","date":"2025-05-28T10:55:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T10:14:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T08:52:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T06:02:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mobile DNA","date":"2025-05-22T15:06:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mobile-dna","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mdna","sideBox":"Learn more about [Mobile DNA](http://mobilednajournal.biomedcentral.com/)","snPcode":"13100","submissionUrl":"https://submission.nature.com/new-submission/13100/3","title":"Mobile DNA","twitterHandle":"@MobDNAjournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d84c84ae-22d8-4f45-b0ad-cf552bafe62b","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:07:56+00:00","versionOfRecord":{"articleIdentity":"rs-6726282","link":"https://doi.org/10.1186/s13100-025-00386-5","journal":{"identity":"mobile-dna","isVorOnly":false,"title":"Mobile DNA"},"publishedOn":"2025-12-05 15:58:22","publishedOnDateReadable":"December 5th, 2025"},"versionCreatedAt":"2025-06-04 08:18:42","video":"","vorDoi":"10.1186/s13100-025-00386-5","vorDoiUrl":"https://doi.org/10.1186/s13100-025-00386-5","workflowStages":[]},"version":"v1","identity":"rs-6726282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6726282","identity":"rs-6726282","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}