RNA-coupled CRISPR screens reveal ZNF207 as a regulator of LMNA aberrant splicing in progeria

Amit K Behera; Jeongjin J Kim; Shreya Kordale; Filip Pekovic; Arun Prasath Damodaran; Bandana Kumari; Sandra Vidak; Ethan Dickson; Mei-Sheng Xiao; Gerard Duncan; Thorkell Andresson; Tom Misteli; Eugene Valkov; Thomas Gonatopoulos-Pournatzis

doi:10.1016/j.molcel.2025.12.003

. Author manuscript; available in PMC: 2026 Mar 3.

Published in final edited form as: Mol Cell. 2025 Dec 30;86(1):41–59.e15. doi: 10.1016/j.molcel.2025.12.003

RNA-coupled CRISPR screens reveal ZNF207 as a regulator of LMNA aberrant splicing in progeria

Amit K Behera ^1,⁵, Jeongjin J Kim ^1,⁵, Shreya Kordale ^1,⁵, Filip Pekovic ^1,², Arun Prasath Damodaran ¹, Bandana Kumari ¹, Sandra Vidak ³, Ethan Dickson ¹, Mei-Sheng Xiao ¹, Gerard Duncan ⁴, Thorkell Andresson ⁴, Tom Misteli ³, Eugene Valkov ^1,², Thomas Gonatopoulos-Pournatzis ^1,^6,^*

PMCID: PMC12952242 NIHMSID: NIHMS2134080 PMID: 41475346

SUMMARY

Despite progress in understanding pre-mRNA splicing, the regulatory mechanisms controlling most alternative splicing events remain unclear. We developed CRASP-seq (CRISPR-based identification of regulators of alternative splicing with phenotypic sequencing), a method that integrates pooled CRISPR-based genetic perturbations with deep sequencing of splicing reporters, to quantitatively assess the impact of all human genes on alternative splicing from a single RNA sample. CRASP-seq identified both known and untested regulators, enriched for proteins involved in RNA splicing and metabolism. As a proof-of-concept, CRASP-seq analysis of the LMNA cryptic splicing event linked to progeria uncovered ZNF207, primarily known for mitotic spindle assembly, as a regulator of progerin splicing. ZNF207 depletion enhances canonical LMNA splicing and decreases progerin protein levels in patient-derived cells. We further show that ZNF207’s zinc-finger domain broadly impacts alternative splicing through direct interactions with U1 small nuclear ribonucleoprotein (snRNP) components. These findings position ZNF207 as a U1 snRNP auxiliary factor and demonstrate the power of CRASP-seq to uncover key regulators and domains of alternative splicing.

Graphical abstract

graphic file with name nihms-2134080-f0001.jpg

In brief

This study introduces CRASP-seq, an RNA-coupled CRISPR screening platform designed to systematically identify regulatory factors and domains of alternative pre-mRNA splicing. Using this technology, ZNF207 is identified as a bona fide splicing regulator through its interaction with U1 snRNP components. ZNF207 modulates aberrant LMNA splicing, a critical event linked to progeria.

INTRODUCTION

Gene expression fidelity and regulatory flexibility depend on precise coordination of pre-mRNA processing events, including capping, splicing, and RNA cleavage followed by polyadenylation. Central to these processes is the spliceosome, a dynamic complex of five small nuclear RNAs (snRNAs) and ~150 proteins organized into small nuclear ribonucleoproteins (snRNPs). These snRNPs assemble stepwise to remove introns: U1 and U2 snRNPs recognize the 5′ and 3′ splice sites, respectively, followed by recruitment of the U4/U6.U5 tri-snRNP complex, which triggers extensive rearrangements in protein and RNA interactions. These rearrangements culminate in two sequential transesterification reactions that excise introns and produce mature transcripts.^1–4

Alternative splicing enables a single gene to generate multiple mRNA isoforms, influencing both protein-coding and regulatory outcomes.^5–8 It is pervasive, affecting nearly all protein-coding genes,^9,10 and many alternative exons impact critical cellular phenotypes.¹¹ Splicing events can occur in several forms, such as the inclusion or skipping of “cassette” exons, mutually exclusive selection of adjacent exons, usage of alternative 5′ and 3′ splice sites, and differential intron retention.

Splice site selection is shaped by multiple factors, including RNA structure, transcription dynamics, chromatin state, and RNA-binding proteins that recognize specific cis-regulatory elements on pre-mRNA.^7,12 Additionally, core spliceosome proteins can selectively influence subsets of alternative splicing events, highlighting the diversity of regulatory mechanisms.^13–17

Aberrant splicing contributes to numerous human diseases, emphasizing the clinical importance of understanding RNA processing.^18,19 Hutchinson-Gilford progeria syndrome (HGPS) illustrates this: a de novo LMNA 1824C>T mutation activates a cryptic splice donor site, producing progerin, a truncated lamin A variant.^20,21 Progerin exerts dominant-negative effects, disrupting nuclear architecture and epigenetic regulation, ultimately causing early adolescent mortality due to cardiovascular complications.^20,21 Identifying splicing regulators that modulate progerin expression is thus critical.

Splicing dysregulation also contributes to the development of cancer and other diseases.^22–27 Examples include mutually exclusive exons in PKM^28,29 and cassette exons in FAS,³⁰ EZH2,³¹ and SRSF7,³² among many others. While these splicing events have been studied to varying degrees, the regulatory factors and pathways controlling most disease-associated splicing events remain largely unknown, partly due to a lack of cost-effective, high-throughput methods for quantitative assessment.

To address this, we developed CRISPR-based identification of regulators of alternative splicing with phenotypic sequencing (CRASP-seq), a methodology combining deep sequencing of splicing reporters with genome-wide CRISPR perturbations. It directly captures splice junctions and quantifies how the knockout of each human gene influences a given splicing event. Unlike arrayed screens, CRASP-seq requires only a single RNA extraction from pooled cells. Using this approach to study LMNA aberrant splicing, we identified ZNF207 as a positive regulator of progerin. Depletion of ZNF207 in HGPS-derived fibroblasts corrects LMNA aberrant splicing and reduces progerin protein expression. ZNF207 broadly regulates alternative splicing and directly binds U1 snRNP. Combining CRASP-seq with base editor-mediated high-throughput mutagenesis highlighted a point mutation in its second zinc-finger (ZnF) β sheet that abolishes U1 snRNP interaction and splicing regulation.

RESULTS

Establishing a robust screening platform for quantifying splicing regulation

We developed a scalable and quantitative platform to study alternative splicing regulation by integrating minigene reporters within genome-wide lentiviral CRISPR gene knockout libraries. This links splicing changes of inducible reporters to specific guide (g)RNAs, connecting gene knockouts with quantitative splicing inclusion levels (Figure 1A).

Figure 1. — (A) Schematic of the CRASP-seq vector integrating constitutive Cas9/Cas12a hgRNA expression under a U6 promoter. A doxycycline (Dox)-inducible TRE (tetracycline response element) promoter drives a splicing minigene reporter from the antisense (−) strand. The reverse tetracycline-controlled transactivator (rtTA) is fused to a puromycin resistance via T2A self-cleaving peptide, enabling selectable, inducible reporter expression. BGH, bovine growth hormone polyadenylation signal.

(B) Reverse-transcription PCR (RT-PCR) validation of *FAS* exon-6 and *EZH2* exon-14 splicing using CRASP-seq vectors in HAP1 cells co-expressing the indicated hgRNAs.

(C) Workflow of the CRASP-seq platform: splicing-derived mRNA products are prepared for Illumina paired-end sequencing, allowing precise association of each hgRNA within splicing outcomes. Element lengths indicated but not to scale.

(D) Gene Ontology (GO) enrichment of regulators identified for the *FAS* and *EZH2* splicing events; all terms listed in Figure S1H.

To achieve efficient gene inactivation, we used our combinatorial CRISPR screening tool, CHyMErA (Cas hybrid for multiplexed editing and screening applications), which co-expresses Cas9 and Cas12a nucleases alongside libraries of hybrid guide (hg)RNAs fused from Cas9 and Cas12a gRNAs transcribed from a single U6 promoter (Figure 1A).^11,33–35 The hgRNA is cleaved by Cas12a’s intrinsic ribonuclease activity^36,37 to release individual guides, enabling combinatorial knockouts. CHyMErA can achieve ultra-efficient gene knockout through dual targeting of individual genes with Cas9 and Cas12a gRNAs, surpassing conventional Cas9 screening platforms.^11,33 Additionally, CHyMErA allows us to target paralogous gene pairs and other genetic elements of interest.^11,33,34 We designed a genome-wide library of 95,893 hgRNAs, targeting 18,888 protein-coding genes (four distinct hgRNAs per gene), 2,357 paralogous gene pairs, and 430 intergenic and non-targeting controls (Table S1; STAR Methods). hgRNAs were cloned into a lentiviral vector containing unique 12-nt cell barcodes (CBCs) to track splicing levels within individual cell lineages and generate replicates (Figure 1A).

Reporter expression was controlled using lentiviral vectors containing a doxycycline-inducible tetracycline response element (TRE), a BGH polyadenylation signal, and reverse tetracycline-controlled transactivator (rtTA)-puromycin fusion separated by a 2A self-cleaving peptide (Figures 1A and S1A). Reporters were cloned on the minus strand of the lentiviral vector, ensuring that the viral genome remains unaffected by splicing and polyadenylation during virus production and packaging. In contrast, the hgRNA expression cassette is oriented on the plus strand to prevent Cas12a’s intrinsic ribonuclease activity^36,37 from recognizing and processing the Cas12a direct repeat (DR) within the reporter transcript.

To validate the system, we designed minigene reporters to monitor the splicing of FAS exon-6 and EZH2 exon-14, representing well-studied (FAS exon-6) and less characterized (EZH2 exon-14) alternative cassette exons linked to cancer.^30,31,38 These minigene reporters, like all others used in this study, contain the full native intronic sequences flanked by adjacent constitutive exons (Table S2). Knockout of known regulators (SF3B1/U2AF2 for FAS,¹⁵ SF3B3 for EZH2³¹) induces the expected exon skipping, confirming the robustness of the perturbation-readout methodology (Figure 1B).

Next, we incorporated FAS and EZH2 minigene reporters into our genome-wide knockout libraries, produced lentivirus, and transduced human HAP1 and RPE1 cells expressing Streptococcus pyogenes Cas9 and optimized Acidaminococcus sp. (op)Cas12a nucleases^11,39 at a low multiplicity of infection (MOI), ensuring at least 200-fold coverage. After puromycin selection and CRISPR editing, the reporters were induced with doxycycline for 24 h at two different time points (4 or 7 days after transduction). Reporter splicing reflects the regulatory effects of cellular genotypes influenced by hgRNAs.

Our workflow quantitatively assesses splicing levels by amplifying and sequencing reporter mRNAs linked to hgRNAs. The hgRNA expression cassette is positioned at the 3′ end of the reporter in the reverse complement orientation (Figure 1C). Total RNA extracted from cells transduced with the hgRNA genome-wide knockout lentiviral library is used to isolate mature poly(A)-tailed mRNA using oligo(dT). Subsequently, cDNA is synthesized using a customized primer containing 10-nt unique molecular identifiers (UMIs) to remove PCR duplicates during analysis. The resulting cDNA is then used to generate Illumina sequencing libraries. Paired-end sequencing quantitatively captures splicing outcomes (read 2) and associated hgRNAs (read 1), linking percent spliced-in (PSI) indices to specific perturbations (Figure 1C). Hits were defined as genes for which >50% of guides exceeded a ΔPSI threshold at 10% false discovery rate (FDR) (STAR Methods).

Comparing early (i.e., T1 or 4 days after transduction) and late (T4, 7 days) time points shows a strong overall correlation; however, common essential genes exhibit a notably lower correlation (Figure S1B). Additionally, we observed a marked reduction in the number of identified gene hits at the later time point, accompanied by a significant depletion of core-essential gene hits (p = 0.0092; Fisher’s exact test; Figure S1C). Gene Ontology (GO) analysis of T1-specific hits reveals enrichment of spliceosome-related genes (Figure S1D). The number of cells carrying guides against core spliceosome components is markedly reduced at T4 relative to T1, whereas cells with intergenic guides increased over time (Figure S1E). These observations suggest that early time points, when cell clones with perturbations in core-essential genes—including many splicing factors—are still maintained, are preferable for knockout-based CRASP-seq screens. Accordingly, we selected 4 days post-transduction (T1) as the optimal time point for inducing the minigene reporter, ensuring detection of essential gene regulatory roles.

FAS exon-6 has been extensively studied, which guided its selection during the optimization of our experimental pipeline.¹⁵ Our screen shows a significant overlap of identified regulators with a previous genome-wide small interfering RNA (siRNA) screen (p value < 0.00001; odds ratio = 3.34; Fisher’s exact test), and an overall positive correlation between these two orthogonal screening methodologies (Figures S1F and S1G), despite differences in gene perturbation methods and cell lines. GO analysis of hits reveals strong enrichment of RNA splicing and processing terms, validating our approach (Figures 1D, S1F, and S1H; Table S3).

Unexpected enrichment of translation-related factors suggested that reporter transcripts might be subject to nonsense-mediated decay (NMD), as a previously described⁴⁰ cryptic 139-nt intron in the U6 promoter is excised downstream of the stop codon (Figure S2A). Cycloheximide treatment increases spliced transcript detection, confirming NMD (Figure S2B). To address this issue, we mutated the cryptic 3′ splice site in the reporter, successfully preventing splicing of the U6 promoter (Figures S2A and S2B) without compromising the functionality of our CRISPR vectors (Figure S2C). We then re-cloned the CRISPR genome-wide knockout library, CBCs, as well as the FAS and EZH2 minigene reporters into this modified vector and repeated the screens. While the correlation between the two screens remains strongly positive (Figure S2D), the previously observed strong enrichment of translation-related factors is largely eliminated (Figure S2E). This suggests exon-inclusion isoforms are more prone to NMD, consistent with previous reports.^41–44 Therefore, all subsequent screens used this modified backbone.

In summary, we developed CRASP-seq, which combines deep sequencing of splicing reporters with genome-wide perturbations. CRASP-seq enables a comprehensive, quantitative assessment of how every human protein-coding gene affects splicing events of interest from a single RNA extraction sample.

Application of CRASP-seq to disease-relevant splicing events

To assess the versatility of CRASP-seq across diverse splicing events, we cloned three additional splicing reporters into genome-wide lentiviral vectors: (1) the mutually exclusive PKM exon-9/10, directing distinct metabolic pathways^28,45,46; (2) a poison cassette exon in SRSF7 introducing a premature stop codon triggering NMD^47,48; and (3) LMNA exon-11, both wild-type (WT) and with a synonymous point mutation activating a cryptic 5′ splice site, producing progerin^20,21,49 (Figure 2A; Table S2).

Figure 2. — (A) Schematic of six splicing minigene reporters, including *LMNA* wild-type (WT) and mutant cryptic splice sites.

(B and C) Heatmaps of genes identified by CRASP-seq as regulators of PKM (B) or SRSF7 (C) isoform expression (ΔPSI relative to intergenic controls). For *PKM*, genes promoting exon-10 (PKM2, red) or exon-9 (PKM1, blue) inclusion are shown. For *SRSF7*, ΔPSI values with/without cycloheximide are shown, with poison exon-promoting genes in blue. Genes validated by RT-PCR are bolded (see Figure S3).

(D) Correlation of CRASP-seq ΔPSI with RT-PCR of endogenous events in HEK293T cells (Figure S3).

(E) Upset plot showing overlap of regulators across alternative splicing events.

(F) Heatmap of pairwise ΔPSI correlations across reporters in HAP1, RPE1, and HepG2 cells; sub-clusters annotated with molecular signatures database (MSigDB) terms. ****p value < 0.0001; individual genes are shown on the right.

All reporters were successfully cloned, maintaining tight hgRNA distribution (Figure S3A). Lentivirus was produced and used to transduce HAP1, RPE1, or HepG2 cells expressing Cas9 and Cas12a, following the same experimental and analytical pipeline described above. For the SRSF7, screens were performed both in the absence and presence of cycloheximide treatment, to allow or inhibit NMD, respectively.

Consistent with the FAS screen (Figures S1F, S1G, and S3B), all assays identified known regulators: SRSF3,⁴⁶ HNRNPA1, and PTBP1²⁸ activate PKM exon-10 (Figure 2B), SF3B3³¹ knockout reduces EZH2 exon-14 inclusion (Figure S3C), and SRSF7 strongly regulates its own poison exon^47,50 (Figure 2C). Translation-related genes are enriched among factors promoting SRSF7 poison exon inclusion in untreated cells (Figure S3D), reflecting known NMD dependence.^51–53 Reverse-transcription PCR (RT-PCR) assays confirmed that cycloheximide treatment increases exon inclusion, while SRSF7-targeting hgRNAs reduce it (Figure S3E), validating our perturbation-readout approach.

The profiled reporters identified several regulators, which, to our knowledge, have not been previously associated with the corresponding events. For instance, RBM39, a U2 snRNP auxiliary factor,^54–56 activates PKM exon-10, while ILF3 and HNRNPK repress it. These findings were validated through RT-PCR assays monitoring the endogenous PKM mutually exclusive exons in HEK293T cells (Figure S3F). Since proliferative cells predominantly use PKM exon-10 (PSI > 95%), PSI differences following ILF3 or HNRNPK depletion are minimal. However, co-depletion with SRSF3—a factor promoting exon-10 inclusion—reveals a pronounced repressive effect of ILF3 and HNRNPK on exon-10 (Figure S3F). In contrast, knockdown of the splicing regulator RBFOX2—a negative control not identified as a hit in the CRASP-seq screen—does not substantially alter PKM exon splicing (Figure S3F). These data validate ILF2 and ILF3 as negative regulators of PKM2 (the exon-10 containing isoform) and align with recent studies showing selective regulation of mutually exclusive exons by ILF2/ILF3.⁵⁷ Together, these findings underscore the high sensitivity of CRASP-seq.

Additionally, RBM39, XAB2, and SRSF7 were among several untested regulators of EZH2 exon-14 (Figure S3C). The involvement was confirmed by monitoring endogenous EZH2 exon-14 in HEK293T and patient-derived renal cell carcinoma lines, where aberrant activation of this exon has been linked to a poor prognosis³¹ (Figure S3G). Consistent with our screen (Figure S3C), RBM39 exhibits a stronger effect in promoting EZH2 exon-14 inclusion than SF3B3 (Figure S3G), the only previously known regulator of this event.³¹

Finally, we identified RBM39, SF3B1, SF3B3, and TNPO3—a gene encoding a nuclear import receptor for serine/argininerich (SR) proteins^58,59—as regulators of the SRSF7 poison exon (Figure 2C). RT-PCR assays monitoring endogenous SRSF7 confirmed that siRNA-mediated knockdown of these genes reduces poison exon inclusion in an NMD-independent manner (Figure S3H). Collectively, these validations underscore the efficacy of CRASP-seq in uncovering regulators of splicing events (Figure 2D).

Across the five screens, 370 alternative splicing regulators were identified, with substantial overlap across cell lines (Figures S4A and S4B; Table S3). Principal-component analysis indicates that the specific splicing event primarily determines perturbation effects (Figure S4C). Consistent with studies showing that core spliceosome machinery can modulate alternative splicing events,^13–17 shared regulators are enriched for core spliceosome proteins, while event-specific regulators include additional factors (Figures 2E, S4D, and S4E). GO analysis of all hits reveals strong enrichment in RNA splicing and processing, along with RNA metabolism and gene regulation (Figures S4F–S4H). Enrichment for DNA transcription-related genes likely reflects contributions to spliceosome biogenesis,^60,61 indirect regulation of key splicing factors, or even direct effects via RNA binding.^62–66 Domains enriched among hits include RNA recognition motif (RRM) and C3H ZnFs, consistent with RNA-mediated regulation (Figure S4I).

Pairwise analysis of PSI correlations across events revealed functionally associated clusters. U2 snRNP components (SF3B/SF3A subcomplexes, SNRPA1, PHF5A) cluster with auxiliary U2AF2 and the mediator complex (Figure 2F), supporting the mediator’s role in U2 snRNA expression.^60,61 SF3B6 associates more with U5 snRNP components (PRPF8, EFTUD, DDX23) than other U2 subunits, in line with recent studies.¹⁷ Correlated perturbations are also observed among NMD components (UPF1, UPF2, SMG1, SMG6, SMG7), ribosomal proteins, and translation factors. These results demonstrate CRASP-seq’s effectiveness and its potential to reveal functional interactions among splicing regulators.

ZNF207 regulates LMNA aberrant splicing in progeria

A de novo, synonymous mutation (1824C>T) in LMNA activates a cryptic 5′ splice site, causing HGPS.^20,21 To identify regulators of LMNA aberrant splicing, we performed a CRASP-seq assay using a minigene reporter containing this mutation.⁴⁹ The screen revealed multiple genes that regulate LMNA splicing, with several restoring splicing to WT levels despite the mutation (Figure 3A; Table S3). In contrast, the WT reporter consistently produces the canonical LMNA isoform, with no knockouts promoting progerin expression (Figure S5A). The genes affecting LMNA aberrant splicing are enriched for RNA splicing and gene regulation-related functions (Figure S5B). Among them, ZNF207, also known as BuGZ, was previously characterized as a mitotic spindle assembly protein due to its stable association with BUB3 via a Gle2-binding-sequence (GLEBS) motif^67,68 (Figure 3B). ZNF207 is a ZnF protein, which also functions as a transcription factor,^69,70 binds RNA, co-purifies with splicing-related proteins, and influences RNA metabolism and splicing.^70–76 Given its high score in the CRASP-seq screen (Figure 3A), we investigated ZNF207’s role in LMNA aberrant splicing and broader alternative splicing regulation.

Figure 3. — (A) Heatmap of ΔPSI for mutant *LMNA* CRASP-seq screen hits in HAP1, RPE1, and HepG2 cells.

(B) Schematic of ZNF207 protein structure, highlighting the C2H2 zinc-finger (ZnF) domains, the charged helical region, low-complexity regions, and the Gle2-binding-sequence (GLEBS) motif.

(C) RT-PCR of *LMNA* splicing in RNA from HGPS patient-derived fibroblasts treated with three independent siRNAs. Quantifications of PSI values from three independent experiments are presented as mean ± standard deviation (SD). ****p value < 0.0001; Dunnett’s multiple comparisons test.

(D) RT-PCR of *LMNA* splicing in RNA from HGPS patient-derived fibroblasts after ZNF207 knockdown and rescue with siRNA-resistant ZNF207 ORF. Quantification of PSI values from three independent experiments is presented as mean ± SD. ****p value < 0.0001; Dunnett’s multiple comparisons test.

(E) Western blot of lamins in HGPS fibroblasts with siRNA-resistant 3×FLAG-ZNF207 or empty vector, with/without siZNF207. Quantification normalized to β-actin and siControl from three independent experiments is presented as mean ± SD. *p value < 0.05, ****p value < 0.0001; Uncorrected Fisher’s least significant difference (LSD) following one-way ANOVA.

To validate ZNF207, we knocked it down with three independent siRNAs in HEK293T cells expressing the LMNA reporter, resulting in significant correction of LMNA splicing (Figures S5C and S5D). Expression of an siRNA-resistant ZNF207 open reading frame (ORF) (Figure S5E) partially rescues aberrant splicing (Figure S5F). Using immortalized fibroblasts from HGPS patients,⁷⁷ all three siRNAs restore endogenous LMNA splicing (Figures 3C and S5G), an effect reversed by ectopic expression of siRNA-resistant ZNF207 (Figures 3D and S5H). Knockdown also significantly reduces progerin protein levels (Figure 3E), despite its well-documented stability,^78,79 an effect reversed upon reintroducing ZNF207 (Figure 3E). These results confirm that ZNF207 depletion enhances canonical LMNA splicing, validating the screen.

To determine whether depleting ZNF207 could rescue molecular phenotypes associated with progeria, we assessed its impact on hallmark disease features in immortalized fibroblasts. Progerin disrupts nuclear envelope morphology and key epigenetic markers, including H3K27me3 and H4K16ac, alongside reduced levels of lamina-associated polypeptide 2α (LAP2α) and lamin B1 (LB1).^49,79–81 HGPS fibroblasts display mild phenotypes (Figure S5I). Long-term (i.e., 15 days) knockdown of ZNF207 in healthy fibroblasts reduces LAP2α, H3K27me3, and H4K16ac, reflecting senescence-associated changes. Consistent with its essential nature, ZNF207 depletion in HGPS-derived fibroblasts also lowers LAP2α and H4K16ac levels and does not restore H3K27me3 or LB1 levels (Figure S5I). Overall, ZNF207 depletion effectively corrects LMNA aberrant splicing, though sustained loss impairs cell fitness, consistent with its essential role.⁸²

ZNF207 depletion results in widespread alternative splicing changes

To assess the global impact of ZNF207 on splicing, we depleted it in HEK293T cells using three independent siRNAs. RNA-seq identified thousands of affected splicing events, predominantly skipped exons, along with alternative 5′ and 3′ splice sites and intron retention (Figure 4A; Table S4). The three siRNAs display significant overlap and highly correlated PSI values (Figures S6A and S6B).

Figure 4. — (A) Distribution of alternative splicing event types (CEs, cassette exons; Alt3, alternative 3′ splice sites; Alt5, alternative 5′ splice sites; IR, retained introns) detected by RNA-seq following *ZNF207* knockdown for 48 h with three independent siRNAs in HEK293T cells. Significant splicing changes: |ΔPSI| ≥ 10; probability ≥ 0.99.

(B) Western blot of ZNF207 in HEK293 Flp-In cells expressing Dox-inducible, siRNA-resistant 3×FLAG C-terminal tagged ZNF207 ORF. Cells were treated with control siRNA or siRNA targeting endogenous *ZNF207* (siZNF207) for 48 h. Blots were probed with antibodies against ZNF207, FLAG, and GAPDH (loading control).

(C) RNA-seq analysis of splicing changes (ΔPSI) upon *ZNF207* knockdown and rescue in HEK293 Flp-In cells. Events with significant splicing changes (|ΔPSI| ≥ 10; probability ≥ 0.95) upon siZNF207 knockdown and rescue by ZNF207 ORF expression are displayed.

(D) RT-PCR analysis of ACLY (left) and SRRM1 (right) splicing in HEK293 Flp-In cells treated with ZNF207-targeting siRNA and/or expressing siRNA-resistant C-terminally 3xFLAG-tagged ZNF207 ORF. Quantification of PSI values from three independent experiments is presented as mean ± SD. *p < 0.05, **p < 0.01, ****p < 0.0001; Dunnett’s test.

(E) Gene expression analysis (Z score normalized) from RNA-seq profiling of HEK293 Flp-In cells treated with ZNF207-targeting siRNA and/or rescued with siRNA-resistant ZNF207 ORF expression. Only genes with significant expression changes (adjusted p < 0.05; |log₂FC| ≥ 0.35) upon siZNF207 and rescued by ZNF207 expression are shown. log₂FC, log₂ fold change.

(F) RT-PCR of LUC7L, LUC7L2, and SNRNP70 splicing in HEK293 Flp-In cells treated with ZNF207-targeting siRNA and/or expressing siRNA-resistant ZNF207 ORF. Experiments were conducted with and without the NMD inhibitor SMG1i (0.5 μM, 6 h). Quantification of PSI values from three independent experiments is shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; Šídák’s test.

(G) *In vitro* splicing of transcribed mutant LMNA reporters incubated with HeLa nuclear extracts, either mock-treated or ZNF207-depleted. Splicing outcomes were quantified by RT-PCR, with all three products—unspliced, canonical, and aberrantly spliced LMNA—indicated. PSI values for the canonical splice site use are represented as mean ± SD; *p < 0.05, Welch’s t test.

To rule out off-target effects, we reintroduced an siRNA-resistant ZNF207-FLAG ORF using the Flp-In system. ZNF207-FLAG was expressed at near-endogenous levels (Figure 4B) and rescued most splicing changes, defining over 800 ZNF207-regulated events (Figures 4C and S6C; Table S5). Overexpression of ZNF207 alone causes minimal changes (Figures S6C and S6D). RT-PCR validations confirmed our sequencing data, as well as the successful rescue by C-terminally tagged ZNF207-FLAG (Figure 4D). In contrast, N-terminally tagged FLAG-ZNF207 fails to rescue, despite successful expression of the ZNF207 transgene, suggesting interference with the splicing activity of the protein (Figures S6E and S6F). Together, these data establish ZNF207 as a key regulator of alternative splicing.

Because ZNF207 has been proposed to act as a transcription factor,^69,70 we examined whether it regulates splicing indirectly via transcriptional control of splicing factors. Transcriptome analysis identified 543 protein-coding genes regulated by ZNF207 (Figures 4E and S7A; Table S6), modestly enriched in amino acid metabolism pathways (Figure S7B). Only a few—such as LUC7L, LUC7L2, SNRNP70, and RBM3—are splicingrelated, and all also exhibit ZNF207-dependent splicing changes that likely trigger NMD (Figures S7C and S7D). RT-(q)PCR confirmed these splicing alterations and their impact on transcript stability via NMD (Figures 4F and S7C–S7E). These findings suggest that ZNF207 does not influence splicing by controlling the transcription of splicing-related genes, but rather affects transcript stability, at least in part, through alternative splicing coupled to NMD.

ZNF207 is known to interact with BUB3 in the mitotic checkpoint.^67,68 To test whether its splicing role depends on this interaction, we depleted BUB3 by RNAi. BUB3 loss does not affect LMNA or other ZNF207-regulated splicing events (Figures S8A and S8C), indicating independence from its kinetochore functions.

Finally, to test whether ZNF207 directly influences splicing, we depleted it from HeLa nuclear extracts using specific antibodies (Figure S8D) and performed in vitro splicing assays with LMNA mutant transcripts. ZNF207 depletion increases canonical LMNA splicing and reduces progerin isoform formation (Figure 4G), consistent with cellular results (Figures 3C and 3D). Together, these findings show that ZNF207, beyond its roles in mitosis and transcription, directly regulates pre-mRNA splicing.

ZNF207 binds regulated exons and associates with the splicing machinery

To investigate how ZNF207 regulates splicing, we performed TurboID biotin proximity labeling (Figure 5A) in HEK293 Flp-In cells expressing miniTurbo-tagged ZNF207 at near-endogenous levels (Figure S9A). Biotinylated proteins were captured and analyzed by mass spectrometry in triplicate; only proteins enriched in all replicates were considered ZNF207 proximal interactors (STAR Methods). We identified 187 proximal proteins (Figure 5B; Table S7), including kinetochore factors such as BUB3, transcription and chromatin regulators, and—most notably—multiple pre-mRNA splicing factors, including U1, U2, and U4/U6.U5 snRNPs and auxiliary splicing regulators. GO analysis confirmed strong enrichment for RNA processing and spliceosome-related terms (Figures 5C and S9B).

To validate these findings, we performed co-immunoprecipitation experiments in HEK293 Flp-In cells expressing FLAG-tagged ZNF207 (Figure 4B). These experiments confirmed that ZNF207 co-purifies with several endogenous splicing factors tested, including U1-related U1-A (SNRPA), U1–70K (SNRNP70), RBM25; U2-related SF3B1; U5-related SNRNP200, and PRPF6 as well as SNRPF, in an RNA-independent manner (Figure 5D). These results indicate that ZNF207 physically interacts with the splicing machinery.

To further investigate the direct role of ZNF207 in alternative splicing regulation, we performed enhanced crosslinking and immunoprecipitation followed by sequencing (eCLIP-seq). We used our HEK293 Flp-In cells stably expressing FLAG-tagged ZNF207 for RNA immunoprecipitation. As controls, we included cells expressing GFP-FLAG as well as N-terminal FLAG-tagged ZNF207, which is splicing-incompetent (Figures S6E and S6F). The eCLIP-seq analysis reveals that ZNF207 preferentially binds to protein-coding genes (Figure 5E). Among the detected peaks, we observed an approximately equal distribution between exonic and intronic binding (Figure S9C). Strikingly, the binding maps for ZNF207 show a binding enrichment over the coding sequences of regulated exons, as compared with exons unaffected by ZNF207 depletion (Figures 5F and S9D). This signal is primarily driven by cassette exons activated by ZNF207 (Figures S9D and S9E), which represent the majority of splicing events regulated by ZNF207 (Figure 4A; Tables S4 and S5). This binding pattern is unique to the splicing-competent C-terminal FLAG-tagged ZNF207 and absent in the splicing-incompetent N-terminal FLAG-tagged variant or the GFP-FLAG negative controls. These results suggest that ZNF207 binding to regulated exons may be important for its role in splicing regulation.

CRASP-seq coupled to high-throughput mutagenesis identifies key regions of ZNF207 for splicing regulation

To dissect the regions of ZNF207 critical for splicing, we performed a high-throughput tiling mutagenesis screen, leveraging recent advances in genome editing.^85–87 For this screen, we replaced the CHyMErA loss-of-function hgRNA cassette in our CRASP-seq vectors with a tiling Cas9 base editor library (Figure 6A). The library targets ZNF207, representative U1, U2, and U5 snRNP genes identified by TurboID, and top hits from the LMNA CRASP-seq screen, totaling 39 genes and 8,594 gRNAs, including controls (Table S8).

Figure 6. — (A) CRASP-seq base editor tiling schematic. A lentiviral vector enables constitutive expression of sgRNAs, while a Dox-inducible TRE promoter drives the expression of a mutant *LMNA* minigene reporter from the antisense (–) strand. RNA products are analyzed via Illumina paired-end sequencing.

(B) Gene-level ΔPSI scores for all the 39 profiled genes. Intergenic and non-targeting controls are also included.

(C) ΔPSI for ZNF207-targeting sgRNAs (excluding splice-site-overlapping guides). Average ΔPSI scores for sgRNAs within 10-amino-acid windows are plotted, with individual sgRNA scores indicated by red (adenine) or black (cytosine) dots.

(D) Schematic representation of ZNF207 constructs analyzed in this figure.

(E and F) RT-PCR of ACLY (top) and SRRM1 (bottom) splicing in HEK293 Flp-In cells (E) or HEK293T cells (F) treated with ZNF207-targeting siRNA and/or expressing siRNA-resistant WT or truncation mutant ZNF207 ORFs. Quantification of ΔPSI values from three independent experiments is presented as mean ± SD. Statistical comparisons were performed relative to the siZNF207 sample without ZNF207 ORF expression.

(G) Biotin-based RNA labeling of UV-crosslinked RNA bound to ZNF207 truncation mutants. Pull-down efficiency was assessed by FLAG western blot. Quantification from three independent experiments is presented as mean ± SD. Statistical comparisons were performed relative to WT.

**p < 0.01, ***p < 0.001, ****p < 0.0001; Dunnett’s test.

Following library cloning and LMNA minigene incorporation into the CRASP-seq vector, we conducted screens in HAP1 cells expressing highly efficient adenine (i.e., ABE8e) and cytosine (i.e., evoCDA1) base editors, as we established previously.¹¹ Gene-level analysis reveals that guides targeting ZNF207 lead to the greatest reduction in progerin isoform expression (Figure 6B; Table S8). In contrast, mutagenesis of SRRT, SRSF3, and ZC3H18 produces the largest progerin isoform increases, consistent with findings from the CHyMErA loss-of-function screen (Figure 3A). These concordant results underscore the robustness of our screening platforms, despite employing different gRNA libraries and approaches.

A detailed analysis of ZNF207-targeting gRNAs reveals that regions overlapping the ZnF domains have the most pronounced impact on LMNA splicing when mutated (Figure 6C; Table S8). ZNF207 is predicted to be predominantly a disordered protein, containing multiple low-complexity regions⁸⁸ (Figures S10A and S10B). Intriguingly, HydRA analysis, a tool for predicting RNA-binding proteins and associated domains,⁸⁹ identifies portions of these disordered regions, including the C-terminal region, as potential RNA-binding sites (Figure S10B).

To validate functional regions, we generated eight truncation mutants of ZNF207 targeting critical regions, including the ZnF domains, GLEBS motif, and C-terminal region (Figure S10C). Transient transfection in ZNF207-depleted HEK293T cells revealed that only the ZnF domains are essential for alternative splicing activity (Figures S10D and S10E). Stable Flp-In cell lines expressing these mutants at near-endogenous levels confirm the ZnF domains as necessary for splicing rescue (Figures S10F and S10G).

A gRNA overlapping the second ZnF domain (targeting amino acids H41, K42, K43) has the strongest impact on LMNA splicing. To validate the functional importance of this region, we generated a panel of eleven ZNF207 mutants, each containing single, dual, or triple predicted amino acid substitutions within this span. These constructs were expressed in cells depleted of endogenous ZNF207 to assess their ability to rescue splicing activity. Constructs bearing even a single amino acid substitution (i.e., K42E) completely lose splicing regulatory activity, as measured by the modulation of ZNF207 splicing targets such as ACLY and SRRM1 (Figures S11A–S11D). Stable cell lines expressing WT or K42E ZNF207 confirm that only WT rescues endogenous targets (Figures 6D, 6E, S12A, and S12B).

K42 resides within the β sheet of the second ZnF domain, and its substitution with glutamic acid does not disrupt domain folding. To assess whether K42’s function depends on charge, we generated K42M and K42R mutants. While K42M fails to rescue ZNF207 depletion-induced splicing defects, K42R fully restores activity, indicating that K42’s positive charge is essential—likely mediating electrostatic interactions with nucleic acids (Figures S11C and S11D). These results highlight the power of CRASP-seq to pinpoint critical amino acid residues within splicing regulators.

Taken together, the above data indicate that the ZnF domains are essential for ZNF207’s ability to regulate splicing. To test sufficiency, truncation mutants containing ZnF domains alone or the ZnF domains followed by the adjacent helical region (ZnF + Hel; amino acids 1–122) were assessed (Figure 6D). The ZnF domains alone are unable to rescue the splicing, whereas ZnF + Hel fully restores splicing following transient transfection in a K42-dependent manner (Figure 6F). Accordingly, expression of the ZnF + Hel truncation mutant at near-endogenous levels using Flp-In cells (Figure S12B) partially rescues splicing changes induced by endogenous ZNF207 depletion (Figure S12C). These results indicate that the N-terminal ZnF + Hel region is largely sufficient for ZNF207’s splicing regulatory function, though the C-terminal disordered region may contribute to full activity.

Collectively, these data define the ZnF domains to be essential for ZNF207’s role in alternative splicing and demonstrate that CRASP-seq high-throughput mutagenesis can pinpoint critical residues within splicing regulators.

ZNF207 engages in critical interactions with U1 snRNP

To investigate how ZNF207 affects splicing, we leveraged the splicing-inactive mutants described above. Using stable Flp-In cell lines, we performed in vivo RNA crosslinking assays and co-immunoprecipitation experiments to assess the effects of these mutations on ZNF207’s interactions with RNA and spliceosomal proteins, respectively. RNA crosslinking experiments reveal that constructs lacking the ZnF domains bind RNA as strongly as WT (Figure S12D) yet fail to rescue splicing (Figure S10G), whereas the splicing-competent ZnF + Hel mutant displays weaker but ZnF-dependent RNA binding (Figure 6G). Similarly, the splicing-defective K42E mutant retains RNA binding in the full-length protein but loses it within the ZnF + Hel truncation, suggesting that ZnF-mediated RNA contacts are critical for function (Figure 6G). These findings indicate that ZNF207 engages RNA through multiple regions, but only ZnF-dependent interactions contribute to splicing regulation.

We next examined how the ZNF207 mutations affect its interactions with spliceosomal proteins. Co-immunoprecipitation analyses reveal that the ZnF domains are specifically required for association with U1 snRNP components (U1-A, U1-C, and U1–70K), while being dispensable for interaction with U2 components such as SF3B1 (Figures S13A and S13B). Strikingly, the K42E mutation disrupts these U1 snRNP interactions, mirroring its loss of splicing activity (p < 0.05; two-way ANOVA; Figures 7A and 7B). However, in the context of the active ZnF + Hel truncation, K42E completely abolishes association with the splicing machinery (Figure S13C). These data suggest that ZNF207 engages the spliceosome through two distinct mechanisms: (1) via the ZnF domains, which mediate critical U1 snRNP-dependent interactions essential for splicing activity, and (2) via the C-terminal intrinsically disordered region (IDR), which contributes additional interactions that appear largely dispensable for ZNF207’s splicing regulatory role, as indicated by our RT-PCR analyses (Figures 6E and 6F).

RNA immunoprecipitation confirmed that the ZnF domain binds U1 snRNA (Figure 7C). To probe the role of individual U1 components, we depleted U1–70K, U1-A, and U1-C and examined ZnF + Hel interactions with U1 snRNP proteins. Loss of U1–70K markedly reduces ZnF + Hel association with both U1-A and U1-C, the latter likely indirectly due to reduced U1-C expression (Figures 7D and 7E). These results indicate that U1–70K is the primary mediator linking ZNF207 to the U1 snRNP complex.

To examine direct interactions between ZNF207 and U1 snRNP, we purified recombinant ZNF207 proteins (WT and K42E mutant) in both full-length and ZnF + Hel truncation forms. In parallel, U1–70K, U1-A, and U1-C proteins were purified, and U1 snRNA was generated by in vitro transcription. Pull-down assays demonstrate a direct ZNF207-U1–70K interaction that is enhanced by U1 snRNA and requires an intact ZnF domain (Figure 7F). No interaction is detected with U1-A or U1-C, except for a weak, RNA-dependent association with U1-A. The ZnF + Hel truncation retains the ability to interact with U1–70K, and this association is again enhanced by U1 snRNA (Figure S13D).

We next tested whether ZNF207 binds directly to U1 snRNA. Electrophoretic mobility shift assays (EMSAs) show that only WT, not K42E, ZnF + Hel binds U1 snRNA in vitro (Figures 7G and 7H), whereas full-length ZNF207 binds similarly in both contexts (Figures S13E and S13F). Thus, the ZnF domain provides critical contacts with U1 snRNA, stabilizing U1 snRNP association. Consistently, pull-down assays reveal strong, direct binding of WT ZNF207 to U1 snRNP, which is significantly weakened by the K42E mutation (p < 0.0001, one-way ANOVA; Figures 7I and 7J).

Together, these results establish that ZNF207 interacts with U1 snRNP through direct binding to U1–70K, reinforced by ZnF-dependent association with U1 snRNA. The C-terminal disordered region contributes to RNA affinity but is dispensable for splicing regulation.

In summary, we have developed CRASP-seq, a robust method for systematically identifying splicing regulators. Applying CRASP-seq to the LMNA 1824C>T mutation identified ZNF207 as a key regulator. In HGPS patient cells, ZNF207 depletion restores normal LMNA splicing and reduces progerin levels (Figure 3E). Mechanistically, ZNF207 binds regulated exons and engages U1 snRNP components to broadly control alternative splicing (Figure 7K), highlighting CRASP-seq’s utility in uncovering critical splicing mechanisms.

DISCUSSION

In this study, we present CRASP-seq, a CRISPR-based approach for systematically identifying splicing regulatory factors and protein domains that control phenotypically relevant alternative splicing events. By linking CRISPR-induced perturbations with high-throughput RNA sequencing readouts, CRASP-seq precisely quantifies the impact of each protein-coding gene on the splicing outcome of a reporter in a single RNA extraction. The platform is versatile, enabling gene knockouts and high-throughput mutagenesis, and captures diverse splicing events, including cassette exons, mutually exclusive exons, and alternative 5′ splice sites. Application to cancer-related and progeria-associated events highlights its potential to identify regulators with significant biological and clinical implications.

Several features distinguish CRASP-seq from existing methods. Compared with RNAi screens, CRISPR provides higher sensitivity with fewer off-target effects.⁹⁰ Unlike most CRISPR screens, which typically infer phenotypes indirectly, CRASP-seq directly measures RNA outcomes, analogous to recent yeast studies now being extended to mammalian systems.^91–95 Therefore, CRASP-seq does not require specialized equipment for cell sorting or visualization,^96–98 nor does it rely on extensive robotics or the thousands of RNA extractions often needed for arrayed screens.^15,62,99 Instead, it relies solely on a high-throughput sequencer, streamlining workflows while enhancing accessibility and reducing costs. This approach provides highly quantitative information on splicing, including precise ΔPSI values, from pooled cell populations. In addition, CRASP-seq enables high-resolution mutational scanning, allowing the identification of critical protein regions and even individual amino acid residues essential for splicing regulation. The method is straightforward to implement, with the entire workflow—from reporter cloning into a CRISPR perturbation vector to obtaining screening results—achievable within 2–3 weeks, making it both practical and broadly applicable for dissecting splicing regulatory mechanisms.

Using CRASP-seq, we identified LMNA aberrant splicing in HGPS. Among hits, we identified several splicing-related genes, including SRSF6, a known negative regulator of progerin isoform expression.¹⁰⁰ In contrast, ZNF207 and several core spliceosome proteins emerged as positive regulators of the progerin isoform. ZNF207, previously linked to kinetochore formation^67,68 and transcriptional regulation,⁶⁹ is here established as a bona fide splicing regulator, consistent with recent studies.^70,73,101 Transcriptomic analyses revealed more than 800 ZNF207-regulated splicing events that were reversed upon ORF reintroduction. Differential expression following ZNF207 depletion is often associated with NMD-triggering splicing events, suggesting splicing contributes to gene expression changes. We propose that “splicing regulator” should complement or even supersede “kinetochore protein” and “transcription factor” as a functional descriptor of ZNF207, with future studies needed to explore how these roles interconnect.

Mechanistically, ZNF207 modulates splicing through interactions with U1 snRNP components, consistent with functional associations with U1–70K and RBM25.¹⁷ Interestingly, our study uncovers a feedback compensatory mechanism in which ZNF207 protein levels regulate the expression of auxiliary U1 splicing regulators, such as LUC7L and LUC7L2, by modulating poison exon inclusion in these genes.^102–104 This finding further underscores the intricate link between ZNF207 and the U1 snRNP complex. ZNF207’s intrinsic disorder and propensity for phase separation⁸⁸ may promote exon inclusion by stabilizing U2-dependent cross-exon interactions after initial recruitment via U1 snRNP. Structural studies of spliceosomal assemblies containing ZNF207 will provide deeper insights.

Notably, BUB3, a stable kinetochore interactor,^67,68 has minimal effects on ZNF207-regulated splicing, and the GLEBS domain is dispensable for splicing (Figures S8A–S8C and S10D–S10G). These findings indicate that ZNF207’s kinetochore and splicing functions are separable, suggesting the potential for therapeutics targeting its splicing activity without affecting cell division. Combined with antisense oligonucleotides⁷⁹ or base-editing approaches,¹⁰⁵ this could enhance treatment strategies for HGPS.

ZNF207 joins an expanding list of C2H2 ZnF proteins with RNA splicing activity.^62,66,106 Our high-throughput mutagenesis screen revealed that ZNF207’s ZnF domains are essential for splicing regulation and identified Lys42 as critical for its function. Biochemical analyses indicate that ZnF-mediated electrostatic interactions with U1 snRNA stabilize ZNF207 association with U1 snRNP in cooperation with U1–70K. These results align with independent studies¹⁰¹ and underscore CRASP-seq’s ability to pinpoint critical residues in splicing regulators. Applying CRASP-seq to other events will likely uncover additional overlooked regulators.

Limitations of the study

The ZNF207 eCLIP experiments used the full-length protein, which binds RNA via both the ZnF and C-terminal IDR domains. The IDR dominates binding but contributes minimally to splicing, likely capturing post-splicing interactions. Future studies using the minimal splicing-active domain (residues 1–122) are needed to define the RNA contacts that contribute to splicing regulation.

Like most screens, CRASP-seq identifies both direct and indirect regulators. For example, we detected a cluster of cell cycle-associated genes affecting splicing (Figure 2F). Some, such as cell cycle kinases, may act directly via post-translational modification of splicing factors,^107,108 while others likely act indirectly by altering transcriptional programs that impact splicing factor expression. Determining direct effects requires mechanistic follow-up, with RNA-binding capacity^71–73 and/or interactions with core splicing machinery providing strong evidence.

One limitation of CRASP-seq is that it relies on monitoring the splicing of minigene reporters rather than of endogenous genes. While reporters include native flanking introns, long-range RNA structures or transcription-dependent effects may not be captured. Despite this, the method robustly identifies regulators of functionally significant splicing events.

Finally, our current framework is largely restricted to a “binary” focus on exon inclusion versus skipping, limiting the detection of more complex events including intron retention. This constraint stems from the use of short-read Illumina sequencing, which is poorly suited for capturing long intron-retention isoforms that are inefficiently amplified, difficult to cluster, and require longer read lengths than those used here. Future integration of CRASP-seq with long-read sequencing will help address this limitation. In addition, anticipated advances in single-cell RNA sequencing technologies—particularly approaches with greater sensitivity and long-read capabilities—may eventually obviate the need for reporter systems altogether.

RESOURCE AVAILABILITY

Lead contact

For additional details and inquiries regarding resources and reagents, please reach out to the lead contact, Thomas Gonatopoulos-Pournatzis (thomas.gonatopoulos@nih.gov).

Materials availability

All generated key plasmids and libraries have been deposited to Addgene (see Table S2). Any other reagent is available upon reasonable request.

Plasmids: https://www.addgene.org/browse/article/28252480/.

Libraries: https://www.addgene.org/pooled-library/gonatopoulos-pournatzis-human-crispr-crasp-seq/.

Data and code availability

CRISPR screening, RNA-seq, and eCLIP data generated by this study have been deposited at GEO and are publicly available. Accession numbers are listed in the key resources table. The ZNF207 miniTurboID mass spectrometry data associated with this study have been deposited to the ProteomeXchange consortium through its partner MassIVE (massive.ucsd.edu). Accession numbers and DOI are listed in the key resources table. Raw data underlying the figures have been deposited in Mendeley Data under the DOI https://doi.org/10.17632/xvzfdm8kky.1.
This paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
ZNF207	Invitrogen	Cat#703747; RRID: AB_2815340
ZNF207	ThermoFisher Scientific	Cat#PA530641; RRID: AB_2548115
FLAG M2	Sigma	Cat#F3165; RRID: AB_259529
GAPDH	Proteintech	Cat#10494–1-AP; RRID: AB_2263076
SF3B1	Proteintech	Cat#27684–1-AP; RRID: AB_2880946
PRPF6	Proteintech	Cat#23929–1-AP; RRID: AB_2879365
SNRNP200	Proteintech	Cat#23875–1-AP; RRID: AB_2879346
RBM25	Proteintech	Cat#25297–1-AP; RRID: AB_2880014
SNRNP70	Abcam	Cat#ab83306; RRID: AB_10673827
SNRPA	Proteintech	Cat#10212–1-AP; RRID: AB_2239723
SNRPC	Proteintech	Cat# 22428–1-AP; RRID: AB_2918077
SNRPF	Proteintech	Cat#14977–1-AP; RRID: AB_2302166
BUB3	ThermoFisher Scientific	Cat#BDB611730; RRID: AB_2243620
HA tag	Sigma-Aldrich	Cat#H3663; RRID: AB_262051
Beta-actin	Proteintech	Cat#20536–1-AP; RRID: AB_10700003
Lamin A/C	Santa Cruz Biotechnology	Cat#sc376248; RRID: AB_10991536
LAP2 alpha	Abcam	Cat#ab5162; RRID: AB_304757
Lamin B1	Abcam	Cat#ab16048; RRID: AB_443298
Histone H3 (tri methyl K27)	Abcam	Cat#ab192985; RRID: AB_2650559
Acetyl-Histone H4 (acetyl K16)	Abcam	Cat#ab109463; RRID: AB_10858987
Anti-rabbit IgG, HRP-linked Antibody	Cell Signaling Technology	Cat#7074; RRID: AB_2099233
Anti-mouse IgG, HRP-linked Antibody	Cell Signaling Technology	Cat#7076; RRID: AB_330924
Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor^™ 594	Invitrogen	Cat#A11012; RRID: AB_2534079
Bacterial and virus strains
NEB Stable competent E. coli cells	New England Biolabs	Cat#C3040H
Endura electrocompetent cells	LGC Biosearch Technologies	Cat#60242–2
One Shot^™ BL21(DE3) Chemically Competent E. coli	ThermoFisher Scientific	Cat#C600003
Chemicals, peptides, and recombinant proteins
TRIzol	Sigma-Aldrich	Cat#T3934
Puromycin	ThermoFisher Scientific	Cat#A1113803
G418 Sulfate	Gibco	Cat#10131027
Blasticidin S	Gibco	Cat#A1113903
Hygromycin	ThermoFisher Scientific	Cat#10687010
Doxycycline	Sigma-Aldrich	Cat#D9891
Polybrene	Sigma-Aldrich	Cat#H9268
DTT (dithiothreitol)	ThermoFisher Scientific	Cat#R0861
D-Biotin	ThermoFisher Scientific	Cat#B20656
IGEPAL^® CA-630	Sigma-Aldrich	Cat#I3021
DAPI	Biotium	Cat#40043
ATP	ThermoFisher Scientific	Cat#R0441
Creatine phosphate	Sigma-Aldrich	Cat#10621714001
Polyvinyl alcohol (PVA)	ThermoFisher Scientific	Cat#041241.22
ZNF207 full-length	This study	N/A
ZNF207 K42E	This study	N/A
ZNF207 1–122 (ZnF+Hel)	This study	N/A
ZNF207 1–122 K42E (ZnF+Hel_K42E)	This study	N/A
U1–70K (SNRNP70) 1–216	This study	N/A
U1-A (SNRPA)	This study	N/A
U1-C (SNRPC)	This study	N/A
Critical commercial assays
RNeasy Plus Mini Kit	Qiagen	Cat#74136
DNA Gel Loading Dye (6X)	ThermoFisher Scientific	Cat#R0611
NEBNext Ultra II Q5 Master Mix	New England Biolabs	Cat#M0544X
Q5^® Site-Directed Mutagenesis Kit (Without Competent Cells)	New England Biolabs	Cat#E0552S
QIAquick PCR Purification Kit	Qiagen	Cat#28104
GeneJET PCR purification column	ThermoFisher Scientific	Cat#K0701
GeneJET Gel Extraction Kit	ThermoFisher Scientific	Cat#K0691
SYBR Safe	ThermoFisher Scientific	Cat#S33102
Chemiluminescent Nucleic Acid Detection Module Kit	ThermoFisher Scientific	Cat#89880
Streptavidin-Horseradish Peroxidase (HRP) Conjugate	ThermoFisher Scientific	Cat#SA10001
EasyPep^™ MS Sample Prep Kits	ThermoFisher Scientific	Cat#A57864
KAPA HiFi HotStart DNA polymerase	Roche	Cat# KK2601
D1000 ScreenTape	Agilent	Cat#5067–5582; Cat#5067–5583
High Sensitivity D1000 ScreenTape	Agilent	Cat#5067–5584
High Sensitivity D1000 Reagents	Agilent	Cat#5067–5585
RNA ScreenTape	Agilent	Cat#5067–5576
RNA ScreenTape Sample Buffer	Agilent	Cat#5067–5577
Qubit dsDNA HS assay	ThermoFisher Scientific	Cat#Q32851
PhiX	Illumina	Cat#FC-110–3001
NovaSeq^™ X Series 1.5B Reagent Kit (300 Cyc)	Illumina	Cat# 20104704
NovaSeq^™ X Series 1.5B Reagent Kit (300 Cyc)	Illumina	Cat# 20104705
Illumina Stranded mRNA Prep kit	Illumina	Cat#15031047
In-Fusion Snap Assembly Master Mix	Takara Bio	Cat#638948
NEBuilder HiFi DNA Assembly Master Mix	New England Biolabs	Cat#E2621L
X-tremeGENE 9 DNA Transfection Reagent	Sigma-Aldrich	Cat#6365809001
QIAGEN OneStep RT-PCR Kit	Qiagen	Cat#210215
SensiFAST^™ SYBR^® No-ROX Kit	Bioline	Cat#BIO-98050
Maxima H Minus First Strand cDNA Synthesis Kit	ThermoFisher Scientific	Cat#K1651
SensiFAST^™ SYBR^® No-ROX One-Step Kit	Bioline	Cat#BIO-72005
Maxi-prep plasmid purification kit	Invitrogen	Cat#K210016
EndoFree Plasmid Maxi Kit	Qiagen	Cat#12362
BP clonase II	ThermoFisher Scientific	Cat#11789020
LR clonase II	ThermoFisher Scientific	Cat#11791020
BveI	ThermoFisher Scientific	Cat#FD1744
Eco47III	ThermoFisher Scientific	Cat#FD0324
Eco32I	ThermoFisher Scientific	Cat#FD0304
KpnI	New England Biolabs	Cat#R3142L
NdeI	New England Biolabs	Cat#R0111L
BamHI	New England Biolabs	Cat#R0136L
NcoI	New England Biolabs	Cat#R0193L
FastAP	ThermoFisher Scientific	Cat#EF0651
RNase Inhibitor, Murine	New England Biolabs	Cat#M0314L
T4 RNA Ligase 1 (ssRNA Ligase)	New England Biolabs	Cat#M0204L
T4 DNA ligase	New England Biolabs	Cat#M0202
Amylose Resin	New England Biolabs	Cat#E0821S
HiScribe^® T7 High Yield RNA Synthesis Kit	New England Biolabs	Cat#E2040S
Lipofectamine RNAiMax	ThermoFisher Scientific	Cat#13778150
Lipofectamine^™ 2000 Transfection Reagent	ThermoFisher Scientific	Cat#11668027
Streptavidin Sepharose Beads	ThermoFisher Scientific	Cat#20353
Dynabeads protein G	ThermoFisher Scientific	Cat#10004D
Dynabeads^™ Oligo(dT)₂₅	ThermoFisher Scientific	Cat#61002
Proteinase-K	ThermoFisher Scientific	Cat#EO0492
mMESSAGE mMACHINE^™ T7 mRNA Kit with CleanCap^™ Reagent AG	Invitrogen	Cat#A57620
RNaseA	Invitrogen	Cat#12091021
HELA NUCLEAR EXTRACT for pre-mRNA splicing	Protein One	Cat#P0002–02
Bicinchoninic acid (BCA) assay	Pierce	Cat#23225
Bradford reagent	BioRad	Cat#5000006
NuPAGE LDS Sample Buffer (4x)	ThermoFisher Scientific	Cat# NP0007
cOmplete^™, Mini, EDTA-free Protease Inhibitor Cocktail	Roche	Cat#11836170001
cOmplete^™ Protease Inhibitor Cocktail	Roche	Cat#11836145001
Protease Inhibitor Cocktail III	Sigma-Aldrich	Cat#539134–1ML
4–12% Bis-Tris gels	Life Technologies	Cat#NP0323BOX
Immobilon-P PVDF membrane	Sigma-Aldrich	Cat#IPVH00010
Benzonase^® Nuclease	Sigma-Aldrich	Cat#E1014
SuperSignal West Pico PLUS chemiluminescence reagent	ThermoFisher Scientific	Cat#34580
SuperSignal^™ Western Blot Substrate Bundle, Femto + trial-size Atto	ThermoFisher Scientific	Cat#A45916
DMEM with high glucose and pyruvate	Gibco	Cat#11995073
heat-inactivated fetal bovine serum (HI-FBS)	Gibco	Cat#16140071
penicillin-streptomycin	Gibco	Cat#15140122
trypsin-EDTA	Gibco	Cat#25200056
Opti-MEM	Gibco	Cat#31985062
MEM, no glutamine	Gibco	Cat#11090081
L-Glutamine (200 mM)	Gibco	Cat#25030081
MEM Non-Essential Amino Acids Solution (100X)	Gibco	Cat#11140050
Sequencing Grade Modified Trypsin	Promega	Cat#V5111
PhenoPlate^™ 384-well microplates	Revvity Health Sciences	Cat#6057300
Deposited data
CRASP-Seq data	This study	GEO: GSE284628
ZNF207 RNA-Seq	This study	GEO: GSE284629, GSE284630
ZNF207 eCLIP-Seq data	This study	GEO: GSE284631
ZNF207 miniTurboID data	This study	proteomXchange: MSV000096693
Unprocessed Imaging data	This study	Mendeley Data: 10.17632/xvzfdm8kky.1
Experimental models: Cell lines
HAP1 Cas9/Cas12a	This study	N/A
HAP1 highly efficient adenine base editor (ABE8e)	Xiao et al.¹¹	N/A
HAP1 cytosine base editor (evoCDA1)	Xiao et al.¹¹	N/A
RPE1 hTERT TP53 −/− Cas9/Cas12a	This study	N/A
HEK293T	ATCC	Cat#ACS-4500
HEK293 Flp-In T-REx cells	Invitrogen	Cat#R78007
HepG2 Cas9/Cas12a	This study	N/A
hTERT-immortalized fibroblasts	Fernandez et al.⁷⁷	N/A
hTERT-immortalized fibroblasts HGPS	Fernandez et al.⁷⁷	N/A
Oligonucleotides
See Table S2	This study	N/A
Recombinant DNA
See Table S2 and STAR Methods	This study	N/A
Plasmids	This study	https://www.addgene.org/depositing/85289/
Libraries	This study	https://www.addgene.org/pooled-library/gonatopoulos-pournatzis-human-crispr-crasp-seq/
Software and algorithms
AlphaFold2 2.3.1	Jumper et al.¹⁰⁹ and Varadi et al.¹¹⁰	https://alphafold.ebi.ac.uk/download
BEDTools 2.30.0	Quinlan and Hall¹¹¹	https://bedtools.readthedocs.io/en/latest/
BLAT	UCSC Genome Browser	https://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads
clusterProfiler	Yu et al.¹¹²	https://www.bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html
cutadapt 4.4	Martin¹¹³	https://cutadapt.readthedocs.io/en/stable/
deepTools 3.5.2	Ramírez et al.¹¹⁴	https://deeptools.readthedocs.io/en/develop/
DESeq2 1.44	Love et al.¹¹⁵	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
DEWSeq 1.18.0	Schwarzl et al.⁸⁴	https://bioconductor.org/packages/release/bioc/html/DEWSeq.html
g:Profiler	Kolberg et al.¹¹⁶	https://biit.cs.ut.ee/gprofiler/gost
htseq-clip 2.19	Sahadevan et al.⁸³	https://htseq-clip.readthedocs.io/en/latest/index.html
IHW 1.32.0	Ignatiadis et al.¹¹⁷	https://bioconductor.org/packages/3.21/bioc/vignettes/IHW/inst/doc/introduction_to_ihw.html
JupyterLab Desktop 4.2.5–1	Project Jupyter	https://jupyter.org/
MultiQC 1.13	Ewels et al.¹¹⁸	https://seqera.io/multiqc/
Python 2.7.15	Python Software Foundation	https://www.python.org
Python 3.8.5	Python Software Foundation	https://www.python.org
R version 4.3.1	R Foundation	https://www.r-project.org
RStudio Desktop 2025.05.0	Posit Software, PBC	https://posit.co/
Samtools 1.19.1	Li et al.¹¹⁹	http://www.htslib.org/download/
STAR 2.7.6a	Dobin et al.¹²⁰	https://github.com/alexdobin/STAR/releases
UCSC table browser	UCSC Genome Browser	https://genome.ucsc.edu
UMI-tools 1.1.5	Smith et al.¹²¹	https://umi-tools.readthedocs.io/en/latest/index.html
Whippet 1.6.2	Sterne-Weiler et al.¹²²	https://github.com/timbitz/Whippet.jl
FlowJo software	BD Biosciences, version 10.8.1	https://www.flowjo.com/solutions/flowjo
Cytoscape v3.9.1	Shannon et al.¹²³	https://cytoscape.org/
STRING plugin v2.0.1	Doncheva et al.¹²⁴	https://apps.cytoscape.org/apps/stringapp
Partek Flow software (version 10.0.23.0531)	N/A	https://www.partek.com/partek-flow/
GraphPad Prism Version 9.0	GraphPad Software Inc.	https://www.graphpad.com/
Adobe Illustrator v28.4.1	Adobe	https://www.adobe.com/products/illustrator.html
Affinity Designer 2 v2.3.0	Affinity Designer	https://affinity.serif.com/en-us/designer/
BioRender	BioRender	https://www.biorender.com/
Other
UV Crosslinker	VWR	Cat#89131–484
4150 TapeStation System	Agilent	Cat#G2992AA
BTX Gemini Electroporator	BTX	Cat#452042
Bioruptor Plus sonicator	Diagenode	Cat#B01020002
Mini Gel Tank	Life Technologies	Cat#A25977,
Mini Blot Module	Life Technologies	Cat#B1000
VeritPro Thermal Cycler	Applied Biosystems	Cat#A48141
CFX96 Touch Real-Time PCR	BioRad	Cat#1855195
NovaSeq X Sequencing System	Illumina	N/A
EVOS^™ M5000 Imaging System	Invitrogen	Cat#AMF5000
iBright CL1500 Imaging System	Invitrogen	Cat#A44114
Vi-Cell BLU Cell Viability Analyzer	Beckman Coulter	Cat#C19196

Open in a new tab

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Cell culture

HEK293T, HEK293 Flp-In, HAP1, and RPE1 cells were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM; Gibco #11995073) supplemented with 10% heat-inactivated fetal bovine serum (HI-FBS; Gibco #16140071) and 1% penicillin-streptomycin (Gibco #15140122). HepG2 cells were cultured in Eagle’s Minimum Essential Medium (EMEM; ATCC #30–2003) with 10% fetal bovine serum (FBS; Gibco #A5670701). hTERT-immortalized fibroblasts, including wild-type (WT) and Hutchinson-Gilford Progeria Syndrome (HGPS) patient-derived cells, were cultured in Minimum Essential Medium (MEM; ThermoFisher Scientific #11090081) supplemented with 10% HI-FBS, 1% penicillin-streptomycin, and 2 mM L-glutamine (ThermoFisher Scientific #25030081). UOK124 cells were cultured in DMEM supplemented with 1% non-essential amino acids (MEM; Gibco #11140050), 10% HI-FBS; Gibco and 1% penicillin-streptomycin. All cell lines were grown at 37°C in a humidified 5% CO₂ atmosphere. Cells were routinely passaged using 0.25% trypsin-EDTA (Gibco #25200056) and seeded at appropriate densities for each experimental condition in tissue culture-treated plates. The culture medium was refreshed every 2–3 days, and cell confluency was monitored with an EVOS^™ M5000 Imaging System (Invitrogen). Cell viability was assessed using a Vi-Cell BLU Cell Viability Analyzer (Beckman Coulter).

Cell line generation

The CHyMErA and base editor stable cell lines were generated and maintained as described previously.¹¹ To generate HEK293T cells stably expressing the LMNA minigene reporter, 350,000 HEK293T cells were seeded and transduced with lentiviral supernatant in growth medium supplemented with polybrene (1:1000 dilution) to enhance infection efficiency. Approximately 22 hours post-transduction, puromycin was added to the medium to select for stable integrands.

For generating Flp-In cell lines expressing ZNF207, one million HEK293 Flp-In cells were transfected with pcDNA5 ZNF207 plasmids or controls using the Lipofectamine 2000 transfection reagent (ThermoFisher Scientific #11668027) following manufacturer’s recommendation. After 4.5 hours, the transfection media was replaced with fresh growth media to reduce Lipofectamine-associated toxicity. Two days post-transfection, cells were treated with 200 μg/mL Hygromycin B (ThermoFisher Scientific #10687010) in DMEM. The hygromycin-containing media was refreshed every 4–5 days to select for stably transfected cells. Once cells reached confluency, various concentrations of doxycycline were tested to optimize expression of FLAG-tagged proteins. Final concentrations of 0.001 μg/mL doxycycline for FLAG-Empty or FLAG-EGFP and 0.5 μg/mL doxycycline for FLAG-ZNF207 constructs were selected for consistent protein expression.

METHOD DETAILS

Cloning CRASP-Seq lentiviral vectors

The CRASP-Seq lentiviral vector was constructed using the pLCHKOv3 backbone (Addgene #209025) as a starting point. First, the two existing BpiI restriction sites, intended for the subsequent two-step library cloning, were removed via site-directed mutagenesis. Second, the hgRNA expression cassette was inverted from the minus to the plus strand orientation. Third, a minigene cloning sequence, including a FLAG-tag and followed by unique Eco47III and Eco32I restriction sites (inserted for the subsequent cloning of cell barcodes and minigene reporters), was inserted downstream of the hgRNA expression cassette on the minus strand. Fourth, a doxycycline-inducible tetracycline response element (TRE) was cloned on the minus strand between the hgRNA expression cassette and the minigene cloning sequence. Fifth, 2A peptide sequence fused to rtTA ORF was inserted immediately upstream of the stop codon of the puromycin resistance cassette. Finally, a BGH poly(A) signal was incorporated into the minus strand upstream of the hgRNA expression cassette (Figure 1A). This CRASP-Seq backbone vector, named pLCHKO-CRASP, has been deposited to Addgene (#231928).

Using pLCHKO-CRASP, we applied NEBuilder HiFi DNA Assembly Master Mix (NEB #E2621L) to clone a cell barcode library containing 12 random nucleotides. A 62-nucleotide (nt) oligonucleotide, ordered from IDT, included 25-nt flanking sequences around the 12 random nucleotides at the Eco47III (ThermoFisher Scientific # FD0324) restriction site. The Eco47III-digested vector was used in 16x NEBuilder reactions following the manufacturer’s protocol. The reaction mixture was precipitated with sodium acetate and ethanol, as previously described,³⁴ and the purified product was transformed into Endura competent cells (LGC Biosearch Technologies #60242–2) by electroporation (1 mm cuvette, 25 μF, 200 Ω, 1,600 V). Sufficient cells were plated on 15-cm LB agar plates containing 100 μg/mL carbenicillin to achieve a library coverage of 2,000-fold. After overnight incubation at 30°C, colonies were scraped, pooled, and the bacterial pellets were collected. Plasmids were extracted using the EndoFree Plasmid Maxi Kit (QIAGEN #12362), and the resulting library plasmids were deposited to Addgene (Addgene #232068 – CRASP-Seq CBC backbone).

The genome-wide CHyMErA KO library was constructed as described previously.¹¹ Briefly, a single oligo pool of 187 nt, including a 20-nt Cas9 guide sequence, followed by the Streptococcus pyogenes Cas9 tracrRNA, theAcidaminococcus sp. Cas12a direct repeat (DR), and a 23-nt Cas12a guide sequence, was synthesized by TWIST Biosciences (Table S1). These guide sequences were flanked by BveI/BspMI restriction sites. The oligo pool, comprising 95,893 guide sequences, was amplified by PCR using KAPA HiFi HotStart DNA polymerase (Roche #KK2601). Four 50 μL PCR reactions were set up with 10 nM oligo pool and 0.35 μM of each primer (Table S2) under the following conditions: initial denaturation at 98°C for 3 minutes, followed by 10 cycles of 98°C for 10 seconds, 62°C for 15 seconds, and 72°C for 20 seconds. Amplified oligos were purified using a PCR purification kit (ThermoFisher Scientific #K0701), and quality was confirmed via 2% agarose gel electrophoresis. Oligos were then digested with BveI (ThermoFisher Scientific #FD1744) and ligated into 2 μg of digested CRASP-Seq CBC backbone using T4 DNA ligase (NEB #M0202) in a Golden Gate reaction (step 1: 37°C for 30 minutes; step 2: 37°C for 30 minutes, 22°C for 30 minutes, repeated for 16 cycles; step 3: 37°C for 15 minutes; step 4: 65°C for 20 minutes) at a 1:9 vector-to-insert molar ratio. The ligation mix was precipitated and purified, and Endura competent cells were transformed by electroporation as described above to achieve a library coverage of >1,000-fold. After incubation at 30°C, colonies were pooled, and the plasmid library was extracted using the EndoFree Plasmid Maxi Kit (QIAGEN #12362). The plasmid library pool is available from Addgene (#232069 – CRASP-Seq Gene KO v1 empty).

As a final step, we cloned various minigene reporters into the CRASP-Seq Gene KO v1 empty library. DNA fragments containing the desired sequences, including complete native intronic sites and neighboring exons, were either synthesized (TWIST Biosciences) or amplified from genomic DNA. NEBuilder was used to insert these fragments into the Eco47III-digested CRASP-Seq Gene KO v1 empty vector with reactions yielding at least 250-fold coverage as described above.

Cloning base editor library

2.5 μg of CRASP-Seq CBC backbone plasmid was digested using the BveI restriction enzyme at 37°C for 2 hours in a total of eight reactions (total plasmid = 20 μg). The 144-nucleotide gRNA library oligo pool (TWIST Biosciences; Table S8) was amplified in a thermocycler using the following protocol: initial denaturation at 95°C for 3 minutes, followed by six cycles of 98°C for 15 seconds, 65°C for 15 seconds, and 65°C for 45 seconds using KAPA HiFi HotStart DNA polymerase. The TKO_F1 and TKO_R2 primers (Table S2) were used for this amplification. Both the digested plasmids and the amplified gRNA library oligos underwent PCR purification using the QIAquick PCR Purification Kit (QIAGEN #28104). Purified plasmids were then further purified via gel purification using GeneJET Gel Extraction Kit (ThermoFisher Scientific # K0691).

Golden gate cloning was performed with ~66 fmol of purified plasmids and ~330 fmol of large-scale oligos, followed by ethanol precipitation and reconstitution in 10 μL of TE buffer. For transformation, 90 μL of Endura electrocompetent cells were used, following the manufacturer’s protocol, and plated on Carbenicillin (100 μg/mL) agar plates. The calculated library coverage was approximately 3,500x. Colonies were collected and pelleted, and the CRASP-Seq BE Tiling v1 empty library (Addgene # 232070) was purified using the EndoFree Plasmid Maxi Kit (QIAGEN #12362).

We next cloned the oligo library into the LMNA minigene reporter. 20 μg of the CRASP-Seq BE Tiling v1 empty library were digested with Eco32I (ThermoFisher Scientific #FD0304) at 37°C for 2 hours, followed by PCR and gel purification (ThermoFisher Scientific # K0691). The LMNA mutant splicing reporter (Table S2; ~46 fmol per reaction) was inserted into the digested plasmid (~16 fmol per reaction) using the NEBuilder HiFi DNA assembly reaction (8 reactions; NEB, Cat# E2621L), followed by ethanol precipitation and reconstitution in TE buffer. Subsequently, 120 μL of Endura electrocompetent cells were transformed with the assembled constructs and plated on Carbenicillin agar plates. After colony collection and pelleting, the LMNA splicing reporter plasmid library was obtained by the EndoFree Plasmid Maxi Kit (QIAGEN #12362).

Lentivirus production

Lentivirus production was carried out as described previously.¹¹ For gRNA library virus production, 8 million HEK293T cells were seeded per 15-cm plate in DMEM with 10% HI-FBS. Twenty-four hours post-seeding, cells were transfected with a mix containing 8 μg of the lentiviral CRASP-Seq library vector, 6.5 μg of packaging plasmid psPAX2 (Addgene #12260), 4 μg of envelope plasmid pMD2.G (Addgene #12259), 48 μL of X-tremeGENE 9 DNA Transfection Reagent (Sigma-Aldrich #6365809001), and 1.4 mL of Opti-MEM (Gibco #31985062). After 24 hours, the medium was replaced with serum-free, high-BSA growth medium (DMEM with 1.1 g/100 mL BSA and 1% penicillin-streptomycin). Virus-containing medium was collected 48 hours post-transfection, centrifuged at 475 rcf for 5 minutes at 4°C, aliquoted, and stored at −80°C.

For viral titer determination, target cells were transduced with serial dilutions of the lentiviral hgRNA library in the presence of polybrene (8 μg/mL; Sigma-Aldrich #H9268). After 24 hours, the virus-containing medium was replaced with fresh medium containing puromycin (1–2 μg/mL; ThermoFisher Scientific #A1113803). Cells were incubated for an additional 48 hours. The multiplicity of infection (MOI) was determined 72 hours post-infection by comparing the survival rate of puromycin-selected cells to infected but non-selected control cells.

For producing lentivirus with the LMNA mutant splicing reporter, 750,000 HEK293T cells were seeded in a 6-well plate and subsequently transfected with 2.5 μg of the lentiviral vector containing the LMNA mutant splicing reporter, 600 ng of psPAX2, and 400 ng of pMD2.G using the Lipofectamine 2000 transfection reagent (ThermoFisher Scientific #11668027), following the manufacturer’s protocol.

CRASP-Seq screening protocol

The generated lentiviral library was used to infect HAP1, RPE1, or HepG2 cells expressing SpCas9 and opCas12a nucleases^11,39 at a multiplicity of infection (MOI) of 0.2, ensuring a coverage of at least 250 cells per hgRNA. After 24 hours, selection was initiated with 2 μg/mL puromycin. Forty-eight hours post-selection (T0), the surviving cells were pooled, plated, and treated with doxycycline to induce reporter expression. After 24 hours (T1), cells were harvested, aliquoted into 10 million-cell samples, and prepared for further analysis. For the SRSF7 poison exon screen, 100 μg/mL cycloheximide was added 4 hours before harvesting to inhibit NMD.

Total RNA was extracted from ~20 millions cells using the RNeasy Plus Mini Kit (QIAGEN #74136) according to the manufacturer’s instructions. mRNA was then isolated using oligo(dT) Dynabeads (ThermoFisher Scientific #61002), followed by cDNA synthesis using the Maxima H Minus First Strand cDNA Synthesis Kit (ThermoFisher Scientific #K1651) with a custom primer (Table S2). The resulting cDNA was purified using the QIAquick PCR Purification Kit (QIAGEN #28104). PCR was optimized and performed using the NEBNext Ultra II Q5 Master Mix (New England Biolabs #M0544X) under the following conditions: 98°C for 30 seconds (initial denaturation), followed by 14–17 cycles of 98°C for 10 seconds, 60°C for 20 seconds, and 72°C for 30 seconds, with a final extension at 72°C for 1 minute. Illumina barcoded forward and reporter-specific reverse primer pairs were utilized (Table S2). The PCR products were visualized on a 1–1.5% agarose gel stained with SYBR Safe (ThermoFisher Scientific #S33102). The desired bands were excised and purified using a gel extraction kit (ThermoFisher Scientific #K0691). The extracted libraries were quantified using the Qubit dsDNA HS Assay (ThermoFisher Scientific #Q32851) and Agilent TapeStation (Agilent #5067–5582 and #5067–5583). The validated sequencing libraries were pooled and sequenced on the NovaSeq 6000 platform using 300-cycle kits, with a 20–25% PhiX spike-in. The sequencing strategy most commonly employed was: Read 1: 210, Index Read 1: 8, Index Read 2: 8, Read 2: 104.

Cloning ZNF207

ZNF207 (ENST00000394670) was cloned into the pDONR223 entry vector using BP Clonase II Enzyme Mix (ThermoFisher Scientific #11789020) following PCR amplification and purification of the target sequences. The BP reaction product was transformed into NEB Stable Competent E. coli cells (New England Biolabs #C3040H) with transformants selected on LB agar plates containing spectinomycin. The resulting pDONR223-ZNF207 construct was then modified by site-directed mutagenesis using the Q5 Site-Directed Mutagenesis Kit (NEB #E0552S) to introduce silent mutations for resistance to the most effective ZNF207-targeting siRNA (#1: CAACTAGTGCAACCAGTAA).

Next, the modified ZNF207 sequences were transferred from the pDONR223 entry vector to the pcDNA5-miniTurboID and pcDNA5–3xFLAG destination vectors via Gateway LR Clonase II Enzyme Mix (ThermoFisher Scientific #11791020). For the pcDNA5–3xFLAG constructs, vectors enabling either N-terminal or C-terminal FLAG tags were used. The LR reaction mix was transformed into NEB Stable Competent E. coli cells, and transformants were selected on LB agar plates containing 100 μg/mL ampicillin. Only the functional pcDNA vector encoding C-terminal FLAG-tagged ZNF207 was deposited to Addgene (Addgene #231929 – pcDNA5-ZNF207–3xFLAG).

Site-directed mutagenesis of the siRNA-resistant pDONR-ZNF207 plasmid was then performed to generate truncated mutant constructs of ZNF207, including ΔZnF, ΔMB, ΔQPGM, ΔRNA1–2, ΔGLEBS, ΔCterm, and ΔRNA3 (Figure S12A). Domain boundaries for ZnF (zinc finger), MB, QPGM, and GLEBS were assigned based on UniProt annotations (uniprot.org), while the RNA1–2 and RNA3 domains were mapped using HydRA,⁸⁹ and the C-terminal domain was predicted based on structural modeling with Alpha-Fold.^109,110 All the destination vectors are submitted to Addgene (see Table S2 for Addgene IDs).

For lentiviral vector construction, an N-terminal FLAG pLX304 plasmid was digested with AfeI and SrfI restriction enzymes. The cut plasmid was then assembled with a DNA fragment synthesized by Twist Bioscience using the NEBuilder HiFi DNA Assembly Kit to generate a C-terminal FLAG-tagged pLX304 plasmid. Gateway LR Clonase II Enzyme Mix was used to generate the pLX304 C-terminal FLAG ZNF207 construct by recombining the siRNA-resistant ZNF207 sequence from the pDONR223 vector into the pLX304 C-terminal FLAG backbone (Addgene #231939 – pLenti-ZNF207–3xFLAG). All cloned constructs were confirmed by long read nanopore sequencing.

For recombinant protein preparation, both wild type and K42E mutations of ZNF207 (GenBank: NM_001098507.2) and the C-terminal truncation (ZNF207(1–122), corresponding to residues M1–A122) were inserted between the KpnI and BamHI restriction sites into the pnYC plasmid¹²⁵ with a TEV-cleavable N-terminal MBP tag, and a C-terminal StrepII-tag. SNRPA (GenBank: NM_004596.5) was inserted between the NdeI and BamHI restriction sites into the pnEK plasmid¹²⁵ with an HRV 3C-cleavable N-terminal His6-SUMO3-tag. SNRPC (GenBank: NM_003093.3) was inserted between the NcoI and BamHI restriction sites of the pnEK plasmid with a C-terminal His6 tag introduced by PCR. SNRNP70 1–216 (GenBank: NM_003089.6, corresponding to residues Met1 to Thr216) was inserted between the NdeI and BamHI restriction sites of the pnEK plasmid with an HRV 3C-cleavable N-terminal His6-SUMO3 tag.

RT-PCR assays

RNA was extracted from frozen cell pellets using RNeasy Plus Universal Mini Kit (QIAGEN #74136) according to manufacturer’s protocol. 40 ng of RNAs were used for RT-PCR reaction using QIAGEN OneStep RT-PCR Kit (QIAGEN #210215). The primers are provided in Table S2. For RT-PCR, 30 cycles were used to amplify LMNA, ACLY, and SRRM1, while 26 cycles were used for the LMNA splicing reporter. For LUC7L, LUC7L2, SNRNP70, and RBM3, reactions were performed with 100 ng of RNA. Amplification was carried out with 29 cycles for LUC7L, 31 cycles for LUC7L2, and 32 cycles for SNRNP70 and RBM3.

Immunoblotting

For experiments with HEK293T and HEK293 Flp-In cells, cell pellets were collected and lysed on ice for 10 minutes in F buffer (10 mM Tris pH 7.05, 50 mM NaCl, 30 mM Na4 pyrophosphate, 50 mM NaF, 5 μM ZnCl2, 10% glycerol, 0.5% Triton X-100), supplemented with a protease inhibitor cocktail (Roche #11836170001). Lysates were then centrifuged at 18,500 rcf for 5 minutes at 4°C, and the supernatants were collected. Samples were prepared by heating at 70°C for 5 minutes in NuPAGE LDS Sample Buffer (ThermoFisher Scientific #NP0007) with DTT (100 mM; ThermoFisher Scientific #R0861). Protein concentrations were determined using Bradford reagent (Bio-Rad #5000006), and equal amounts of protein (10–30 μg) were loaded and separated on 4–12% Bis-Tris gels (ThermoFisher Scientific #NP0323BOX). Proteins were then transferred to PVDF membranes, blocked with 5% milk for 1 hour at room temperature, and probed with primary antibodies including ZNF207 (1:2,000, Invitrogen #703747), FLAG M2 (1:2,000, Sigma #F3165), HA (1:1,000, Sigma #H3663), GAPDH (1:2500, Proteintech #10494–1-AP), SF3B1 (1:2,000, Proteintech #27684–1-AP), PRPF6 (1:1,000, Proteintech #23929–1-AP), SNRNP200 (1:1,000, Proteintech #23875–1-AP), RBM25 (1:2,000, Proteintech #25297–1-AP), SNRNP70 (1:1,000, Abcam #ab83306), SNRPA (1:2,000, Proteintech #10212–1-AP), SNRPC (1:1,000, Proteintech # 22428–1-AP), SNRPF (1:1,000, Proteintech #14977–1-AP), and BUB3 (1:1,000, ThermoFisher Scientific #BDB611730). All primary antibodies were prepared in 5% milk unless noted otherwise. After primary antibody incubation, membranes were washed and incubated with HRP-conjugated secondary antibodies (anti-Rabbit, Cell Signaling Technology #7074; anti-Mouse, Cell Signaling Technology #7076) at a 1:5,000 dilution for 1 hour at room temperature. Following additional washes, chemiluminescence detection was performed using SuperSignal West Pico PLUS (ThermoFisher Scientific #34580) or Femto (ThermoFisher Scientific #A45916), and images were captured using the Invitrogen iBright CL1500 Imaging System (ThermoFisher Scientific # A44114).

For HGPS patient-derived immortalized fibroblast cells, equal cell numbers (25,000–50,000) were pelleted and lysed in F buffer (1–2 μL of lysis buffer per 1,000 cells) with a protease inhibitor cocktail. After a 10-minute ice incubation, lysates were sonicated using a Bioruptor Plus (Diagenode # B01020002) at high power for 14 cycles (20 seconds on, 10 seconds off). Sonicated lysates were then mixed with NuPAGE LDS Sample Buffer (ThermoFisher Scientific #NP0007) with DTT (100 mM; ThermoFisher Scientific #R0861) and heated at 95°C for 5 minutes. The initial sample volume was used to determine the amount of beta-actin (1:5,000 in 5% milk; Proteintech #20536–1-AP), while loading volumes for progerin detection were adjusted based on beta-actin levels using Lamin A/C antibody (1:10,000 in 5% BSA; Santa Cruz Biotechnology #sc376248).

To assess biotinylation by miniTurbo Tag, proteins transferred to PVDF membranes were blocked for 1 hour in 5% BSA. The membranes were then incubated with Streptavidin-HRP (1:5,000 in 5% BSA; ThermoFisher Scientific #SA10001) for 1 hour at room temperature or with specific primary antibodies overnight at 4°C. Following washes, chemiluminescence detection was performed as described, with images acquired using the iBright CL1500 Imaging System.

Co-immunoprecipitation

Six million HEK293 Flp-In cells expressing FLAG-EGFP, FLAG-ZNF207 WT, or FLAG-ZNF207 truncated mutants were plated and treated with doxycycline (0.5 μg/mL) for 20 hours. After treatment, cells were washed with PBS, centrifuged, and the resulting cell pellets were lysed in IGEPAL buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% IGEPAL CA-630, 5% glycerol, with protease inhibitors) and incubated on a rotator at 4°C for 15 minutes. For RNA degradation, 125 units of benzonase were added to each lysate. For immunoprecipitation, protein lysates were incubated at 4°C for 4.5 hours with 10 μL Dynabeads^™ Protein G (Invitrogen #10003D) and 5 μg M2 FLAG antibody (Sigma #F3165). After incubation, the beads were washed four times with fresh IGEPAL buffer to remove non-specifically bound proteins. The bound protein complexes were then eluted by boiling in NuPAGE LDS Sample Buffer (ThermoFisher Scientific #NP0007) supplemented with DTT (100 mM; ThermoFisher Scientific, #R0861) at 70°C for 10 minutes at 1,200 rpm. The eluted proteins were analyzed by western blot using the indicated primary antibodies.

Quantification of U1 snRNA co-immunoprecipitated with ZNF207

HEK293 Flp-In cells (2 × 10⁶) expressing FLAG-EGFP or FLAG-ZNF207 truncated mutants (1–122; ZnF+Hel or ZnF+Hel_K42E) were plated and induced with doxycycline (0.5 μg/mL) for 20 hours. Cells were washed with PBS, pelleted, and lysed in IGEPAL buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% IGEPAL CA-630, 5% glycerol, plus protease inhibitors). Lysates were incubated on ice for 15 minutes and sonicated with a Bioruptor Plus (Diagenode #B01020002) at low power for 5 cycles (30 s on/30 s off). Turbo DNase (2 U, Invitrogen #AM2238) and Murine RNase inhibitor (4 U, NEB #M0314S) were added to the lysates. For immunoprecipitation, lysates were incubated for 4.5 h at 4°C with Dynabeads^™ Protein G (3.5 μL, Invitrogen #10003D) pre-bound to M2 FLAG antibody (1.5 μg, Sigma #F3165). Beads were washed four times with IGEPAL buffer to remove nonspecific complexes. Bound RNAs were purified with TRIzol and eluted in 20 μL of nuclease-free water. After 1:100 dilution, RT-qPCR was performed using a primer set targeting U1 snRNA. The primers are listed in Table S2.

ZNF207 rescue experiments

For transient transfection experiments, 700,000 HEK293T cells expressing the LMNA mutant minigene were plated for non-targeting control siRNA (siControl) treatment, and 1 million cells were plated for siRNA targeting ZNF207 (siZNF207 #1: CAACUAGUGCAACCAGUAA). Sixteen hours post-siRNA transfection (25 nM siRNA) using Lipofectamine RNAiMAX (ThermoFisher Scientific #13778150), cells were transfected with plasmids using X-tremeGENE9 (Sigma #6365779001). Cells were collected 48 hours after plasmid transfection for further analysis.

For experiments with stable ZNF207-expressing HEK293T Flp-In cells, 700,000 cells were plated for non-targeting control siRNA (siControl), and 1 million cells were plated for siZNF207 #1 (CAACUAGUGCAACCAGUAA). Sixteen hours after siRNA transfection, doxycycline was added as previously described to induce ZNF207 expression. Cells were collected and frozen 48 hours post-siRNA transfection for downstream applications.

In vivo RNA crosslink assays

In vivo RNA–protein crosslinking was carried out essentially as previously described.¹²⁶ HEK293T Flp-In cells were treated with 0.5 μg/mL doxycycline for 24 h to induce expression of ZNF207 variants or EGFP (negative control). After induction, cells were washed twice with PBS, resuspended in 1 mL PBS, and subjected to UV irradiation at 254 nm (400 mJ/cm²) to covalently crosslink RNA–protein complexes. Two millions of crosslinked cells were lysed in 100 μL eCLIP lysis buffer supplemented with 0.55 μL Protease Inhibitor Cocktail. Lysates were sonicated for a total of 5 minutes (4°C; 30 s on/off cycles, low setting) to ensure efficient disruption. For immunoprecipitation, cleared lysates were incubated for 2 hours at 4°C with Dynabeads^™ Protein G (3.5 μL, Invitrogen #10003D) pre-bound to M2 FLAG antibody (1.5 μg; Sigma #F3165). Following dephosphorylation and biotin labeling reaction (16°C, overnight),¹²⁶ beads were washed stringently, and bound complexes were eluted under denaturing conditions (70°C for 10 minutes at 1,200 rpm). Samples were resolved on NuPAGE 4–12% Bis-Tris gels (150 V, 75 min) and transferred to PVDF membranes at 30 V for 2 hours. Biotinylated crosslinked RNA was detected using the Chemiluminescent Nucleic Acid Detection Module kit (ThermoFisher Scientific #89880).¹²⁶ FLAG-tagged immunoprecipitated proteins were analyzed in parallel by immunoblotting as described above.

RT-qPCR validations

For most RT-qPCR validation, 40 ng of RNA was used with the SensiFAST SYBR No-ROX One-Step Kit (Bioline #BIO-72005) following the manufacturer’s protocol. For SMG1 NMD inhibitor treatment experiments, cDNAs were synthesized from 1μg of RNA using the Maxima H Minus First Strand cDNA Synthesis Kit (ThermoFisher Scientific #K1651) and subsequently diluted 1:25. cDNA equivalent to 4 ng of RNA was used in the SensiFAST SYBR No-ROX Kit (Bioline #BIO-72005). Two to three technical replicates were performed for each sample to ensure accuracy, and outlier values were excluded from analysis to obtain reliable results. Primer sequences are listed in Table S2.

miniTurboID mass spectrometry

Eight million HEK293 Flp-In cells were plated for each cell line (miniTurbo-Empty, miniTurbo-EGFP, or ZNF207-miniTurbo) in 15 cm cell culture dishes. After 48 hours, doxycycline was added to the media at a final concentration of 0.0005 μg/mL for miniTurbo-Empty and miniTurbo-EGFP (Addgene #209059), and 0.5 μg/mL for ZNF207-miniTurbo (Addgene #231938). Twenty hours after doxycycline treatment, D-Biotin (ThermoFisher Scientific #B20656) was added to the media at a final concentration of 50 μM. After an additional 4 hours of incubation, approximately 30 million cells were harvested and frozen for BioID analysis, while ~50,000 cells were collected for immunoblotting.

HEK293 Flp-In cells lines were lysed in 1 mL of 0.5% IGEPAL lysis buffer (150 mM NaCl, 50 mM HEPES pH 7.2, 0.5% IGEPAL [Sigma-Aldrich #I3021]) containing protease inhibitors (Roche cOmplete #11836145001; 1 tablet per 50 mL Lysis Buffer). Cell pellets were resuspended by gentle pipetting until homogenous, followed by pulse sonication to shear DNA. Lysates were clarified by centrifugation at 18,500 × g for 10 minutes at 4°C. Protein concentration was determined using a BCA assay, and 1 mg of total protein per sample was adjusted to 1 mL with cold 0.5% IGEPAL lysis buffer. Protein lysates were incubated with 60 μL of a 50% slurry of Streptavidin Sepharose Beads (ThermoFisher Scientific #20353) overnight at 4°C with rotation. Beads were washed sequentially: three times with 1 mL cold 0.5% IGEPAL lysis buffer, three times with ice-cold 1× TBS, and once with 50 mM HEPES (pH 8.0). Washed beads were resuspended in 50 mM HEPES (pH 8.0) and stored at −80°C.

Beads were thawed on ice, heated to 95°C for 4 minutes, and cooled to room temperature. Each sample was digested with 2 μg trypsin (Promega #V5111) overnight at 37°C with gentle rocking. Peptides were cleaned using EasyPep^™ MS Sample Prep Kits (ThermoFisher Scientific #A57864) according to the manufactures instructions and dried in a speedvac.

Dried peptides were resuspended in 15 μL of 0.1% TFA and analyzed on an EASY-nLC 1200 system coupled to a Q Exactive HF mass spectrometer (ThermoFisher Scientific) with an EasySpray ion source. Peptides were loaded onto an Acclaim PepMap 100 C18 trap column (75 μm × 2 cm; ThermoFisher Scientific) and separated on a PepMap RSLC C18 analytical column (75 μm × 25 cm). Elution was performed using a two-step gradient: 5–27% acetonitrile with 0.1% formic acid over 60 minutes, followed by 27–40% acetonitrile with 0.1% formic acid over 45 minutes, at a flow rate of 300 nL/min. MS1 scans were acquired at 60,000 resolution over a mass range of 380–1580 m/z, with a maximum injection time of 120 ms and an AGC target of 3 × 10⁶. MS2 scans were performed at 15,000 resolution, using a normalized collision energy of 27, maximum injection time of 50 ms, and an AGC target of 2 × 10⁵.

Raw MS data were processed using Proteome Discoverer 2.4 (ThermoFisher Scientific) with the Sequest search engine. Data were searched against the UniProt human database using fully tryptic peptides, allowing up to 2 missed cleavages. Peptide length was restricted to 6–40 amino acids, with precursor and fragment mass tolerances of 10 ppm and 0.02 Da, respectively. FDR was set to ≤ 1% using Percolator.

Protein intensities were normalized to the sample with the highest total intensity. For each protein, median intensities within experimental groups were compared to control groups (e.g., empty vector or EGFP) using a one-tailed t-test assuming equal variance.

Proximal interactors consistently identified across all three experimental replicates were selected for network visualization (Figure 5B) and gene ontology (GO) analysis (Figures 5C and S9B). For GO analysis, enrichment of biological processes and CORUM protein complexes was performed using g:Profiler.¹¹⁶ The background set included all proteins detected in the mass spectrometry experiments (Table S7), including those from control samples. Proteins included in the Figure 5B network were restricted to those with a median peptide count of five or higher. Network visualization in Figure 5B was created using the Cytoscape STRING application,^123,124 for importing STRING networks.

Immunofluorescence staining

Cells were grown on 384-well (Perkin Elmer, #6057300) microplates and fixed for 15 minutes with 4% paraformaldehyde (PFA, Electron Microscopy Sciences). Fixed cells were then washed one time with PBS, permeabilized for 10 minutes (PBS/0.5% Triton X-100) and washed again one time with PBS. Next, cells were incubated with primary antibodies diluted in blocking buffer (PBS, 0.05% Tween20, 5% bovine serum albumin) for 1.5 hour. After two consecutive washes with PBS, cells were incubated with secondary antibodies diluted in blocking buffer for 1 hour and washed once with PBS. Cells were then counterstained with DAPI (Biotium, #40043) for 30 minutes and washed once with PBS. After washing, cells could be stored for extended periods of time in PBS at 4°C. All steps for IF staining were performed at ambient temperature. Primary antibodies used for immunofluorescence were: α-LAP2alpha rabbit polyclonal (1:400, Abcam, ab5162), α-Lamin B1 rabbit polyclonal (1:200, Abcam, #ab16048), α-Histone H3 (tri methyl K27) rabbit monoclonal (1:400, Abcam, #ab192985) and α-acetyl-Histone H4 (Ac-Lys16) rabbit recombinant monoclonal (1:400, Abcam, #ab109463). The secondary antibody used for immunofluorescence detection was Alexa Fluor Goat-anti-Rabbit 594 (1:400, Invitrogen, #A11012).

Recombinant protein production and purification

All recombinant proteins were produced in E. coli BL21(DE3) Star cells (ThermoFisher Scientific #C600003) in autoinduction media¹²⁷ at 20°C overnight. Full-length ZNF207 and the C-terminal truncation (ZNF207(1–122), corresponding to residues M1-A122) were expressed as fusion proteins carrying an N-terminal, TEV-cleavable MBP-tag, and a C-terminal StrepII-tag. Harvested cells were resuspended in protein buffer (50 mM HEPES, pH 7.5, 200 mM NaCl, 5% (v/v) glycerol) and lysed by sonication. The lysate was clarified by centrifugation at 40,000 g for 30 minutes and then incubated with 1 ml of amylose resin (NEB #E8021S) for 90 minutes at 6°C. Contaminants were removed by washing with protein buffer before elution in protein buffer supplemented with 30 mM D-(+)-maltose. The MBP tag was then cleaved off with recombinant hyperTEV60¹²⁸ protease overnight at 6°C. Afterwards, the NaCl concentration was reduced to 50 mM before further purification by anion exchange chromatography on a Capto HiRes Q 10/100 column (Cytiva) with a linear gradient starting with IEX buffer A (10 mM HEPES, pH 7.5, 50 mM NaCl, 5% (v/v) glycerol) and ending with IEX buffer B (10 mM HEPES pH 7.5, 1000 mM NaCl, 5% (v/v) glycerol). ZNF207(1–122) was further purified by size exclusion chromatography on a HiLoad Superdex 75 26/600 column (Cytiva) equilibrated in SEC buffer (10 mM HEPES, pH 7.5, 200 mM NaCl, 5% (v/v) glycerol). The peak fractions were then pooled together, concentrated with a centrifugal filter, flash-frozen in liquid nitrogen, and stored at −80°C.

SNRPA (U1-A) was produced as a fusion protein carrying an N-terminal, HRV 3C-cleavable His6-SUMO3 tag. Harvested cells were resuspended in protein buffer (50 mM HEPES, pH 7.5, 1 M NaCl, 5% (v/v) glycerol, 20 mM imidazole) and lysed by sonication. The lysate was clarified by centrifugation at 40,000 g for 30 minutes and loaded onto a 5 ml nickel-charged HisTrap column (Cytiva). Contaminants were removed by washing with a protein buffer supplemented with 40 mM imidazole, and the protein was eluted in a protein buffer supplemented with 250 mM imidazole. SNRPA was further purified by size exclusion chromatography on a HiLoad Superdex 200 16/600 column (Cytiva) equilibrated in SEC buffer (10 mM HEPES, pH 7.5, 200 mM NaCl, 5% (v/v) glycerol). The His6-SUMO3 tag was cleaved off with recombinant HRV 3C protease overnight at 6°C. The protein was further purified by cation exchange chromatography on a Mono S 4.6/100 column (Cytiva) and eluted with a linear gradient using the same buffers as those used for the anion exchange chromatography of ZNF207. The peak fractions were flash-frozen in liquid nitrogen and stored at −80°C.

SNRPC (U1-C) was expressed as a fusion protein carrying a C-terminal His6-tag, while SNRNP70 (U1–70K) (residues M1-T216) was expressed as a fusion protein carrying an N-terminal, HRV-3C-cleavable His6-SUMO3-tag. For both proteins, the harvested cells were resuspended in protein buffer (50 mM HEPES, pH 7.5, 1 M NaCl, 5% (v/v) glycerol, 5 mM CHAPS, 20 mM imidazole), and then lysed by sonication. The lysate was clarified by centrifugation at 40,000 g for 30 minutes and loaded onto a 5 ml nickel-charged HisTrap column (Cytiva). Contaminants were removed by washing with protein buffer supplemented with 40 mM imidazole. For SNRNP70, the protein was eluted in elution buffer (50 mM HEPES, pH 7.5, 200 mM NaCl, 5 mM CHAPS, 5% (v/v) glycerol, 250 mM imidazole), flash-frozen in liquid nitrogen, and stored at −80°C. SNRPC was eluted in protein buffer supplemented with 500 mM imidazole, and further purified by size exclusion chromatography on a HiLoad Superdex 75 26/600 column (Cytiva) in SEC buffer (10 mM HEPES, pH 7.5, 200 mM NaCl, 5 mM CHAPS, 5% (v/v) glycerol). Peak fractions were then pooled together, concentrated with a centrifugal filter, flash-frozen in liquid nitrogen, and stored at −80°C.

Preparation of U1 snRNA

The U1 snRNA (GenBank: NR_004430.4) was synthesized as a gene fragment (Azenta) with an upstream T7 promoter. The gene fragment was amplified by PCR, and the purified PCR products were used as templates for in vitro transcription (IVT) using the HiScribe T7 High Yield RNA Synthesis Kit (NEB #E2040S). IVT products were separated by size-exclusion chromatography on a Superdex 200 increase 10/300 GL column (Cytiva) in SEC buffer (10 mM HEPES, pH 7.5, 200 mM NaCl). The fractions containing the intact RNA substrates were pooled, ethanol precipitated, and resuspended in RNase-free water.

Electrophoretic mobility shift assay (EMSA)

Electrophoretic mobility shift assay (EMSA) binding reactions contained 25 nM refolded U1 snRNA and 25–800 nM ZNF207 protein. The reactions were carried out for 15 minutes at 37°C in a buffer containing 20 mM PIPES, pH 6.8, 10 mM KCl, 40 mM NaCl, 2 mM Mg(OAc)2, 3% (v/v) Ficoll 400, and 0.05% (v/v) NP-40. The RNA-protein complexes were analysed by electrophoresis on a nondenaturing polyacrylamide gel in 0.5x TBE buffer, pH 8.3, at 10 V cm–1. Gels were stained in 0.5x TBE pH 7.5 with 1x SYBR Gold (ThermoFisher Scientific) for 5 minutes before analysis. Images were quantified using FiJi.¹²⁹

Pull-down assays

Purified proteins were immobilized as bait via their C-terminal StrepII-tag on homemade streptavidin resin. 250 pmol of bait protein was incubated for 1 hour in 1x PBS-T (155.17 mM NaCl, 1.06 mM KH2PO4, 2.96 mM Na2HPO4, 0.05% (v/v) Tween-20) at 6°C under constant agitation. Unbound protein was removed following two washes with pull-down buffer, and 250 pmol of prey protein was incubated for 2 hours with the bead-bound protein. Finally, the beads were washed three times with binding buffer, and the bound proteins were eluted using binding buffer supplemented with 50 mM biotin. Eluted proteins were analyzed using SDS-PAGE, followed by Coomassie blue staining. For reactions containing U1 snRNA, the RNA was denatured for 3 minutes at 95°C, rapidly cooled down on ice, and refolded for 30 minutes in 1x refolding buffer (10 mM Tris-HCl, pH 6.9, 100 mM KCl, 10 mM MgCl2) at room temperature. The refolded U1 snRNA was incubated with the individual U1 snRNP proteins or a mixture of these proteins for 30 minutes at 30°C in 1x PBS-T before being added to the immobilized bait protein.

In vitro splicing

To generate the LMNA mutant minigene splicing reporter DNA template, PCR was performed using the mutant LMNA reporter from the lentiviral vector used for the screen and a primer set containing a T7 promoter sequence. The corresponding pre-mRNA was synthesized by in vitro transcription according to the manufacturer’s protocol (Invitrogen, #A57620). RNA integrity and quality were confirmed using an Agilent TapeStation (Agilent, #5067–5576 and #5067–5577). For in vitro splicing reactions, 5 ng of LMNA mutant pre-mRNA was incubated in a 10 μL reaction containing: 0.25 μL of 60 mM ATP (ThermoFisher Scientific, #R0441), 0.25 μL of 0.2 M creatine phosphate (Sigma-Aldrich, #10621714001), 0.25 μL of 0.2 M DTT (ThermoFisher Scientific, #R0861), 0.25 μL of 50 mM MgCl₂, 2 μL of 13% polyvinyl alcohol (ThermoFisher Scientific, #041241.22), 0.25 μL of RNase inhibitor, and 6 μL of HeLa nuclear extract (Protein One, #P0002–02). Reactions were incubated at 30°C for 1 hour. Following incubation, RNA was purified with Trizol and eluted in 20 μL of nuclease-free water. Two microliters of the purified RNA were used for RT-PCR analysis of the LMNA mutant minigene. The primers are listed in Table S2. For immunodepletion assays, pre-conjugated Dynabeads^™ Protein G (Invitrogen, #10003D) were incubated with either ZNF207 antibody (ThermoFisher Scientific, #PA530641) or beads alone (mock control). Nuclear extract was incubated with the antibody–bead complexes at 4 °C for 5 hours prior to use in splicing reactions.

eCLIP sequencing and analysis

Twelve million cells were plated per cell line (FLAG-EGFP, FLAG-ZNF207 N-terminus, or ZNF207-FLAG C-terminus) in 15 cm cell culture dishes. After 24 hours, doxycycline was added to the media at a final concentration of 0.5 μg/mL to induce protein expression. Cells were washed twice with PBS and resuspended in 3 mL of PBS. UV-crosslinking was performed at 254 nm with an energy setting of 400 mJ/cm². Following crosslinking, cells were counted, and approximately 21 million viable cells were pelleted, frozen, and sent to EclipsBio for further assays, library generation and sequencing. eCLIP was performed based on the single-end seCLIP protocol¹³⁰ with specific modifications. Samples were lysed using 1 mL of eCLIP lysis buffer containing 6 μL of Proteinase Inhibitor Cocktail and 20 μL of Murine RNase Inhibitor. Lysates underwent two rounds of sonication (4 minutes total) with 30-second ON/OFF cycles at 75% amplitude. For immunoprecipitation, a pre-validated anti-FLAG antibody was pre-coupled to Anti-Rabbit IgG Dynabeads (ThermoFisher Scientific #11202D). The antibody-bead complex was added to the lysate and incubated overnight at 4°C. Before immunoprecipitation, 2% of the lysate was set aside as the paired input control. The remaining sample was subjected to magnetic separation and washed thoroughly with eCLIP high-stringency wash buffers. Immunoprecipitation and input samples were excised from the membrane at a size corresponding to ~75 kDa above the expected protein band. Subsequent steps, including RNA adapter ligation, IP-Western blot validation, reverse transcription, DNA adapter ligation, and PCR amplification, were performed as previously described in the seCLIP protocol.¹³⁰

We used FastQC and MultiQC¹¹⁸ for read quality assessments at various stages of the eCLIP-Seq analytical pipeline. UMI (Unique Molecular Identifier) barcodes were identified and annotated using UMI-tools.¹²¹ Cutadapt¹¹³ was used for the read trimming. Trimmed reads were first aligned to repetitive elements of human genome (sourced from RepBase, GIRI (Genetic Information Research Institute) (https://www.girinst.org/server/RepBase/)) so that they can be later separated to increase accuracy of the eCLIP-Seq peak identification in the subsequent steps. The unaligned reads from this step were used to perform the main alignment with human genome (hg38) using gencode v44 primary assembly genome fasta and GTF files. All the mapping steps were performed with STAR.¹²⁰ The mapped reads were deduplicated using UMI-tools, which considers UMI annotations from the preprocessing step and alignment positions to remove duplicate reads from the samples. Thus, generated final alignments were considered for peak calling and generation of metagene coverage plots. We used SAMtools¹¹⁹ to merge, filter, and index Binary Alignment Files (BAM) and BEDTools¹¹¹ to manage BED files. We used the htseq-clip tool (v2.19)⁸³ with default parameters for peak calling, followed by statistical analysis using the DEWSeq tool (v1.18).⁸⁴ Regions of enrichment (enrichedWindowRegions) were identified based on a log2 fold change (L2FC) ≥ 0.5 and an adjusted p-value (padj) ≤ 0.05 was calculated using independent hypothesis weighting (IHW).¹¹⁷ These regions were further analyzed to determine the RNA biotypes, genomic locations, and gene categories associated with the bound targets.

DeepTools¹¹⁴ was used to generate ZNF207 metagene coverage plots using a subtraction-based IP_vs_Input normalization method. We determined ZNF207 regulated cassette exons by assessing siZNF207 responsive splicing events that are rescued by ZNF207_FL overexpression from RNA-Seq data analysis. We selected all the alternative exons that are not regulated by ZNF207 as a control for comparison with following criteria: (1) 90 ≥ PSI ≥ 10 for siZNF207 knock-down condition (2) |dPSI| < 5 and probability < 0.95 for siZNF207 knock-down condition. We used cassette exons of size ≥ 6bp of protein coding genes with minimum FPKM expression value of 0.1 to perform comparative metagene enrichment analysis for ZNF207 eCLIP-Seq data.

Design of genome-wide knock-out library

The CHyMErA hybrid guide RNA (hgRNA) gene knockout library was constructed to enable dual-targeting of individual genes and combinatorial targeting of paralog gene pairs using Cas9 and Cas12a enzymes. The process began with the curation of protein-coding genes from the Gencode Comprehensive Annotation v43. Genes and transcripts were filtered to ensure high-quality annotations. Only genes labeled as protein-coding were retained, while polymorphic pseudogenes and transcript isoforms with fewer than two untranslated regions (UTRs) were excluded, except when all transcripts for a gene had fewer than two UTRs. Transcripts with a transcript support level of 3 or higher were removed unless their exclusion would eliminate a gene entirely. Additionally, fusion genes and annotations within the mitochondrial genome were excluded.

Single guide RNAs (sgRNAs) targeting human protein-coding genes were then selected from a database of all possible Cas9 and Cas12a sgRNAs, based off the presence of “NGG” and “TTTV” PAMs, respectively. Only sgRNAs with upper and lower cut sites within the coding sequence (CDS) of a gene were considered. These sgRNAs were required to target a single site in the genome (Hamming.0 = 1) without any genomic sites within one mismatch (Hamming.1 = 0), and Cas12a spacers with no more than 2 sites in the genome with 2 mismatches (Hamming.2 <= 2). Guide RNAs with off-target GuideScan scores^131,132 exceeding 0.16 for both enzymes (Cas9 and Cas12a) were retained. On-target efficiency was assessed using Cas9 Rule Set 2 scores¹³³ and Cas12a-specific CHyMErA_Net scores,³³ both of which were required to exceed 0.1. Additional filtering was applied to eliminate guide RNAs containing or creating BveI and Eco47III restriction sites, which are critical for downstream library construction and reporter cloning.

Each Cas9 and Csa12a sgRNA for every unique Ensembl GeneID was ranked based on its quality, assessed by transcript coverage (Transcript_Percentage_Targeted), cut site location, spacer overlap, and On-Target Score. The percentage of transcript isoforms targeted by each guide was evaluated, with optimal percentage of transcripts targeted being 100%, cut sites located at least 10% downstream of the start codon (N-terminal) and no more than 50% upstream of the stop codon (C-terminal) of every transcript. Spacers with no shared genomic location with other chosen guides were selected. Guides were iteratively filtered, with quality thresholds reducing by fifths (Transcript_Percentage_Targeted from 1 −> 0.8, cut sites within 8% downstream of the start codon and 40% upstream of the stop codon, and minimal spacer overlaps from 0−>4) with guides passing each quality threshold ordered by On-Target Score until all guides have been ranked. Based on this assessment, sgRNAs assigned quality ranks from 1 (highest) to 6 (lowest) based off the Round of quality each guide passed. The top 25 sgRNAs for each gene were selected for both Cas9 (a1-a25) and Cas12a (b1-b25).

To construct hybrid guides RNAs (hgRNAs), the top 25 sgRNAs for each enzyme were paired combinatorially. hgRNAs were evaluated based on the same criteria as sgRNAs with the added quality criteria of minimal genomic distance between Cas9 and Cas12a cut sites. hgRNA pairs targeting all transcript isoforms in the upstream-middle of the CDS with no spacer overlap and an optimal genomic distance of at least 150 base pairs were prioritized. If these criteria were not met, iterative quality thresholds were applied to relax the selection criteria, with lower thresholds reducing the stringency of Percentage_Transcript_Targeted, transcript CDS cut site, spacer overlap and hgRNA cut genomic distance requirements by one fifth until 4 hgRNAs were found. Each hgRNA was labeled according to the quality rank of its Cas9 and Cas12a components (e.g., a6-b3, where a6 represents the 6th-ranked Cas9 guide and b3 the 3rd-ranked Cas12a guide). The selection process ensured that the best pairs were prioritized based on a balance of these criteria.

To ensure diversity among hgRNAs, selected pairs were shuffled and reanalyzed to maximize the distribution of quality sgRNAs while maintaining overall hgRNA quality. For example, if the initial selection yielded pairs such as a1-b1, a2-b2, a3-b3, and a4-b4, a subsequent reshuffling could generate pairs such as a1-b4, a2-b3, a3-b2, and a4-b1, provided the reshuffled pairs maintain hgRNA quality thresholds.

In cases with limited sgRNA availability, specific rules guided the selection process. When only three Cas9 and three Cas12a sgRNAs were available, only three hgRNAs were selected. For three Cas9 and two Cas12a sgRNAs, two hgRNAs were chosen along with a Cas9-only guide paired with a Cas12a intergenic guide, and vice versa for two Cas9 and three Cas12a sgRNAs. If three Cas9 and one Cas12a sgRNAs were available, one hgRNA was formed along with two Cas9-only guides paired with two Cas12a intergenic guides and vice versa for one Cas9 and three Cas12a sgRNAs. When two Cas9 and two Cas12a sgRNAs were available, two hgRNAs were chosen alongside the same Cas9 sgRNAs paired with Cas12a intergenic guides. For two Cas9 and one Cas12a sgRNA, one hgRNA was formed alongside two Cas9 sgRNAs paired with 2 Cas12a intergenic guides and one Cas12a sgRNA paired with one Cas9 intergenic guide and vice versa for 1 Cas9 and 2 Cas12a sgRNAs. If only one Cas9 and one Cas12a guide were available, three guide combinations were formed: one hgRNA, one Cas9-only guide, and one Cas12a-only guide. For cases where two or more Cas9 sgRNAs were available but no Cas12a sgRNAs, intergenic Cas12a guides were added, and the reverse was applied for cases where two or more Cas12a sgRNAs were available but no Cas9 sgRNAs. No hgRNAs were selected for genes where only a single Cas9 or Cas12a sgRNA was available. This approach allowed flexibility while maintaining the library’s robustness and comprehensiveness.

The targeted paralogs were chosen based on definitions established in previous studies.^33,134–136 Additionally, 624 hand-picked gene pairs, primarily involved in splicing-related processes, were included to enhance the focus on splicing regulation. The construction of the paralog knockout hybrid guide RNA (hgRNA) library utilized computational criteria to identify optimal Cas9 and Cas12a sgRNAs for targeting paralog gene pairs. To begin, information such as gene IDs and genomic coordinates was extracted using the BioMart R package with the hsapiens_gene_ensembl dataset (version 104). Key attributes, including chromosome name, start position, HGNC symbol, Ensembl gene ID, description, external gene source, and gene biotype, were retrieved using the getBM function with filters “hgnc_symbol” (or “external_synonym”), “chromosome_name,” and “biotype.” Paralog pairs were then sorted based on their library of origin, which included both previously generated paralog combinations and handpicked paralog pairs.

For each paralog pair, the selection process aimed to identify the best Cas9 and Cas12a sgRNAs for targeting Paralog A and Paralog B based on the same criteria used to rank sgRNAs for the dual knockout hgRNA library detailed above. When at least two Cas9 and Cas12a sgRNAs were available for both paralogs, hgRNA combinations were extracted, targeting each paralog twice for a total of four hybrid guides. Two Cas9 (A1- and A2-) and two Cas12a (-A1 and -A2) guides targeting gene A and two Cas9 (B1- and B2-) and Cas12a (-B1 and -B2) guides targeting gene B will make hybrid guide RNA combinations (A1-B1, A2-B2, B1-A1, and B2-A2) with Cas9 guides in the first position and Cas12a guides in the second position). If no Cas9 guides targeted Paralog A but Cas9 guides were available for Paralog B, Cas12a guides targeting Paralog A were used to form hybrid combinations (e.g. B1-A1, B2-A2, B3-A3, B4-A4). By default, if only Cas9 guides or Cas12a are available to target gene A and B, no hgRNAs will be chosen.

The full library was further supplemented with 331 fully intergenic hgRNAs and 99 non-targeting hgRNAs to serve as controls. A pool of 95,893 oligos, each 187 nucleotides in length, was designed to include a 20-nt Cas9 guide sequence, followed by the Streptococcus pyogenes Cas9 tracrRNA, the Acidaminococcus sp. Cas12a direct repeat (DR), and a 23-nt Cas12a guide sequence (Table S1). The oligos were synthesized by TWIST Biosciences and cloned into the CRASP-Seq vector as described above.

Design base editor library

We designed an sgRNA tiling library to target the coding sequences of all isoforms for 39 selected genes. Exon coordinates were derived from the GRCh38 assembly using Gencode version 46 (GFF3 file). To ensure compatibility with downstream cloning steps and maintain sgRNA efficiency, guides containing homopolymer stretches (AAAAAA, TTTTT, CCCCCC, GGGGGG) or restriction motifs (e.g., GATATC, GCAGGT, ACCTGC) were excluded. Additional filters removed sgRNAs with sequences starting with CAGGT or ATATC, or ending with GCAG, to prevent interference with EcoRV and BfuAI/BveI recognition sites. To enable base editing and ensure effective targeting, only sgRNAs overlapping at least 1 nucleotide of an exon and containing an A or C residue within positions 1–12 of the spacer sequence were retained. Both plus- and minus-strand sgRNAs were included. Off-target effects were minimized by applying GuideScan thresholds: an off-target score > 0.16 and a Hamming distance ≤ 2, as established previously.¹³² In total, the library consists of 6,984 sgRNAs targeting the 39 genes, supplemented by 1,520 intergenic and 92 non-targeting control sgRNAs, that were selected from our previous Cas9 sgRNA libraries (Table S8).¹¹ The library oligo pool consists of 144-nucleotide sequences, encompassing the Cas9 protospacer region followed by a modified tracrRNA sequence.¹³⁷ These sequences are flanked by stuffer regions containing BveI restriction sites, enabling efficient cloning into the digested CRASP-Seq vector.

QUANTIFICATION AND STATISTICAL ANALYSIS

Analysis of CRASP-Seq data

The CRASP-Seq data were analyzed using a custom bioinformatics pipeline consisting of several sequential steps. Briefly, the pipeline involves quantifying the Percent Spliced In (PSI) index for the minigene reporter associated with each hgRNA expressed in the cells. UMIs introduced during cDNA synthesis is used to remove PCR duplicates, while CBCs are applied to randomly generate two replicate samples. By applying a 10% false discovery rate (FDR) threshold, we establish a PSI cutoff to determine whether individual hgRNAs positively or negatively regulate a minigene reporter. Positive and negative regulators are defined as those with at least two hgRNAs detected to affect reporter splicing. Only regulators identified in both experimental replicates were considered significant hits.

More specifically, first we performed mapping and guide annotation by retrieving each component of the CRISPR-Cas system and splicing reporter cassette, including the Cas9 guide, Cas12a guide, CBC, and UMIs from Read 1, as well as minigene splicing event monitoring from Read 2. These components were extracted from sequencing reads using plasmid backbone-adjacent anchor sequences and mapped with the aligner tool STAR¹²⁰ to annotate the guide sequences based on the genome-wide CRISPR library. Alignment files were then managed using SAMtools¹¹⁹ to facilitate downstream applications. PCR-mediated sequence deduplication was addressed by monitoring CBCs and UMIs, allowing a hamming distance of 1 for each sequence element to remove duplicates. Only fully annotated and unique reads were retained for quantifying splicing regulation and linking splicing outcomes to specific genetic perturbations. To assess splicing outcomes, Percent Splicing In (PSI) and ΔPSI scores were calculated as follows:

PSI (Percent Splicing In) = (IE/(IE+EE)) * 100, where IE represents inclusion event counts and EE represents exclusion event counts.
ΔPSI was defined as PSI_guide - PSI_intergenic, where PSI_guide represents the PSI value for a particular guide, and PSI_intergenic represents the cumulative PSI value of all intergenic guides in the sequencing library.

To identify individual hgRNAs affecting the splicing reporter, we calculated10% FDR by analyzing intergenic hgRNAs and setting PSI cutoffs to identify significant hgRNAs as those at the 5% extremes of the intergenic distribution. At the gene level, we further required that the ΔPSI for each gene (calculated across all guides targeting the same gene) met this cutoff, with more than half of the guide RNAs for the gene exceeding this threshold (e.g., 3 out of 4 guides or 2 out of 3 guides). Genes with at least two guides identified as hits were assigned as splicing regulator hits. Additionally, genes were considered hits only if they met these criteria in both experimental replicates, as determined by splitting data based on the CBC barcode, to ensure robust hit calling.

The base editor screen was analyzed by estimating ΔPSI scores for all sgRNAs and genes (by merging all the sgRNAs targeting the same gene). To plot the sequence-level tiling profile of ZNF207, the protein region corresponding to the guide RNAs were retrieved using Ensembldb package (version 2.30.0). We extracted genome coordinates of ZNF207 (Transcript ENST00000394670.9) from Ensembl 113 using AnnotationHub and then used the genomeToProtein function to map the guide RNA (lying completely within the exons) to the positions of protein sequence. For the genes with forward orientation, we defined the positions by considering the start index of mapped positions while for the reverse orientation last index was taken. The ΔPSI score within specific ZNF207 windows was calculated as the average of the ΔPSI values from adenine and cytosine base editor screens for each guide RNA overlapping these windows. For gene-level analysis, the average adenine and cytosine ΔPSI scores for each gene were calculated as described above. These scores were then used to plot bar graphs, with genes arranged in ascending order of their ΔPSI scores.

Gene expression and splicing analysis from RNA-sequencing data

The raw FASTQ reads were trimmed with Cutadapt¹¹³ tool and mapped to human genome (hg38) with STAR aligner.¹²⁰ We used gencode v44 reference files (primary assembly genome fasta and primary assembly GTF (Gene Transcript Features) for mapping and transcript annotation steps. STAR generated gene count files were used for differential gene expression analysis with DE-Seq2.¹¹⁵ We used SAMtools¹¹⁹ to manage alignment files for various steps such as merging and indexing Binary Alignment Files (BAM). Following statistical parameters were calculated to study differential gene expression either through custom scripts or through existing R packages such as DESeq2: (1) FPKM (Fragments per Kilo-base of Gene per Million Reads): ((Paired Reads/library size) *1000000)/ gene size) *1000 (2) L2FC (Log2 Fold Change): −log2 (Test Counts / Control Counts). ZNF207-regulated genes were defined as those exhibiting an absolute log2 fold change (LFC) difference of ≥ 0.35 between siControl and siZNF207 conditions, with an adjusted p-value < 0.05. Additionally, these genes were required to show similar differences between siZNF207-treated cells expressing the empty vector and those expressing C-terminally tagged ZNF207. Only genes with FPKM values > 0.1 were included in the analysis. For GO analysis (Figure S7B), enrichment of biological processes was performed using g:Profiler.^116,124 The background set comprised all protein-coding genes with FPKM values greater than 0.1.

Whippet¹²² tool was used to perform differential splicing analysis with trimmed FASTQ files and STAR generated alignment files to identify splicing events in any test_vs_control comparison using above mentioned reference files. ZNF207-regulated splicing events were identified as those displaying an absolute splicing change (ΔPSI) of ≥10 between siControl and siZNF207 conditions, with a probability score of ≥0.95. Furthermore, these events had to exhibit consistent differences between siZNF207-treated cells expressing an empty vector and those expressing C-terminally tagged ZNF207. Only genes with FPKM values exceeding 0.1 were included in the analysis.

Gene ontology enrichment analysis

Gene ontology (GO) term enrichment analysis of significant hits from the CRASP-Seq screens was conducted using g:Profiler¹¹⁶ with the protein-coding gene set targeted by the CRISPR library serving as the background reference (Table S1). Network views (Figures 1D and S4G) were visualized in Cytoscape^123,138 using the Enrichment Map plugin, which organizes categories based on shared gene overlaps to highlight related functional clusters. For the annotation of heatmap subclusters in Figure 2F, we conducted Gene Set Enrichment Analysis (GSEA) using the enricher function from the clusterProfiler¹¹² tool with default parameters.

High-throughput image acquisition, quantification, and statistical analysis

Cells were imaged in two channels (405 and 561 nm excitation lasers) in an automated fashion using a dual spinning disk high-throughput confocal microscope (Yokogawa CV7000S) with a 405/488/561/640 nm excitation dichroic mirror, a 40 air objective lens (NA 0.95), a 568 nm excitation dichroic mirror, and two 16-bit sCMOS cameras (Andor, 2560 × 2160 pixels) with binning set to 2 (Pixel size: 0.325 nm for 40X and 0.216 nm for 60X nm). Emission bandpass filters were used for each channel: 445/45 and 600/37 nm. Sixteen randomly selected fields of view were imaged per well in a single optimal focal plane. Images were corrected on the fly using a geometric correction for camera background, illumination field (vignetting), camera alignment, and chromatic aberrations. Corrected images were saved and stored as 16-bit TIFF files.

Captured images from high-throughput imaging experiments were analyzed using SiMA 1.3 (Revvity Signals). Briefly, nuclei regions of interest (ROI) were segmented using the DAPI (405 nm) channel and nucleus ROIs adjacent to the image edges were excluded from subsequent image analysis steps. The mean fluorescence intensity for the nucleus ROI was measured in the 561 nm channel. SiMA results were exported as comma-separated text files and analyzed using R. 600–1000 cells were analyzed per condition.

High-throughput imaging experiments were performed on three technical replicates (wells) and averaged to obtain the measurement for that experiment. The experiments were repeated independently three times (n = 3), and the values for each experiment were averaged. Single cell data analysis was performed using R software and the data are shown as the average mean values of 3 different experiments +/− standard deviation (SD). Statistical analysis for the average mean values of single cell data was performed by an ANOVA one-way test, followed by a post-hoc Dunnett’s test using the cell line as the predictor variable and fixing wild-type (WT) fibroblasts treated with control siRNas (siCTL) as the negative control for the WT cells treated with siZNF207 and HGPS fibroblasts treated with siCTL as the negative control for HGPS siZNF207. Corresponding p-value is shown in every figure (Figure S5I).

Supplementary Material

Table S1

NIHMS2134080-supplement-Table_S1.xlsx^{(33.7MB, xlsx)}

Table S2

NIHMS2134080-supplement-Table_S2.xlsx^{(22.2KB, xlsx)}

Table S3

NIHMS2134080-supplement-Table_S3.xlsx^{(38.6MB, xlsx)}

Table S4

NIHMS2134080-supplement-Table_S4.xlsx^{(36.5MB, xlsx)}

Table S5

NIHMS2134080-supplement-Table_S5.xlsx^{(42.2MB, xlsx)}

Table S6

NIHMS2134080-supplement-Table_S6.xlsx^{(5.5MB, xlsx)}

Table S7

NIHMS2134080-supplement-Table_S7.xlsx^{(605.6KB, xlsx)}

Table S8

NIHMS2134080-supplement-Table_S8.xlsx^{(2.2MB, xlsx)}

Supplemental Figures

NIHMS2134080-supplement-Supplemental_Figures.pdf^{(17.1MB, pdf)}

Supplemental information can be found online at https://doi.org/10.1016/j.molcel.2025.12.003.

Highlights.

CRASP-seq: RNA-coupled CRISPR screen quantifying gene and domain impact on splicing
Profiling of five events identified 370 genes influencing alternative splicing
ZNF207 regulates splicing by interacting with U1 snRNP via its zinc-finger domains
Depletion of ZNF207 corrects the aberrant LMNA splicing underlying progeria

ACKNOWLEDGMENTS

The authors thank Michael Aregger, Wilfried Guiblet, Sandra Wolin, Soyeong Sim, Colin Wu, Moonsup Lee, and Brian Yee for constructive discussions and technical assistance. We also thank members of the Gonatopoulos-Pournatzis and Aregger groups, as well as the RNA Biology Laboratory at NCI-Frederick. We are also grateful to the CCR Sequencing Facility, as well as the NIH/NCI/CCR High Throughput Imaging Facility (HiTIF), for help with high-throughput imaging and Gianluca Pegoraro (NIH/NCI/CCR HiTIF) for help with writing the R code for high-throughput image analysis. The NMD inhibitor SMG1i was kindly provided by the Cystic Fibrosis Foundation Chemical Compound Program. Figures 1A, 1C, 6A, 7K, and the graphical abstract include elements from BioRender.com. F.P. was supported by a Walter Benjamin postdoctoral fellowship from the German Research Foundation (Deutsche Forschungsgemeinschaft) (project number 531520533). This research was supported by the Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH) Intramural Research Program (project numbers 1ZIABC012019, 1ZIABC011977, and 1ZIABC010309) and federal funds from the NCI, NIH under contract HHSN261200800001E. The contributions of the NIH authors are considered works of the United States Government. The findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS

Following the generation of the original draft, the authors used ChatGPT to assist with language refinement. After using this tool, the authors reviewed and further edited the content as needed and take full responsibility for the content of the published article.

REFERENCES

1.Kastner B, Will CL, Stark H, and Lührmann R (2019). Structural Insights into Nuclear pre-mRNA Splicing in Higher Eukaryotes. Cold Spring Harb. Perspect. Biol 11, a032417. 10.1101/cshperspect.a032417. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Wan R, Bai R, Zhan X, and Shi Y (2020). How Is Precursor Messenger RNA Spliced by the Spliceosome? Annu. Rev. Biochem 89, 333–358. 10.1146/annurev-biochem-013118-111024. [DOI] [PubMed] [Google Scholar]
3.Tholen J, and Galej WP (2022). Structural studies of the spliceosome: Bridging the gaps. Curr. Opin. Struct. Biol 77, 102461. 10.1016/j.sbi.2022.102461. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Martínez-Lumbreras S, Morguet C, and Sattler M (2024). Dynamic interactions drive early spliceosome assembly. Curr. Opin. Struct. Biol 88, 102907. 10.1016/j.sbi.2024.102907. [DOI] [PubMed] [Google Scholar]
5.Ule J, and Blencowe BJ (2019). Alternative Splicing Regulatory Networks: Functions, Mechanisms, and Evolution. Mol. Cell 76, 329–345. 10.1016/j.molcel.2019.09.017. [DOI] [PubMed] [Google Scholar]
6.Marasco LE, and Kornblihtt AR (2023). The physiology of alternative splicing. Nat. Rev. Mol. Cell Biol 24, 242–254. 10.1038/s41580-022-00545-z. [DOI] [PubMed] [Google Scholar]
7.Rogalska ME, Vivori C, and Valcárcel J (2023). Regulation of premRNA splicing: roles in physiology and disease, and therapeutic prospects. Nat. Rev. Genet 24, 251–269. 10.1038/s41576-022-00556-8. [DOI] [PubMed] [Google Scholar]
8.Baralle FE, and Giudice J (2017). Alternative splicing as a regulator of development and tissue identity. Nat. Rev. Mol. Cell Biol 18, 437–451. 10.1038/nrm.2017.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Pan Q, Shai O, Lee LJ, Frey BJ, and Blencowe BJ (2008). Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet 40, 1413–1415. 10.1038/ng.259. [DOI] [PubMed] [Google Scholar]
10.Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, and Burge CB (2008). Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476. 10.1038/nature07509. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Xiao M-S, Damodaran AP, Kumari B, Dickson E, Xing K, On TA, Parab N, King HE, Perez AR, Guiblet WM, et al. (2024). Genome-scale exon perturbation screens uncover exons critical for cell fitness. Mol. Cell 84, 2553–2572.e19. 10.1016/j.molcel.2024.05.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Fu XD, and Ares M Jr. (2014). Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet 15, 689–701. 10.1038/nrg3778. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Park JW, Parisky K, Celotto AM, Reenan RA, and Graveley BR (2004). Identification of alternative splicing regulators by RNA interference in Drosophila. Proc. Natl. Acad. Sci. USA 101, 15974–15979. 10.1073/pnas.0407004101. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Brooks AN, Duff MO, May G, Yang L, Bolisetty M, Landolin J, Wan K, Sandler J, Booth BW, Celniker SE, et al. (2015). Regulation of alternative splicing in Drosophila by 56 RNA binding proteins. Genome Res. 25, 1771–1780. 10.1101/gr.192518.115. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Tejedor JR, Papasaikas P, and Valcárcel J (2015). Genome-wide identification of Fas/CD95 alternative splicing regulators reveals links with iron homeostasis. Mol. Cell 57, 23–38. 10.1016/j.molcel.2014.10.029. [DOI] [PubMed] [Google Scholar]
16.Papasaikas P, Tejedor JR, Vigevani L, and Valcárcel J (2015). Functional splicing network reveals extensive regulatory potential of the core spliceosomal machinery. Mol. Cell 57, 7–22. 10.1016/j.molcel.2014.10.030. [DOI] [PubMed] [Google Scholar]
17.Rogalska ME, Mancini E, Bonnal S, Gohr A, Dunyak BM, Arecco N, Smith PG, Vaillancourt FH, and Valcárcel J (2024). Transcriptome-wide splicing network reveals specialized regulatory functions of the core spliceosome. Science 386, 551–560. 10.1126/science.adn8105. [DOI] [PubMed] [Google Scholar]
18.Manning KS, and Cooper TA (2017). The roles of RNA processing in translating genotype to phenotype. Nat. Rev. Mol. Cell Biol 18, 102–114. 10.1038/nrm.2016.139. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Scotti MM, and Swanson MS (2016). RNA mis-splicing in disease. Nat. Rev. Genet 17, 19–32. 10.1038/nrg.2015.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Gordon LB, Brown WT, and Collins FS (2003). Hutchinson-Gilford Progeria Syndrome. In GeneReviews^® [Internet], Adam MP, Bick S, Mirzaa GM, Pagon RA, Wallace SE, and Amemiya A, eds. (University of Washington; ), pp. 1993–2025. https://www.ncbi.nlm.nih.gov/books/NBK1121/. [PubMed] [Google Scholar]
21.Gordon LB, Rothman FG, López-Otín C, and Misteli T (2014). Progeria: a paradigm for translational medicine. Cell 156, 400–407. 10.1016/j.cell.2013.12.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Bonnal SC, López-Oreja I, and Valcárcel J (2020). Roles and mechanisms of alternative splicing in cancer - implications for care. Nat. Rev. Clin. Oncol 17, 457–474. 10.1038/s41571-020-0350-x. [DOI] [PubMed] [Google Scholar]
23.Bradley RK, and Anczuków O (2023). RNA splicing dysregulation and the hallmarks of cancer. Nat. Rev. Cancer 23, 135–155. 10.1038/s41568-022-00541-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Kahles A, Lehmann KV, Toussaint NC, Hüser M, Stark SG, Sachsenberg T, Stegle O, Kohlbacher O, Sander C; Cancer; Genome; Atlas; Research Network, and et al. (2018). Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients. Cancer Cell 34, 211–224.e6. 10.1016/j.ccell.2018.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.PCAWG Transcriptome; Core Group, Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, and et al. (2020). Genomic basis for RNA alterations in cancer. Nature 578, 129–136. 10.1038/s41586-020-1970-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Yoshimi A, Lin KT, Wiseman DH, Rahman MA, Pastore A, Wang B, Lee SCW, Micol JB, Zhang XJ, de Botton S, et al. (2019). Coordinated alterations in RNA splicing and epigenetic regulation drive leukaemogenesis. Nature 574, 273–277. 10.1038/s41586-019-1618-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Jung H, Lee D, Lee J, Park D, Kim YJ, Park WY, Hong D, Park PJ, and Lee E (2015). Intron retention is a widespread mechanism of tumor-suppressor inactivation. Nat. Genet 47, 1242–1248. 10.1038/ng.3414. [DOI] [PubMed] [Google Scholar]
28.David CJ, Chen M, Assanah M, Canoll P, and Manley JL (2010). HnRNP proteins controlled by c-Myc deregulate pyruvate kinase mRNA splicing in cancer. Nature 463, 364–368. 10.1038/nature08697. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Wang Z, Jeon HY, Rigo F, Bennett CF, and Krainer AR (2012). Manipulation of PK-M mutually exclusive alternative splicing by antisense oligonucleotides. Open Biol. 2, 120133. 10.1098/rsob.120133. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Paronetto MP, Bernardis I, Volpe E, Bechara E, Sebestyén E, Eyras E, and Valcárcel J (2014). Regulation of FAS exon definition and apoptosis by the Ewing sarcoma protein. Cell Rep. 7, 1211–1226. 10.1016/j.celrep.2014.03.077. [DOI] [PubMed] [Google Scholar]
31.Chen K, Xiao H, Zeng J, Yu G, Zhou H, Huang C, Yao W, Xiao W, Hu J, Guan W, et al. (2017). Alternative Splicing of EZH2 pre-mRNA by SF3B3 Contributes to the Tumorigenic Potential of Renal Cancer. Clin. Cancer Res 23, 3428–3441. 10.1158/1078-0432.Ccr-16-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Thomas JD, Polaski JT, Feng Q, De Neef EJ, Hoppe ER, McSharry MV, Pangallo J, Gabel AM, Belleville AE, Watson J, et al. (2020). RNA isoform screens uncover the essentiality and tumor-suppressor activity of ultraconserved poison exons. Nat. Genet 52, 84–94. 10.1038/s41588-019-0555-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Gonatopoulos-Pournatzis T, Aregger M, Brown KR, Farhangmehr S, Braunschweig U, Ward HN, Ha KCH, Weiss A, Billmann M, Durbic T, et al. (2020). Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9–Cas12a platform. Nat. Biotechnol 38, 638–648. 10.1038/s41587-020-0437-z. [DOI] [PubMed] [Google Scholar]
34.Aregger M, Xing K, and Gonatopoulos-Pournatzis T (2021). Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening. Nat. Protoc 16, 4722–4765. 10.1038/s41596-021-00595-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Ward HN, Aregger M, Gonatopoulos-Pournatzis T, Billmann M, Ohsumi TK, Brown KR, Blencowe BJ, Moffat J, and Myers CL (2021). Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline. Nat. Protoc 16, 4766–4798. 10.1038/s41596-021-00596-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Zetsche B, Heidenreich M, Mohanraju P, Fedorova I, Kneppers J, DeGennaro EM, Winblad N, Choudhury SR, Abudayyeh OO, Gootenberg JS, et al. (2016). Multiplex gene editing by CRISPR–Cpf1 using a single crRNA array. Nat. Biotechnol 35, 31–34. 10.1038/nbt.3737. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Fonfara I, Richter H, Bratovič M, Le Rhun A, and Charpentier E (2016). The CRISPR-associated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR RNA. Nature 532, 517–521. 10.1038/nature17945. [DOI] [PubMed] [Google Scholar]
38.Mu W, Starmer J, Yee D, and Magnuson T (2018). EZH2 variants differentially regulate polycomb repressive complex 2 in histone methylation and cell differentiation. Epigenetics Chromatin 11, 71. 10.1186/s13072-018-0242-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Gier RA, Budinich KA, Evitt NH, Cao Z, Freilich ES, Chen Q, Qi J, Lan Y, Kohli RM, and Shi J (2020). High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening. Nat. Commun 11, 3455. 10.1038/s41467-020-17209-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Braun SE, Shi X, Qiu G, Wong FE, Joshi PJ, Prasad VR, and Johnson RP (2007). Instability of retroviral vectors with HIV-1-specific RT aptamers due to cryptic splice sites in the U6 promoter. AIDS Res. Ther 4, 24. 10.1186/1742-6405-4-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Hoek TA, Khuperkar D, Lindeboom RGH, Sonneveld S, Verhagen BMP, Boersma S, Vermeulen M, and Tanenbaum ME (2019). Single-Molecule Imaging Uncovers Rules Governing Nonsense-Mediated mRNA Decay. Mol. Cell 75, 324–339.e11. 10.1016/j.molcel.2019.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Le Hir H, Gatfield D, Izaurralde E, and Moore MJ (2001). The exonexon junction complex provides a binding platform for factors involved in mRNA export and nonsense-mediated mRNA decay. EMBO J. 20, 4987–4997. 10.1093/emboj/20.17.4987. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Diem MD, Chan CC, Younis I, and Dreyfuss G (2007). PYM binds the cytoplasmic exon-junction complex and ribosomes to enhance translation of spliced mRNAs. Nat. Struct. Mol. Biol 14, 1173–1179. 10.1038/nsmb1321. [DOI] [PubMed] [Google Scholar]
44.Chazal PE, Daguenet E, Wendling C, Ulryck N, Tomasetto C, Sargueil B, and Le Hir H (2013). EJC core component MLN51 interacts with eIF3 and activates translation. Proc. Natl. Acad. Sci. USA 110, 5903–5908. 10.1073/pnas.1218732110. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Chen M, David CJ, and Manley JL (2012). Concentration-dependent control of pyruvate kinase M mutually exclusive splicing by hnRNP proteins. Nat. Struct. Mol. Biol 19, 346–354. 10.1038/nsmb.2219. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Wang Z, Chatterjee D, Jeon HY, Akerman M, Vander Heiden MG, Cantley LC, and Krainer AR (2012). Exon-centric regulation of pyruvate kinase M alternative splicing via mutually exclusive exons. J. Mol. Cell Biol 4, 79–87. 10.1093/jmcb/mjr030. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Königs V, de Oliveira Freitas Machado C, Arnold B, Blümel N, Solovyeva A, Löbbert S, Schafranek M, Ruiz De Los Mozos I, Wittig I, McNicoll F, et al. (2020). SRSF7 maintains its homeostasis through the expression of Split-ORFs and nuclear body assembly. Nat. Struct. Mol. Biol 27, 260–273. 10.1038/s41594-020-0385-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Lareau LF, Inada M, Green RE, Wengrod JC, and Brenner SE (2007). Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature 446, 926–929. 10.1038/nature05676. [DOI] [PubMed] [Google Scholar]
49.Scaffidi P, and Misteli T (2005). Reversal of the cellular phenotype in the premature aging disease Hutchinson-Gilford progeria syndrome. Nat. Med 11, 440–445. 10.1038/nm1204. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Leclair NK, Brugiolo M, Urbanski L, Lawson SC, Thakar K, Yurieva M, George J, Hinson JT, Cheng A, Graveley BR, et al. (2020). Poison Exon Splicing Regulates a Coordinated Network of SR Protein Expression during Differentiation and Tumorigenesis. Mol. Cell 80, 648–665.e9. 10.1016/j.molcel.2020.10.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Karousis ED, and Mühlemann O (2019). Nonsense-Mediated mRNA Decay Begins Where Translation Ends. Cold Spring Harb. Perspect. Biol 11, a032862. 10.1101/cshperspect.a032862. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Kishor A, Fritz SE, and Hogg JR (2019). Nonsense-mediated mRNA decay: The challenge of telling right from wrong in a complex transcriptome. Wiley Interdiscip. Rev. RNA 10, e1548. 10.1002/wrna.1548. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Kurosaki T, Popp MW, and Maquat LE (2019). Quality and quantity control of gene expression by nonsense-mediated mRNA decay. Nat. Rev. Mol. Cell Biol 20, 406–420. 10.1038/s41580-019-0126-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Loerch S, Maucuer A, Manceau V, Green MR, and Kielkopf CL (2014). Cancer-relevant splicing factor CAPERα engages the essential splicing factor SF3b155 in a specific ternary complex. J. Biol. Chem 289, 17325–17337. 10.1074/jbc.M114.558825. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Stepanyuk GA, Serrano P, Peralta E, Farr CL, Axelrod HL, Geralt M, Das D, Chiu HJ, Jaroszewski L, Deacon AM, et al. (2016). UHM-ULM interactions in the RBM39-U2AF65 splicing-factor complex. Acta Crystallogr. D Struct. Biol 72, 497–511. 10.1107/s2059798316001248. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Wang E, Lu SX, Pastore A, Chen X, Imig J, Chun-Wei Lee S, Hockemeyer K, Ghebrechristos YE, Yoshimi A, Inoue D, et al. (2019). Targeting an RNA-Binding Protein Network in Acute Myeloid Leukemia. Cancer Cell 35, 369–384.e7. 10.1016/j.ccell.2019.01.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Haque N, Will A, Cook AG, and Hogg JR (2023). A network of DZF proteins controls alternative splicing regulation and fidelity. Nucleic Acids Res. 51, 6411–6429. 10.1093/nar/gkad351. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Kataoka N, Bachorik JL, and Dreyfuss G (1999). Transportin-SR, a nuclear import receptor for SR proteins. J. Cell Biol 145, 1145–1152. 10.1083/jcb.145.6.1145. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Lai MC, Lin RI, and Tarn WY (2001). Transportin-SR2 mediates nuclear import of phosphorylated SR proteins. Proc. Natl. Acad. Sci. USA 98, 10154–10159. 10.1073/pnas.181354098. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Takahashi H, Takigawa I, Watanabe M, Anwar D, Shibata M, Tomomori-Sato C, Sato S, Ranjan A, Seidel CW, Tsukiyama T, et al. (2015). MED26 regulates the transcription of snRNA genes through the recruitment of little elongation complex. Nat. Commun 6, 5941. 10.1038/ncomms6941. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Guiro J, and Murphy S (2017). Regulation of expression of human RNA polymerase II-transcribed snRNA genes. Open Biol. 7, 170073. 10.1098/rsob.170073. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Han H, Braunschweig U, Gonatopoulos-Pournatzis T, Weatheritt RJ, Hirsch CL, Ha KCH, Radovani E, Nabeel-Shah S, Sterne-Weiler T, Wang J, et al. (2017). Multilayered Control of Alternative Splicing Regulatory Networks by Transcription Factors. Mol. Cell 65, 539–553.e7. 10.1016/j.molcel.2017.01.011. [DOI] [PubMed] [Google Scholar]
63.Sigova AA, Abraham BJ, Ji X, Molinie B, Hannett NM, Guo YE, Jangi M, Giallourakis CC, Sharp PA, and Young RA (2015). Transcription factor trapping by RNA in gene regulatory elements. Science 350, 978–981. 10.1126/science.aad3346. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Oksuz O, Henninger JE, Warneford-Thomson R, Zheng MM, Erb H, Vancura A, Overholt KJ, Hawken SW, Banani SF, Lauman R, et al. (2023). Transcription factors interact with RNA to regulate genes. Mol. Cell 83, 2449–2463.e13. 10.1016/j.molcel.2023.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.G Hendrickson D, Kelley DR, Tenen D, Bernstein B, and Rinn JL (2016). Widespread RNA binding by chromatin-associated proteins. Genome Biol. 17, 28. 10.1186/s13059-016-0878-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Nabeel-Shah S, Pu S, Burns JD, Braunschweig U, Ahmed N, Burke GL, Lee H, Radovani E, Zhong G, Tang H, et al. (2024). C2H2-zinc-finger transcription factors bind RNA and function in diverse post-transcriptional regulatory processes. Mol. Cell 84, 3810–3825.e10. 10.1016/j.molcel.2024.08.037. [DOI] [PubMed] [Google Scholar]
67.Jiang H, He X, Wang S, Jia J, Wan Y, Wang Y, Zeng R, Yates J 3rd, Zhu X, and Zheng Y (2014). A microtubule-associated zinc finger protein, BuGZ, regulates mitotic chromosome alignment by ensuring Bub3 stability and kinetochore targeting. Dev. Cell 28, 268–281. 10.1016/j.devcel.2013.12.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Toledo CM, Herman JA, Olsen JB, Ding Y, Corrin P, Girard EJ, Olson JM, Emili A, DeLuca JG, and Paddison PJ (2014). BuGZ is required for Bub3 stability, Bub1 kinetochore function, and chromosome alignment. Dev. Cell 28, 282–294. 10.1016/j.devcel.2013.12.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Fang F, Xia N, Angulo B, Carey J, Cady Z, Durruthy-Durruthy J, Bennett T, Sebastiano V, and Reijo Pera RA (2018). A distinct isoform of ZNF207 controls self-renewal and pluripotency of human embryonic stem cells. Nat. Commun 9, 4384. 10.1038/s41467-018-06908-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Malla S, Prasad Bhattarai D, Groza P, Melguizo-Sanchis D, Atanasoai I, Martinez-Gamero C, Román ÁC, Zhu D, Lee DF, Kutter C, et al. (2022). ZFP207 sustains pluripotency by coordinating OCT4 stability, alternative splicing and RNA export. EMBO Rep. 23, e53191. 10.15252/embr.202153191. [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Baltz AG, Munschauer M, Schwanhäusser B, Vasile A, Murakawa Y, Schueler M, Youngs N, Penfold-Brown D, Drew K, Milek M, et al. (2012). The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690. 10.1016/j.molcel.2012.05.021. [DOI] [PubMed] [Google Scholar]
72.Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, Davey NE, Humphreys DT, Preiss T, Steinmetz LM, et al. (2012). Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell 149, 1393–1406. 10.1016/j.cell.2012.04.031. [DOI] [PubMed] [Google Scholar]
73.Choi Y, Um B, Na Y, Kim J, Kim JS, and Kim VN (2024). Time-resolved profiling of RNA binding proteins throughout the mRNA life cycle. Mol. Cell 84, 1764–1782.e10. 10.1016/j.molcel.2024.03.012. [DOI] [PubMed] [Google Scholar]
74.Hegele A, Kamburov A, Grossmann A, Sourlis C, Wowro S, Weimann M, Will CL, Pena V, Lührmann R, and Stelzl U (2012). Dynamic protein-protein interaction wiring of the human spliceosome. Mol. Cell 45, 567–580. 10.1016/j.molcel.2011.12.034. [DOI] [PubMed] [Google Scholar]
75.Wan Y, Zheng X, Chen H, Guo Y, Jiang H, He X, Zhu X, and Zheng Y (2015). Splicing function of mitotic regulators links R-loop-mediated DNA damage to tumor cell killing. J. Cell Biol 209, 235–246. 10.1083/jcb.201409073. [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Cho NH, Cheveralls KC, Brunner AD, Kim K, Michaelis AC, Raghavan P, Kobayashi H, Savy L, Li JY, Canaj H, et al. (2022). OpenCell: Endogenous tagging for the cartography of human cellular organization. Science 375, eabi6983. 10.1126/science.abi6983. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Fernandez P, Scaffidi P, Markert E, Lee JH, Rane S, and Misteli T (2014). Transformation resistance in a premature aging disorder identifies a tumor-protective function of BRD4. Cell Rep. 9, 248–260. 10.1016/j.celrep.2014.08.069. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Wu D, Yates PA, Zhang H, and Cao K (2016). Comparing lamin proteins post-translational relative stability using a 2A peptide-based system reveals elevated resistance of progerin to cellular degradation. Nucleus 7, 585–596. 10.1080/19491034.2016.1260803. [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Puttaraju M, Jackson M, Klein S, Shilo A, Bennett CF, Gordon L, Rigo F, and Misteli T (2021). Systematic screening identifies therapeutic antisense oligonucleotides for Hutchinson-Gilford progeria syndrome. Nat. Med 27, 526–535. 10.1038/s41591-021-01262-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Scaffidi P, and Misteli T (2006). Lamin A-dependent nuclear defects in human aging. Science 312, 1059–1063. 10.1126/science.1127168. [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Vidak S, Kubben N, Dechat T, and Foisner R (2015). Proliferation of progeria cells is enhanced by lamina-associated polypeptide 2α (LAP2α) through expression of extracellular matrix proteins. Genes Dev. 29, 2022–2036. 10.1101/gad.263939.115. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, Gill S, Harrington WF, Pantel S, Krill-Burger JM, et al. (2017). Defining a Cancer Dependency Map. Cell 170, 564–576.e16. 10.1016/j.cell.2017.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Sahadevan S, Sekaran T, Ashaf N, Fritz M, Hentze MW, Huber W, and Schwarzl T (2015). htseq-clip: a toolset for the preprocessing of eCLIP/iCLIP datasets. Bioinformatics 39, btac747. 10.1093/bioinformatics/btac747. [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Schwarzl T, Sahadevan S, Lang B, Miladi M, Backofen R, Huber W, Hentze MW, and Tartaglia GG (2024). Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq. Nucleic Acids Res. 52, e1. 10.1093/nar/gkad998. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Beusch I, Rao B, Studer MK, Luhovska T, Šukytė V, Lei S, Oses-Prieto J, SeGraves E, Burlingame A, Jonas S, et al. (2023). Targeted high-throughput mutagenesis of the human spliceosome reveals its in vivo operating principles. Mol. Cell 83, 2578–2594.e9. 10.1016/j.molcel.2023.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Hanna RE, Hegde M, Fagre CR, DeWeirdt PC, Sangree AK, Szegletes Z, Griffith A, Feeley MN, Sanson KR, Baidi Y, et al. (2021). Massively parallel assessment of human variants with base editor screens. Cell 184, 1064–1080.e20. 10.1016/j.cell.2021.01.012. [DOI] [PubMed] [Google Scholar]
87.Cuella-Martin R, Hayward SB, Fan X, Chen X, Huang J-W, Taglialatela A, Leuzzi G, Zhao J, Rabadan R, Lu C, et al. (2021). Functional interrogation of DNA damage response variants with base editing screens. Cell 184, 1081–1097.e19. 10.1016/j.cell.2021.01.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Jiang H, Wang S, Huang Y, He X, Cui H, Zhu X, and Zheng Y (2015). Phase transition of spindle-associated protein regulate spindle apparatus assembly. Cell 163, 108–122. 10.1016/j.cell.2015.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Jin W, Brannan KW, Kapeli K, Park SS, Tan HQ, Gosztyla ML, Mujumdar M, Ahdout J, Henroid B, Rothamel K, et al. (2023). HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence. Mol. Cell 83, 2595–2611.e11. 10.1016/j.molcel.2023.06.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Evers B, Jastrzebski K, Heijmans JPM, Grernrum W, Beijersbergen RL, and Bernards R (2016). CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat. Biotechnol 34, 631–633. 10.1038/nbt.3536. [DOI] [PubMed] [Google Scholar]
91.Muller R, Meacham ZA, Ferguson L, and Ingolia NT (2020). CiBER-seq dissects genetic networks by quantitative CRISPRi profiling of expression phenotypes. Science 370, eabb9662. 10.1126/science.abb9662. [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Alford BD, Tassoni-Tsuchida E, Khan D, Work JJ, Valiant G, and Brandman O (2021). ReporterSeq reveals genome-wide dynamic modulators of the heat shock response across diverse stressors. eLife 10, e57376. 10.7554/eLife.57376. [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Kim J, Muller RY, Bondra ER, and Ingolia NT (2024). CRISPRi with barcoded expression reporters dissects regulatory networks in human cells. Preprint at bioRxiv. 10.1101/2024.09.06.611573. [DOI] [Google Scholar]
94.Lobel JH, and Ingolia NT (2025). Precise measurement of molecular phenotypes with barcode-based CRISPRi systems. Genome Biol. 26, 142. 10.1186/s13059-025-03610-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Nugent PJ, Park H, Wladyka CL, Yelland JN, Sinha S, Chen KY, Bynum C, Quarterman G, Lee SC, Hsieh AC, et al. (2025). Decoding post-transcriptional regulatory networks by RNA-linked CRISPR screening in human cells. Nat. Methods 22, 1237–1246. 10.1038/s41592-025-02702-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
96.Gonatopoulos-Pournatzis T, Wu M, Braunschweig U, Roth J, Han H, Best AJ, Raj B, Aregger M, O’Hanlon D, Ellis JD, et al. (2018). Genome-wide CRISPR-Cas9 Interrogation of Splicing Networks Reveals a Mechanism for Recognition of Autism-Misregulated Neuronal Microexons. Mol. Cell 72, 510–524.e12. 10.1016/j.molcel.2018.10.008. [DOI] [PubMed] [Google Scholar]
97.Benbarche S, Pineda JMB, Galvis LB, Biswas J, Liu B, Wang E, Zhang Q, Hogg SJ, Lyttle K, Dahi A, et al. (2024). GPATCH8 modulates mutant SF3B1 mis-splicing and pathogenicity in hematologic malignancies. Mol. Cell 84, 1886–1903.e10. 10.1016/j.molcel.2024.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
98.Scarborough AM, Flaherty JN, Hunter OV, Liu K, Kumar A, Xing C, Tu BP, and Conrad NK (2021). SAM homeostasis is regulated by CFIm-mediated splicing of MAT2A. eLife 10, e64930. 10.7554/eLife.64930. [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Han H, Best AJ, Braunschweig U, Mikolajewicz N, Li JD, Roth J, Chowdhury F, Mantica F, Nabeel-Shah S, Parada G, et al. (2022). Systematic exploration of dynamic splicing networks reveals conserved multistage regulators of neurogenesis. Mol. Cell 82, 2982–2999.e14. 10.1016/j.molcel.2022.06.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
100.Lopez-Mejia IC, Vautrot V, De Toledo M, Behm-Ansmant I, Bourgeois CF, Navarro CL, Osorio FG, Freije JMP, Stévenin J, De Sandre-Giovannoli A, et al. (2011). A conserved splicing mechanism of the LMNA gene controls premature aging. Hum. Mol. Genet 20, 4540–4555. 10.1093/hmg/ddr385. [DOI] [PubMed] [Google Scholar]
101.Zhou Y, Tong C, Shi Z, Zhang Y, Xiong X, Shen X, Li X, and Yin Y (2025). Condensation of ZFP207 and U1 snRNP promotes spliceosome assembly. Nat. Struct. Mol. Biol 32, 1038–1049. 10.1038/s41594-025-01501-z. [DOI] [PubMed] [Google Scholar]
102.Jourdain AA, Begg BE, Mick E, Shah H, Calvo SE, Skinner OS, Sharma R, Blue SM, Yeo GW, Burge CB, et al. (2021). Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS. Mol. Cell 81, 1905–1919.e12. 10.1016/j.molcel.2021.02.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
103.Daniels NJ, Hershberger CE, Gu X, Schueger C, DiPasquale WM, Brick J, Saunthararajah Y, Maciejewski JP, and Padgett RA (2021). Functional analyses of human LUC7-like proteins involved in splicing regulation and myeloid neoplasms. Cell Rep. 35, 108989. 10.1016/j.celrep.2021.108989. [DOI] [PMC free article] [PubMed] [Google Scholar]
104.Kenny CJ, McGurk MP, Schüler S, Cordero A, Laubinger S, and Burge CB (2025). LUC7 proteins define two major classes of 5′ splice sites in animals and plants. Nat. Commun 16, 1574. 10.1038/s41467-025-56577-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
105.Koblan LW, Erdos MR, Wilson C, Cabral WA, Levy JM, Xiong ZM, Tavarez UL, Davison LM, Gete YG, Mao X, et al. (2021). In vivo base editing rescues Hutchinson-Gilford progeria syndrome in mice. Nature 589, 608–614. 10.1038/s41586-020-03086-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
106.Gosztyla ML, Zhan L, Olson S, Wei X, Naritomi J, Nguyen G, Street L, Goda GA, Cavazos FF, Schmok JC, et al. (2024). Integrated multi-omics analysis of zinc-finger proteins uncovers roles in RNA regulation. Mol. Cell 84, 3826–3842.e8. 10.1016/j.molcel.2024.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
107.Moore MJ, Wang Q, Kennedy CJ, and Silver PA (2010). An alternative splicing network links cell-cycle control to apoptosis. Cell 142, 625–636. 10.1016/j.cell.2010.07.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
108.Damodaran AP, Gavard O, Gagné JP, Rogalska ME, Behera AK, Mancini E, Bertolin G, Courtheoux T, Kumari B, Cailloce J, et al. (2025). Proteomic study identifies Aurora-A-mediated regulation of alternative splicing through multiple splicing factors. J. Biol. Chem 301, 108000. 10.1016/j.jbc.2024.108000. [DOI] [PMC free article] [PubMed] [Google Scholar]
109.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589. 10.1038/s41586-021-03819-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, et al. (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444. 10.1093/nar/gkab1061. [DOI] [PMC free article] [PubMed] [Google Scholar]
111.Quinlan AR, and Hall IM (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842. 10.1093/bioinformatics/btq033. [DOI] [PMC free article] [PubMed] [Google Scholar]
112.Yu G, Wang LG, Han Y, and He QY (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287. 10.1089/omi.2011.0118. [DOI] [PMC free article] [PubMed] [Google Scholar]
113.Martin M (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10. 10.14806/ej.17.1.200. [DOI] [Google Scholar]
114.Ramírez F, Dündar F, Diehl S, Grüning BA, and Manke T (2014). deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191. 10.1093/nar/gku365. [DOI] [PMC free article] [PubMed] [Google Scholar]
115.Love MI, Huber W, and Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
116.Kolberg L, Raudvere U, Kuzmin I, Adler P, Vilo J, and Peterson H (2023). g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. 51, W207–W212. 10.1093/nar/gkad347. [DOI] [PMC free article] [PubMed] [Google Scholar]
117.Ignatiadis N, Klaus B, Zaugg JB, and Huber W (2016). Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods 13, 577–580. 10.1038/nmeth.3885. [DOI] [PMC free article] [PubMed] [Google Scholar]
118.Ewels P, Magnusson M, Lundin S, and Käller M (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048. 10.1093/bioinformatics/btw354. [DOI] [PMC free article] [PubMed] [Google Scholar]
119.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, and Durbin R; 1000 Genome Project Data Processing Subgroup (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079. 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]
120.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, and Gingeras TR (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]
121.Smith T, Heger A, and Sudbery I (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499. 10.1101/gr.209601.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
122.Sterne-Weiler T, Weatheritt RJ, Best AJ, Ha KCH, and Blencowe BJ (2018). Efficient and Accurate Quantitative Profiling of Alternative Splicing Patterns of Any Complexity on a Laptop. Mol. Cell 72, 187–200.e6. 10.1016/j.molcel.2018.08.018. [DOI] [PubMed] [Google Scholar]
123.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, and Ideker T (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]
124.Doncheva NT, Morris JH, Gorodkin J, and Jensen LJ (2019). Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J. Proteome Res 18, 623–632. 10.1021/acs.jproteome.8b00702. [DOI] [PMC free article] [PubMed] [Google Scholar]
125.Diebold ML, Fribourg S, Koch M, Metzger T, and Romier C (2011). Deciphering correct strategies for multiprotein complex assembly by coexpression: application to complexes as large as the histone octamer. J. Struct. Biol 175, 178–188. 10.1016/j.jsb.2011.02.001. [DOI] [PubMed] [Google Scholar]
126.Blue SM, Yee BA, Pratt GA, Mueller JR, Park SS, Shishkin AA, Starner AC, Van Nostrand EL, and Yeo GW (2022). Transcriptome-wide identification of RNA-binding protein binding sites using seCLIP-seq. Nat. Protoc 17, 1223–1265. 10.1038/s41596-022-00680-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Studier FW (2005). Protein production by auto-induction in high density shaking cultures. Protein Expr. Purif 41, 207–234. 10.1016/j.pep.2005.01.016. [DOI] [PubMed] [Google Scholar]
128.Sumida KH, Núñez-Franco R, Kalvet I, Pellock SJ, Wicky BIM, Milles LF, Dauparas J, Wang J, Kipnis Y, Jameson N, et al. (2024). Improving Protein Expression, Stability, and Function with ProteinMPNN. J. Am. Chem. Soc 146, 2054–2061. 10.1021/jacs.3c10941. [DOI] [PMC free article] [PubMed] [Google Scholar]
129.Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, et al. (2012). Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682. 10.1038/nmeth.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
130.Van Nostrand EL, Nguyen TB, Gelboin-Burkhart C, Wang R, Blue SM, Pratt GA, Louie AL, and Yeo GW (2017). Robust, Cost-Effective Profiling of RNA Binding Protein Targets with Single-end Enhanced Crosslinking and Immunoprecipitation (seCLIP). Methods Mol. Biol 1648, 177–200. 10.1007/978-1-4939-7204-3_14. [DOI] [PMC free article] [PubMed] [Google Scholar]
131.Perez AR, Pritykin Y, Vidigal JA, Chhangawala S, Zamparo L, Leslie CS, and Ventura A (2017). GuideScan software for improved single and paired CRISPR guide RNA design. Nat. Biotechnol 35, 347–349. 10.1038/nbt.3804. [DOI] [PMC free article] [PubMed] [Google Scholar]
132.Perez AR, Sala L, Perez RK, and Vidigal JA (2021). CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens. Nat. Commun 12, 6461. 10.1038/s41467-021-26722-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
133.Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R, et al. (2016). Optimized sgRNA design to maximize activity and minimize offtarget effects of CRISPR-Cas9. Nat. Biotechnol 34, 184–191. 10.1038/nbt.3437. [DOI] [PMC free article] [PubMed] [Google Scholar]
134.Dede M, McLaughlin M, Kim E, and Hart T (2020). Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens. Genome Biol. 21, 262. 10.1186/s13059-020-02173-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
135.Parrish PCR, Thomas JD, Gabel AM, Kamlapurkar S, Bradley RK, and Berger AH (2021). Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome. Cell Rep. 36, 109597. 10.1016/J.CELREP.2021.109597. [DOI] [PMC free article] [PubMed] [Google Scholar]
136.Thompson NA, Ranzani M, van der Weyden L, Iyer V, Offord V, Droop A, Behan F, Gonçalves E, Speak A, Iorio F, et al. (2021). Combinatorial CRISPR screen identifies fitness effects of gene paralogues. Nat. Commun 12, 1302. 10.1038/s41467-021-21478-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
137.Dang Y, Jia G, Choi J, Ma H, Anaya E, Ye C, Shankar P, and Wu H (2015). Optimizing sgRNA structure to improve CRISPR-Cas9 knockout efficiency. Genome Biol. 16, 280. 10.1186/s13059-015-0846-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
138.Merico D, Isserlin R, Stueker O, Emili A, and Bader GD (2010). Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984. 10.1371/journal.pone.0013984. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1

NIHMS2134080-supplement-Table_S1.xlsx^{(33.7MB, xlsx)}

Table S2

NIHMS2134080-supplement-Table_S2.xlsx^{(22.2KB, xlsx)}

Table S3

NIHMS2134080-supplement-Table_S3.xlsx^{(38.6MB, xlsx)}

Table S4

NIHMS2134080-supplement-Table_S4.xlsx^{(36.5MB, xlsx)}

Table S5

NIHMS2134080-supplement-Table_S5.xlsx^{(42.2MB, xlsx)}

Table S6

NIHMS2134080-supplement-Table_S6.xlsx^{(5.5MB, xlsx)}

Table S7

NIHMS2134080-supplement-Table_S7.xlsx^{(605.6KB, xlsx)}

Table S8

NIHMS2134080-supplement-Table_S8.xlsx^{(2.2MB, xlsx)}

Supplemental Figures

NIHMS2134080-supplement-Supplemental_Figures.pdf^{(17.1MB, pdf)}

Data Availability Statement

CRISPR screening, RNA-seq, and eCLIP data generated by this study have been deposited at GEO and are publicly available. Accession numbers are listed in the key resources table. The ZNF207 miniTurboID mass spectrometry data associated with this study have been deposited to the ProteomeXchange consortium through its partner MassIVE (massive.ucsd.edu). Accession numbers and DOI are listed in the key resources table. Raw data underlying the figures have been deposited in Mendeley Data under the DOI https://doi.org/10.17632/xvzfdm8kky.1.
This paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
ZNF207	Invitrogen	Cat#703747; RRID: AB_2815340
ZNF207	ThermoFisher Scientific	Cat#PA530641; RRID: AB_2548115
FLAG M2	Sigma	Cat#F3165; RRID: AB_259529
GAPDH	Proteintech	Cat#10494–1-AP; RRID: AB_2263076
SF3B1	Proteintech	Cat#27684–1-AP; RRID: AB_2880946
PRPF6	Proteintech	Cat#23929–1-AP; RRID: AB_2879365
SNRNP200	Proteintech	Cat#23875–1-AP; RRID: AB_2879346
RBM25	Proteintech	Cat#25297–1-AP; RRID: AB_2880014
SNRNP70	Abcam	Cat#ab83306; RRID: AB_10673827
SNRPA	Proteintech	Cat#10212–1-AP; RRID: AB_2239723
SNRPC	Proteintech	Cat# 22428–1-AP; RRID: AB_2918077
SNRPF	Proteintech	Cat#14977–1-AP; RRID: AB_2302166
BUB3	ThermoFisher Scientific	Cat#BDB611730; RRID: AB_2243620
HA tag	Sigma-Aldrich	Cat#H3663; RRID: AB_262051
Beta-actin	Proteintech	Cat#20536–1-AP; RRID: AB_10700003
Lamin A/C	Santa Cruz Biotechnology	Cat#sc376248; RRID: AB_10991536
LAP2 alpha	Abcam	Cat#ab5162; RRID: AB_304757
Lamin B1	Abcam	Cat#ab16048; RRID: AB_443298
Histone H3 (tri methyl K27)	Abcam	Cat#ab192985; RRID: AB_2650559
Acetyl-Histone H4 (acetyl K16)	Abcam	Cat#ab109463; RRID: AB_10858987
Anti-rabbit IgG, HRP-linked Antibody	Cell Signaling Technology	Cat#7074; RRID: AB_2099233
Anti-mouse IgG, HRP-linked Antibody	Cell Signaling Technology	Cat#7076; RRID: AB_330924
Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor^™ 594	Invitrogen	Cat#A11012; RRID: AB_2534079
Bacterial and virus strains
NEB Stable competent E. coli cells	New England Biolabs	Cat#C3040H
Endura electrocompetent cells	LGC Biosearch Technologies	Cat#60242–2
One Shot^™ BL21(DE3) Chemically Competent E. coli	ThermoFisher Scientific	Cat#C600003
Chemicals, peptides, and recombinant proteins
TRIzol	Sigma-Aldrich	Cat#T3934
Puromycin	ThermoFisher Scientific	Cat#A1113803
G418 Sulfate	Gibco	Cat#10131027
Blasticidin S	Gibco	Cat#A1113903
Hygromycin	ThermoFisher Scientific	Cat#10687010
Doxycycline	Sigma-Aldrich	Cat#D9891
Polybrene	Sigma-Aldrich	Cat#H9268
DTT (dithiothreitol)	ThermoFisher Scientific	Cat#R0861
D-Biotin	ThermoFisher Scientific	Cat#B20656
IGEPAL^® CA-630	Sigma-Aldrich	Cat#I3021
DAPI	Biotium	Cat#40043
ATP	ThermoFisher Scientific	Cat#R0441
Creatine phosphate	Sigma-Aldrich	Cat#10621714001
Polyvinyl alcohol (PVA)	ThermoFisher Scientific	Cat#041241.22
ZNF207 full-length	This study	N/A
ZNF207 K42E	This study	N/A
ZNF207 1–122 (ZnF+Hel)	This study	N/A
ZNF207 1–122 K42E (ZnF+Hel_K42E)	This study	N/A
U1–70K (SNRNP70) 1–216	This study	N/A
U1-A (SNRPA)	This study	N/A
U1-C (SNRPC)	This study	N/A
Critical commercial assays
RNeasy Plus Mini Kit	Qiagen	Cat#74136
DNA Gel Loading Dye (6X)	ThermoFisher Scientific	Cat#R0611
NEBNext Ultra II Q5 Master Mix	New England Biolabs	Cat#M0544X
Q5^® Site-Directed Mutagenesis Kit (Without Competent Cells)	New England Biolabs	Cat#E0552S
QIAquick PCR Purification Kit	Qiagen	Cat#28104
GeneJET PCR purification column	ThermoFisher Scientific	Cat#K0701
GeneJET Gel Extraction Kit	ThermoFisher Scientific	Cat#K0691
SYBR Safe	ThermoFisher Scientific	Cat#S33102
Chemiluminescent Nucleic Acid Detection Module Kit	ThermoFisher Scientific	Cat#89880
Streptavidin-Horseradish Peroxidase (HRP) Conjugate	ThermoFisher Scientific	Cat#SA10001
EasyPep^™ MS Sample Prep Kits	ThermoFisher Scientific	Cat#A57864
KAPA HiFi HotStart DNA polymerase	Roche	Cat# KK2601
D1000 ScreenTape	Agilent	Cat#5067–5582; Cat#5067–5583
High Sensitivity D1000 ScreenTape	Agilent	Cat#5067–5584
High Sensitivity D1000 Reagents	Agilent	Cat#5067–5585
RNA ScreenTape	Agilent	Cat#5067–5576
RNA ScreenTape Sample Buffer	Agilent	Cat#5067–5577
Qubit dsDNA HS assay	ThermoFisher Scientific	Cat#Q32851
PhiX	Illumina	Cat#FC-110–3001
NovaSeq^™ X Series 1.5B Reagent Kit (300 Cyc)	Illumina	Cat# 20104704
NovaSeq^™ X Series 1.5B Reagent Kit (300 Cyc)	Illumina	Cat# 20104705
Illumina Stranded mRNA Prep kit	Illumina	Cat#15031047
In-Fusion Snap Assembly Master Mix	Takara Bio	Cat#638948
NEBuilder HiFi DNA Assembly Master Mix	New England Biolabs	Cat#E2621L
X-tremeGENE 9 DNA Transfection Reagent	Sigma-Aldrich	Cat#6365809001
QIAGEN OneStep RT-PCR Kit	Qiagen	Cat#210215
SensiFAST^™ SYBR^® No-ROX Kit	Bioline	Cat#BIO-98050
Maxima H Minus First Strand cDNA Synthesis Kit	ThermoFisher Scientific	Cat#K1651
SensiFAST^™ SYBR^® No-ROX One-Step Kit	Bioline	Cat#BIO-72005
Maxi-prep plasmid purification kit	Invitrogen	Cat#K210016
EndoFree Plasmid Maxi Kit	Qiagen	Cat#12362
BP clonase II	ThermoFisher Scientific	Cat#11789020
LR clonase II	ThermoFisher Scientific	Cat#11791020
BveI	ThermoFisher Scientific	Cat#FD1744
Eco47III	ThermoFisher Scientific	Cat#FD0324
Eco32I	ThermoFisher Scientific	Cat#FD0304
KpnI	New England Biolabs	Cat#R3142L
NdeI	New England Biolabs	Cat#R0111L
BamHI	New England Biolabs	Cat#R0136L
NcoI	New England Biolabs	Cat#R0193L
FastAP	ThermoFisher Scientific	Cat#EF0651
RNase Inhibitor, Murine	New England Biolabs	Cat#M0314L
T4 RNA Ligase 1 (ssRNA Ligase)	New England Biolabs	Cat#M0204L
T4 DNA ligase	New England Biolabs	Cat#M0202
Amylose Resin	New England Biolabs	Cat#E0821S
HiScribe^® T7 High Yield RNA Synthesis Kit	New England Biolabs	Cat#E2040S
Lipofectamine RNAiMax	ThermoFisher Scientific	Cat#13778150
Lipofectamine^™ 2000 Transfection Reagent	ThermoFisher Scientific	Cat#11668027
Streptavidin Sepharose Beads	ThermoFisher Scientific	Cat#20353
Dynabeads protein G	ThermoFisher Scientific	Cat#10004D
Dynabeads^™ Oligo(dT)₂₅	ThermoFisher Scientific	Cat#61002
Proteinase-K	ThermoFisher Scientific	Cat#EO0492
mMESSAGE mMACHINE^™ T7 mRNA Kit with CleanCap^™ Reagent AG	Invitrogen	Cat#A57620
RNaseA	Invitrogen	Cat#12091021
HELA NUCLEAR EXTRACT for pre-mRNA splicing	Protein One	Cat#P0002–02
Bicinchoninic acid (BCA) assay	Pierce	Cat#23225
Bradford reagent	BioRad	Cat#5000006
NuPAGE LDS Sample Buffer (4x)	ThermoFisher Scientific	Cat# NP0007
cOmplete^™, Mini, EDTA-free Protease Inhibitor Cocktail	Roche	Cat#11836170001
cOmplete^™ Protease Inhibitor Cocktail	Roche	Cat#11836145001
Protease Inhibitor Cocktail III	Sigma-Aldrich	Cat#539134–1ML
4–12% Bis-Tris gels	Life Technologies	Cat#NP0323BOX
Immobilon-P PVDF membrane	Sigma-Aldrich	Cat#IPVH00010
Benzonase^® Nuclease	Sigma-Aldrich	Cat#E1014
SuperSignal West Pico PLUS chemiluminescence reagent	ThermoFisher Scientific	Cat#34580
SuperSignal^™ Western Blot Substrate Bundle, Femto + trial-size Atto	ThermoFisher Scientific	Cat#A45916
DMEM with high glucose and pyruvate	Gibco	Cat#11995073
heat-inactivated fetal bovine serum (HI-FBS)	Gibco	Cat#16140071
penicillin-streptomycin	Gibco	Cat#15140122
trypsin-EDTA	Gibco	Cat#25200056
Opti-MEM	Gibco	Cat#31985062
MEM, no glutamine	Gibco	Cat#11090081
L-Glutamine (200 mM)	Gibco	Cat#25030081
MEM Non-Essential Amino Acids Solution (100X)	Gibco	Cat#11140050
Sequencing Grade Modified Trypsin	Promega	Cat#V5111
PhenoPlate^™ 384-well microplates	Revvity Health Sciences	Cat#6057300
Deposited data
CRASP-Seq data	This study	GEO: GSE284628
ZNF207 RNA-Seq	This study	GEO: GSE284629, GSE284630
ZNF207 eCLIP-Seq data	This study	GEO: GSE284631
ZNF207 miniTurboID data	This study	proteomXchange: MSV000096693
Unprocessed Imaging data	This study	Mendeley Data: 10.17632/xvzfdm8kky.1
Experimental models: Cell lines
HAP1 Cas9/Cas12a	This study	N/A
HAP1 highly efficient adenine base editor (ABE8e)	Xiao et al.¹¹	N/A
HAP1 cytosine base editor (evoCDA1)	Xiao et al.¹¹	N/A
RPE1 hTERT TP53 −/− Cas9/Cas12a	This study	N/A
HEK293T	ATCC	Cat#ACS-4500
HEK293 Flp-In T-REx cells	Invitrogen	Cat#R78007
HepG2 Cas9/Cas12a	This study	N/A
hTERT-immortalized fibroblasts	Fernandez et al.⁷⁷	N/A
hTERT-immortalized fibroblasts HGPS	Fernandez et al.⁷⁷	N/A
Oligonucleotides
See Table S2	This study	N/A
Recombinant DNA
See Table S2 and STAR Methods	This study	N/A
Plasmids	This study	https://www.addgene.org/depositing/85289/
Libraries	This study	https://www.addgene.org/pooled-library/gonatopoulos-pournatzis-human-crispr-crasp-seq/
Software and algorithms
AlphaFold2 2.3.1	Jumper et al.¹⁰⁹ and Varadi et al.¹¹⁰	https://alphafold.ebi.ac.uk/download
BEDTools 2.30.0	Quinlan and Hall¹¹¹	https://bedtools.readthedocs.io/en/latest/
BLAT	UCSC Genome Browser	https://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads
clusterProfiler	Yu et al.¹¹²	https://www.bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html
cutadapt 4.4	Martin¹¹³	https://cutadapt.readthedocs.io/en/stable/
deepTools 3.5.2	Ramírez et al.¹¹⁴	https://deeptools.readthedocs.io/en/develop/
DESeq2 1.44	Love et al.¹¹⁵	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
DEWSeq 1.18.0	Schwarzl et al.⁸⁴	https://bioconductor.org/packages/release/bioc/html/DEWSeq.html
g:Profiler	Kolberg et al.¹¹⁶	https://biit.cs.ut.ee/gprofiler/gost
htseq-clip 2.19	Sahadevan et al.⁸³	https://htseq-clip.readthedocs.io/en/latest/index.html
IHW 1.32.0	Ignatiadis et al.¹¹⁷	https://bioconductor.org/packages/3.21/bioc/vignettes/IHW/inst/doc/introduction_to_ihw.html
JupyterLab Desktop 4.2.5–1	Project Jupyter	https://jupyter.org/
MultiQC 1.13	Ewels et al.¹¹⁸	https://seqera.io/multiqc/
Python 2.7.15	Python Software Foundation	https://www.python.org
Python 3.8.5	Python Software Foundation	https://www.python.org
R version 4.3.1	R Foundation	https://www.r-project.org
RStudio Desktop 2025.05.0	Posit Software, PBC	https://posit.co/
Samtools 1.19.1	Li et al.¹¹⁹	http://www.htslib.org/download/
STAR 2.7.6a	Dobin et al.¹²⁰	https://github.com/alexdobin/STAR/releases
UCSC table browser	UCSC Genome Browser	https://genome.ucsc.edu
UMI-tools 1.1.5	Smith et al.¹²¹	https://umi-tools.readthedocs.io/en/latest/index.html
Whippet 1.6.2	Sterne-Weiler et al.¹²²	https://github.com/timbitz/Whippet.jl
FlowJo software	BD Biosciences, version 10.8.1	https://www.flowjo.com/solutions/flowjo
Cytoscape v3.9.1	Shannon et al.¹²³	https://cytoscape.org/
STRING plugin v2.0.1	Doncheva et al.¹²⁴	https://apps.cytoscape.org/apps/stringapp
Partek Flow software (version 10.0.23.0531)	N/A	https://www.partek.com/partek-flow/
GraphPad Prism Version 9.0	GraphPad Software Inc.	https://www.graphpad.com/
Adobe Illustrator v28.4.1	Adobe	https://www.adobe.com/products/illustrator.html
Affinity Designer 2 v2.3.0	Affinity Designer	https://affinity.serif.com/en-us/designer/
BioRender	BioRender	https://www.biorender.com/
Other
UV Crosslinker	VWR	Cat#89131–484
4150 TapeStation System	Agilent	Cat#G2992AA
BTX Gemini Electroporator	BTX	Cat#452042
Bioruptor Plus sonicator	Diagenode	Cat#B01020002
Mini Gel Tank	Life Technologies	Cat#A25977,
Mini Blot Module	Life Technologies	Cat#B1000
VeritPro Thermal Cycler	Applied Biosystems	Cat#A48141
CFX96 Touch Real-Time PCR	BioRad	Cat#1855195
NovaSeq X Sequencing System	Illumina	N/A
EVOS^™ M5000 Imaging System	Invitrogen	Cat#AMF5000
iBright CL1500 Imaging System	Invitrogen	Cat#A44114
Vi-Cell BLU Cell Viability Analyzer	Beckman Coulter	Cat#C19196

Open in a new tab

[R1] 1.Kastner B, Will CL, Stark H, and Lührmann R (2019). Structural Insights into Nuclear pre-mRNA Splicing in Higher Eukaryotes. Cold Spring Harb. Perspect. Biol 11, a032417. 10.1101/cshperspect.a032417. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Wan R, Bai R, Zhan X, and Shi Y (2020). How Is Precursor Messenger RNA Spliced by the Spliceosome? Annu. Rev. Biochem 89, 333–358. 10.1146/annurev-biochem-013118-111024. [DOI] [PubMed] [Google Scholar]

[R3] 3.Tholen J, and Galej WP (2022). Structural studies of the spliceosome: Bridging the gaps. Curr. Opin. Struct. Biol 77, 102461. 10.1016/j.sbi.2022.102461. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Martínez-Lumbreras S, Morguet C, and Sattler M (2024). Dynamic interactions drive early spliceosome assembly. Curr. Opin. Struct. Biol 88, 102907. 10.1016/j.sbi.2024.102907. [DOI] [PubMed] [Google Scholar]

[R5] 5.Ule J, and Blencowe BJ (2019). Alternative Splicing Regulatory Networks: Functions, Mechanisms, and Evolution. Mol. Cell 76, 329–345. 10.1016/j.molcel.2019.09.017. [DOI] [PubMed] [Google Scholar]

[R6] 6.Marasco LE, and Kornblihtt AR (2023). The physiology of alternative splicing. Nat. Rev. Mol. Cell Biol 24, 242–254. 10.1038/s41580-022-00545-z. [DOI] [PubMed] [Google Scholar]

[R7] 7.Rogalska ME, Vivori C, and Valcárcel J (2023). Regulation of premRNA splicing: roles in physiology and disease, and therapeutic prospects. Nat. Rev. Genet 24, 251–269. 10.1038/s41576-022-00556-8. [DOI] [PubMed] [Google Scholar]

[R8] 8.Baralle FE, and Giudice J (2017). Alternative splicing as a regulator of development and tissue identity. Nat. Rev. Mol. Cell Biol 18, 437–451. 10.1038/nrm.2017.27. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Pan Q, Shai O, Lee LJ, Frey BJ, and Blencowe BJ (2008). Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet 40, 1413–1415. 10.1038/ng.259. [DOI] [PubMed] [Google Scholar]

[R10] 10.Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, and Burge CB (2008). Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476. 10.1038/nature07509. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Xiao M-S, Damodaran AP, Kumari B, Dickson E, Xing K, On TA, Parab N, King HE, Perez AR, Guiblet WM, et al. (2024). Genome-scale exon perturbation screens uncover exons critical for cell fitness. Mol. Cell 84, 2553–2572.e19. 10.1016/j.molcel.2024.05.024. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Fu XD, and Ares M Jr. (2014). Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet 15, 689–701. 10.1038/nrg3778. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Park JW, Parisky K, Celotto AM, Reenan RA, and Graveley BR (2004). Identification of alternative splicing regulators by RNA interference in Drosophila. Proc. Natl. Acad. Sci. USA 101, 15974–15979. 10.1073/pnas.0407004101. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Brooks AN, Duff MO, May G, Yang L, Bolisetty M, Landolin J, Wan K, Sandler J, Booth BW, Celniker SE, et al. (2015). Regulation of alternative splicing in Drosophila by 56 RNA binding proteins. Genome Res. 25, 1771–1780. 10.1101/gr.192518.115. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Tejedor JR, Papasaikas P, and Valcárcel J (2015). Genome-wide identification of Fas/CD95 alternative splicing regulators reveals links with iron homeostasis. Mol. Cell 57, 23–38. 10.1016/j.molcel.2014.10.029. [DOI] [PubMed] [Google Scholar]

[R16] 16.Papasaikas P, Tejedor JR, Vigevani L, and Valcárcel J (2015). Functional splicing network reveals extensive regulatory potential of the core spliceosomal machinery. Mol. Cell 57, 7–22. 10.1016/j.molcel.2014.10.030. [DOI] [PubMed] [Google Scholar]

[R17] 17.Rogalska ME, Mancini E, Bonnal S, Gohr A, Dunyak BM, Arecco N, Smith PG, Vaillancourt FH, and Valcárcel J (2024). Transcriptome-wide splicing network reveals specialized regulatory functions of the core spliceosome. Science 386, 551–560. 10.1126/science.adn8105. [DOI] [PubMed] [Google Scholar]

[R18] 18.Manning KS, and Cooper TA (2017). The roles of RNA processing in translating genotype to phenotype. Nat. Rev. Mol. Cell Biol 18, 102–114. 10.1038/nrm.2016.139. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Scotti MM, and Swanson MS (2016). RNA mis-splicing in disease. Nat. Rev. Genet 17, 19–32. 10.1038/nrg.2015.3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Gordon LB, Brown WT, and Collins FS (2003). Hutchinson-Gilford Progeria Syndrome. In GeneReviews^® [Internet], Adam MP, Bick S, Mirzaa GM, Pagon RA, Wallace SE, and Amemiya A, eds. (University of Washington; ), pp. 1993–2025. https://www.ncbi.nlm.nih.gov/books/NBK1121/. [PubMed] [Google Scholar]

[R21] 21.Gordon LB, Rothman FG, López-Otín C, and Misteli T (2014). Progeria: a paradigm for translational medicine. Cell 156, 400–407. 10.1016/j.cell.2013.12.028. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Bonnal SC, López-Oreja I, and Valcárcel J (2020). Roles and mechanisms of alternative splicing in cancer - implications for care. Nat. Rev. Clin. Oncol 17, 457–474. 10.1038/s41571-020-0350-x. [DOI] [PubMed] [Google Scholar]

[R23] 23.Bradley RK, and Anczuków O (2023). RNA splicing dysregulation and the hallmarks of cancer. Nat. Rev. Cancer 23, 135–155. 10.1038/s41568-022-00541-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Kahles A, Lehmann KV, Toussaint NC, Hüser M, Stark SG, Sachsenberg T, Stegle O, Kohlbacher O, Sander C; Cancer; Genome; Atlas; Research Network, and et al. (2018). Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients. Cancer Cell 34, 211–224.e6. 10.1016/j.ccell.2018.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.PCAWG Transcriptome; Core Group, Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, and et al. (2020). Genomic basis for RNA alterations in cancer. Nature 578, 129–136. 10.1038/s41586-020-1970-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Yoshimi A, Lin KT, Wiseman DH, Rahman MA, Pastore A, Wang B, Lee SCW, Micol JB, Zhang XJ, de Botton S, et al. (2019). Coordinated alterations in RNA splicing and epigenetic regulation drive leukaemogenesis. Nature 574, 273–277. 10.1038/s41586-019-1618-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Jung H, Lee D, Lee J, Park D, Kim YJ, Park WY, Hong D, Park PJ, and Lee E (2015). Intron retention is a widespread mechanism of tumor-suppressor inactivation. Nat. Genet 47, 1242–1248. 10.1038/ng.3414. [DOI] [PubMed] [Google Scholar]

[R28] 28.David CJ, Chen M, Assanah M, Canoll P, and Manley JL (2010). HnRNP proteins controlled by c-Myc deregulate pyruvate kinase mRNA splicing in cancer. Nature 463, 364–368. 10.1038/nature08697. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Wang Z, Jeon HY, Rigo F, Bennett CF, and Krainer AR (2012). Manipulation of PK-M mutually exclusive alternative splicing by antisense oligonucleotides. Open Biol. 2, 120133. 10.1098/rsob.120133. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Paronetto MP, Bernardis I, Volpe E, Bechara E, Sebestyén E, Eyras E, and Valcárcel J (2014). Regulation of FAS exon definition and apoptosis by the Ewing sarcoma protein. Cell Rep. 7, 1211–1226. 10.1016/j.celrep.2014.03.077. [DOI] [PubMed] [Google Scholar]

[R31] 31.Chen K, Xiao H, Zeng J, Yu G, Zhou H, Huang C, Yao W, Xiao W, Hu J, Guan W, et al. (2017). Alternative Splicing of EZH2 pre-mRNA by SF3B3 Contributes to the Tumorigenic Potential of Renal Cancer. Clin. Cancer Res 23, 3428–3441. 10.1158/1078-0432.Ccr-16-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Thomas JD, Polaski JT, Feng Q, De Neef EJ, Hoppe ER, McSharry MV, Pangallo J, Gabel AM, Belleville AE, Watson J, et al. (2020). RNA isoform screens uncover the essentiality and tumor-suppressor activity of ultraconserved poison exons. Nat. Genet 52, 84–94. 10.1038/s41588-019-0555-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Gonatopoulos-Pournatzis T, Aregger M, Brown KR, Farhangmehr S, Braunschweig U, Ward HN, Ha KCH, Weiss A, Billmann M, Durbic T, et al. (2020). Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9–Cas12a platform. Nat. Biotechnol 38, 638–648. 10.1038/s41587-020-0437-z. [DOI] [PubMed] [Google Scholar]

[R34] 34.Aregger M, Xing K, and Gonatopoulos-Pournatzis T (2021). Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening. Nat. Protoc 16, 4722–4765. 10.1038/s41596-021-00595-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Ward HN, Aregger M, Gonatopoulos-Pournatzis T, Billmann M, Ohsumi TK, Brown KR, Blencowe BJ, Moffat J, and Myers CL (2021). Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline. Nat. Protoc 16, 4766–4798. 10.1038/s41596-021-00596-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Zetsche B, Heidenreich M, Mohanraju P, Fedorova I, Kneppers J, DeGennaro EM, Winblad N, Choudhury SR, Abudayyeh OO, Gootenberg JS, et al. (2016). Multiplex gene editing by CRISPR–Cpf1 using a single crRNA array. Nat. Biotechnol 35, 31–34. 10.1038/nbt.3737. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Fonfara I, Richter H, Bratovič M, Le Rhun A, and Charpentier E (2016). The CRISPR-associated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR RNA. Nature 532, 517–521. 10.1038/nature17945. [DOI] [PubMed] [Google Scholar]

[R38] 38.Mu W, Starmer J, Yee D, and Magnuson T (2018). EZH2 variants differentially regulate polycomb repressive complex 2 in histone methylation and cell differentiation. Epigenetics Chromatin 11, 71. 10.1186/s13072-018-0242-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Gier RA, Budinich KA, Evitt NH, Cao Z, Freilich ES, Chen Q, Qi J, Lan Y, Kohli RM, and Shi J (2020). High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening. Nat. Commun 11, 3455. 10.1038/s41467-020-17209-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Braun SE, Shi X, Qiu G, Wong FE, Joshi PJ, Prasad VR, and Johnson RP (2007). Instability of retroviral vectors with HIV-1-specific RT aptamers due to cryptic splice sites in the U6 promoter. AIDS Res. Ther 4, 24. 10.1186/1742-6405-4-24. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Hoek TA, Khuperkar D, Lindeboom RGH, Sonneveld S, Verhagen BMP, Boersma S, Vermeulen M, and Tanenbaum ME (2019). Single-Molecule Imaging Uncovers Rules Governing Nonsense-Mediated mRNA Decay. Mol. Cell 75, 324–339.e11. 10.1016/j.molcel.2019.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Le Hir H, Gatfield D, Izaurralde E, and Moore MJ (2001). The exonexon junction complex provides a binding platform for factors involved in mRNA export and nonsense-mediated mRNA decay. EMBO J. 20, 4987–4997. 10.1093/emboj/20.17.4987. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R43] 43.Diem MD, Chan CC, Younis I, and Dreyfuss G (2007). PYM binds the cytoplasmic exon-junction complex and ribosomes to enhance translation of spliced mRNAs. Nat. Struct. Mol. Biol 14, 1173–1179. 10.1038/nsmb1321. [DOI] [PubMed] [Google Scholar]

[R44] 44.Chazal PE, Daguenet E, Wendling C, Ulryck N, Tomasetto C, Sargueil B, and Le Hir H (2013). EJC core component MLN51 interacts with eIF3 and activates translation. Proc. Natl. Acad. Sci. USA 110, 5903–5908. 10.1073/pnas.1218732110. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Chen M, David CJ, and Manley JL (2012). Concentration-dependent control of pyruvate kinase M mutually exclusive splicing by hnRNP proteins. Nat. Struct. Mol. Biol 19, 346–354. 10.1038/nsmb.2219. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Wang Z, Chatterjee D, Jeon HY, Akerman M, Vander Heiden MG, Cantley LC, and Krainer AR (2012). Exon-centric regulation of pyruvate kinase M alternative splicing via mutually exclusive exons. J. Mol. Cell Biol 4, 79–87. 10.1093/jmcb/mjr030. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Königs V, de Oliveira Freitas Machado C, Arnold B, Blümel N, Solovyeva A, Löbbert S, Schafranek M, Ruiz De Los Mozos I, Wittig I, McNicoll F, et al. (2020). SRSF7 maintains its homeostasis through the expression of Split-ORFs and nuclear body assembly. Nat. Struct. Mol. Biol 27, 260–273. 10.1038/s41594-020-0385-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Lareau LF, Inada M, Green RE, Wengrod JC, and Brenner SE (2007). Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature 446, 926–929. 10.1038/nature05676. [DOI] [PubMed] [Google Scholar]

[R49] 49.Scaffidi P, and Misteli T (2005). Reversal of the cellular phenotype in the premature aging disease Hutchinson-Gilford progeria syndrome. Nat. Med 11, 440–445. 10.1038/nm1204. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Leclair NK, Brugiolo M, Urbanski L, Lawson SC, Thakar K, Yurieva M, George J, Hinson JT, Cheng A, Graveley BR, et al. (2020). Poison Exon Splicing Regulates a Coordinated Network of SR Protein Expression during Differentiation and Tumorigenesis. Mol. Cell 80, 648–665.e9. 10.1016/j.molcel.2020.10.019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 51.Karousis ED, and Mühlemann O (2019). Nonsense-Mediated mRNA Decay Begins Where Translation Ends. Cold Spring Harb. Perspect. Biol 11, a032862. 10.1101/cshperspect.a032862. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 52.Kishor A, Fritz SE, and Hogg JR (2019). Nonsense-mediated mRNA decay: The challenge of telling right from wrong in a complex transcriptome. Wiley Interdiscip. Rev. RNA 10, e1548. 10.1002/wrna.1548. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Kurosaki T, Popp MW, and Maquat LE (2019). Quality and quantity control of gene expression by nonsense-mediated mRNA decay. Nat. Rev. Mol. Cell Biol 20, 406–420. 10.1038/s41580-019-0126-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R54] 54.Loerch S, Maucuer A, Manceau V, Green MR, and Kielkopf CL (2014). Cancer-relevant splicing factor CAPERα engages the essential splicing factor SF3b155 in a specific ternary complex. J. Biol. Chem 289, 17325–17337. 10.1074/jbc.M114.558825. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Stepanyuk GA, Serrano P, Peralta E, Farr CL, Axelrod HL, Geralt M, Das D, Chiu HJ, Jaroszewski L, Deacon AM, et al. (2016). UHM-ULM interactions in the RBM39-U2AF65 splicing-factor complex. Acta Crystallogr. D Struct. Biol 72, 497–511. 10.1107/s2059798316001248. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Wang E, Lu SX, Pastore A, Chen X, Imig J, Chun-Wei Lee S, Hockemeyer K, Ghebrechristos YE, Yoshimi A, Inoue D, et al. (2019). Targeting an RNA-Binding Protein Network in Acute Myeloid Leukemia. Cancer Cell 35, 369–384.e7. 10.1016/j.ccell.2019.01.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 57.Haque N, Will A, Cook AG, and Hogg JR (2023). A network of DZF proteins controls alternative splicing regulation and fidelity. Nucleic Acids Res. 51, 6411–6429. 10.1093/nar/gkad351. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Kataoka N, Bachorik JL, and Dreyfuss G (1999). Transportin-SR, a nuclear import receptor for SR proteins. J. Cell Biol 145, 1145–1152. 10.1083/jcb.145.6.1145. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R59] 59.Lai MC, Lin RI, and Tarn WY (2001). Transportin-SR2 mediates nuclear import of phosphorylated SR proteins. Proc. Natl. Acad. Sci. USA 98, 10154–10159. 10.1073/pnas.181354098. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R60] 60.Takahashi H, Takigawa I, Watanabe M, Anwar D, Shibata M, Tomomori-Sato C, Sato S, Ranjan A, Seidel CW, Tsukiyama T, et al. (2015). MED26 regulates the transcription of snRNA genes through the recruitment of little elongation complex. Nat. Commun 6, 5941. 10.1038/ncomms6941. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.Guiro J, and Murphy S (2017). Regulation of expression of human RNA polymerase II-transcribed snRNA genes. Open Biol. 7, 170073. 10.1098/rsob.170073. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.Han H, Braunschweig U, Gonatopoulos-Pournatzis T, Weatheritt RJ, Hirsch CL, Ha KCH, Radovani E, Nabeel-Shah S, Sterne-Weiler T, Wang J, et al. (2017). Multilayered Control of Alternative Splicing Regulatory Networks by Transcription Factors. Mol. Cell 65, 539–553.e7. 10.1016/j.molcel.2017.01.011. [DOI] [PubMed] [Google Scholar]

[R63] 63.Sigova AA, Abraham BJ, Ji X, Molinie B, Hannett NM, Guo YE, Jangi M, Giallourakis CC, Sharp PA, and Young RA (2015). Transcription factor trapping by RNA in gene regulatory elements. Science 350, 978–981. 10.1126/science.aad3346. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Oksuz O, Henninger JE, Warneford-Thomson R, Zheng MM, Erb H, Vancura A, Overholt KJ, Hawken SW, Banani SF, Lauman R, et al. (2023). Transcription factors interact with RNA to regulate genes. Mol. Cell 83, 2449–2463.e13. 10.1016/j.molcel.2023.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.G Hendrickson D, Kelley DR, Tenen D, Bernstein B, and Rinn JL (2016). Widespread RNA binding by chromatin-associated proteins. Genome Biol. 17, 28. 10.1186/s13059-016-0878-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R66] 66.Nabeel-Shah S, Pu S, Burns JD, Braunschweig U, Ahmed N, Burke GL, Lee H, Radovani E, Zhong G, Tang H, et al. (2024). C2H2-zinc-finger transcription factors bind RNA and function in diverse post-transcriptional regulatory processes. Mol. Cell 84, 3810–3825.e10. 10.1016/j.molcel.2024.08.037. [DOI] [PubMed] [Google Scholar]

[R67] 67.Jiang H, He X, Wang S, Jia J, Wan Y, Wang Y, Zeng R, Yates J 3rd, Zhu X, and Zheng Y (2014). A microtubule-associated zinc finger protein, BuGZ, regulates mitotic chromosome alignment by ensuring Bub3 stability and kinetochore targeting. Dev. Cell 28, 268–281. 10.1016/j.devcel.2013.12.013. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R68] 68.Toledo CM, Herman JA, Olsen JB, Ding Y, Corrin P, Girard EJ, Olson JM, Emili A, DeLuca JG, and Paddison PJ (2014). BuGZ is required for Bub3 stability, Bub1 kinetochore function, and chromosome alignment. Dev. Cell 28, 282–294. 10.1016/j.devcel.2013.12.014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R69] 69.Fang F, Xia N, Angulo B, Carey J, Cady Z, Durruthy-Durruthy J, Bennett T, Sebastiano V, and Reijo Pera RA (2018). A distinct isoform of ZNF207 controls self-renewal and pluripotency of human embryonic stem cells. Nat. Commun 9, 4384. 10.1038/s41467-018-06908-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R70] 70.Malla S, Prasad Bhattarai D, Groza P, Melguizo-Sanchis D, Atanasoai I, Martinez-Gamero C, Román ÁC, Zhu D, Lee DF, Kutter C, et al. (2022). ZFP207 sustains pluripotency by coordinating OCT4 stability, alternative splicing and RNA export. EMBO Rep. 23, e53191. 10.15252/embr.202153191. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R71] 71.Baltz AG, Munschauer M, Schwanhäusser B, Vasile A, Murakawa Y, Schueler M, Youngs N, Penfold-Brown D, Drew K, Milek M, et al. (2012). The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690. 10.1016/j.molcel.2012.05.021. [DOI] [PubMed] [Google Scholar]

[R72] 72.Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, Davey NE, Humphreys DT, Preiss T, Steinmetz LM, et al. (2012). Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell 149, 1393–1406. 10.1016/j.cell.2012.04.031. [DOI] [PubMed] [Google Scholar]

[R73] 73.Choi Y, Um B, Na Y, Kim J, Kim JS, and Kim VN (2024). Time-resolved profiling of RNA binding proteins throughout the mRNA life cycle. Mol. Cell 84, 1764–1782.e10. 10.1016/j.molcel.2024.03.012. [DOI] [PubMed] [Google Scholar]

[R74] 74.Hegele A, Kamburov A, Grossmann A, Sourlis C, Wowro S, Weimann M, Will CL, Pena V, Lührmann R, and Stelzl U (2012). Dynamic protein-protein interaction wiring of the human spliceosome. Mol. Cell 45, 567–580. 10.1016/j.molcel.2011.12.034. [DOI] [PubMed] [Google Scholar]

[R75] 75.Wan Y, Zheng X, Chen H, Guo Y, Jiang H, He X, Zhu X, and Zheng Y (2015). Splicing function of mitotic regulators links R-loop-mediated DNA damage to tumor cell killing. J. Cell Biol 209, 235–246. 10.1083/jcb.201409073. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R76] 76.Cho NH, Cheveralls KC, Brunner AD, Kim K, Michaelis AC, Raghavan P, Kobayashi H, Savy L, Li JY, Canaj H, et al. (2022). OpenCell: Endogenous tagging for the cartography of human cellular organization. Science 375, eabi6983. 10.1126/science.abi6983. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R77] 77.Fernandez P, Scaffidi P, Markert E, Lee JH, Rane S, and Misteli T (2014). Transformation resistance in a premature aging disorder identifies a tumor-protective function of BRD4. Cell Rep. 9, 248–260. 10.1016/j.celrep.2014.08.069. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R78] 78.Wu D, Yates PA, Zhang H, and Cao K (2016). Comparing lamin proteins post-translational relative stability using a 2A peptide-based system reveals elevated resistance of progerin to cellular degradation. Nucleus 7, 585–596. 10.1080/19491034.2016.1260803. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R79] 79.Puttaraju M, Jackson M, Klein S, Shilo A, Bennett CF, Gordon L, Rigo F, and Misteli T (2021). Systematic screening identifies therapeutic antisense oligonucleotides for Hutchinson-Gilford progeria syndrome. Nat. Med 27, 526–535. 10.1038/s41591-021-01262-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R80] 80.Scaffidi P, and Misteli T (2006). Lamin A-dependent nuclear defects in human aging. Science 312, 1059–1063. 10.1126/science.1127168. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R81] 81.Vidak S, Kubben N, Dechat T, and Foisner R (2015). Proliferation of progeria cells is enhanced by lamina-associated polypeptide 2α (LAP2α) through expression of extracellular matrix proteins. Genes Dev. 29, 2022–2036. 10.1101/gad.263939.115. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R82] 82.Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, Gill S, Harrington WF, Pantel S, Krill-Burger JM, et al. (2017). Defining a Cancer Dependency Map. Cell 170, 564–576.e16. 10.1016/j.cell.2017.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R83] 83.Sahadevan S, Sekaran T, Ashaf N, Fritz M, Hentze MW, Huber W, and Schwarzl T (2015). htseq-clip: a toolset for the preprocessing of eCLIP/iCLIP datasets. Bioinformatics 39, btac747. 10.1093/bioinformatics/btac747. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R84] 84.Schwarzl T, Sahadevan S, Lang B, Miladi M, Backofen R, Huber W, Hentze MW, and Tartaglia GG (2024). Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq. Nucleic Acids Res. 52, e1. 10.1093/nar/gkad998. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R85] 85.Beusch I, Rao B, Studer MK, Luhovska T, Šukytė V, Lei S, Oses-Prieto J, SeGraves E, Burlingame A, Jonas S, et al. (2023). Targeted high-throughput mutagenesis of the human spliceosome reveals its in vivo operating principles. Mol. Cell 83, 2578–2594.e9. 10.1016/j.molcel.2023.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R86] 86.Hanna RE, Hegde M, Fagre CR, DeWeirdt PC, Sangree AK, Szegletes Z, Griffith A, Feeley MN, Sanson KR, Baidi Y, et al. (2021). Massively parallel assessment of human variants with base editor screens. Cell 184, 1064–1080.e20. 10.1016/j.cell.2021.01.012. [DOI] [PubMed] [Google Scholar]

[R87] 87.Cuella-Martin R, Hayward SB, Fan X, Chen X, Huang J-W, Taglialatela A, Leuzzi G, Zhao J, Rabadan R, Lu C, et al. (2021). Functional interrogation of DNA damage response variants with base editing screens. Cell 184, 1081–1097.e19. 10.1016/j.cell.2021.01.041. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R88] 88.Jiang H, Wang S, Huang Y, He X, Cui H, Zhu X, and Zheng Y (2015). Phase transition of spindle-associated protein regulate spindle apparatus assembly. Cell 163, 108–122. 10.1016/j.cell.2015.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R89] 89.Jin W, Brannan KW, Kapeli K, Park SS, Tan HQ, Gosztyla ML, Mujumdar M, Ahdout J, Henroid B, Rothamel K, et al. (2023). HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence. Mol. Cell 83, 2595–2611.e11. 10.1016/j.molcel.2023.06.019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R90] 90.Evers B, Jastrzebski K, Heijmans JPM, Grernrum W, Beijersbergen RL, and Bernards R (2016). CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat. Biotechnol 34, 631–633. 10.1038/nbt.3536. [DOI] [PubMed] [Google Scholar]

[R91] 91.Muller R, Meacham ZA, Ferguson L, and Ingolia NT (2020). CiBER-seq dissects genetic networks by quantitative CRISPRi profiling of expression phenotypes. Science 370, eabb9662. 10.1126/science.abb9662. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R92] 92.Alford BD, Tassoni-Tsuchida E, Khan D, Work JJ, Valiant G, and Brandman O (2021). ReporterSeq reveals genome-wide dynamic modulators of the heat shock response across diverse stressors. eLife 10, e57376. 10.7554/eLife.57376. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R93] 93.Kim J, Muller RY, Bondra ER, and Ingolia NT (2024). CRISPRi with barcoded expression reporters dissects regulatory networks in human cells. Preprint at bioRxiv. 10.1101/2024.09.06.611573. [DOI] [Google Scholar]

[R94] 94.Lobel JH, and Ingolia NT (2025). Precise measurement of molecular phenotypes with barcode-based CRISPRi systems. Genome Biol. 26, 142. 10.1186/s13059-025-03610-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R95] 95.Nugent PJ, Park H, Wladyka CL, Yelland JN, Sinha S, Chen KY, Bynum C, Quarterman G, Lee SC, Hsieh AC, et al. (2025). Decoding post-transcriptional regulatory networks by RNA-linked CRISPR screening in human cells. Nat. Methods 22, 1237–1246. 10.1038/s41592-025-02702-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R96] 96.Gonatopoulos-Pournatzis T, Wu M, Braunschweig U, Roth J, Han H, Best AJ, Raj B, Aregger M, O’Hanlon D, Ellis JD, et al. (2018). Genome-wide CRISPR-Cas9 Interrogation of Splicing Networks Reveals a Mechanism for Recognition of Autism-Misregulated Neuronal Microexons. Mol. Cell 72, 510–524.e12. 10.1016/j.molcel.2018.10.008. [DOI] [PubMed] [Google Scholar]

[R97] 97.Benbarche S, Pineda JMB, Galvis LB, Biswas J, Liu B, Wang E, Zhang Q, Hogg SJ, Lyttle K, Dahi A, et al. (2024). GPATCH8 modulates mutant SF3B1 mis-splicing and pathogenicity in hematologic malignancies. Mol. Cell 84, 1886–1903.e10. 10.1016/j.molcel.2024.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R98] 98.Scarborough AM, Flaherty JN, Hunter OV, Liu K, Kumar A, Xing C, Tu BP, and Conrad NK (2021). SAM homeostasis is regulated by CFIm-mediated splicing of MAT2A. eLife 10, e64930. 10.7554/eLife.64930. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R99] 99.Han H, Best AJ, Braunschweig U, Mikolajewicz N, Li JD, Roth J, Chowdhury F, Mantica F, Nabeel-Shah S, Parada G, et al. (2022). Systematic exploration of dynamic splicing networks reveals conserved multistage regulators of neurogenesis. Mol. Cell 82, 2982–2999.e14. 10.1016/j.molcel.2022.06.036. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R100] 100.Lopez-Mejia IC, Vautrot V, De Toledo M, Behm-Ansmant I, Bourgeois CF, Navarro CL, Osorio FG, Freije JMP, Stévenin J, De Sandre-Giovannoli A, et al. (2011). A conserved splicing mechanism of the LMNA gene controls premature aging. Hum. Mol. Genet 20, 4540–4555. 10.1093/hmg/ddr385. [DOI] [PubMed] [Google Scholar]

[R101] 101.Zhou Y, Tong C, Shi Z, Zhang Y, Xiong X, Shen X, Li X, and Yin Y (2025). Condensation of ZFP207 and U1 snRNP promotes spliceosome assembly. Nat. Struct. Mol. Biol 32, 1038–1049. 10.1038/s41594-025-01501-z. [DOI] [PubMed] [Google Scholar]

[R102] 102.Jourdain AA, Begg BE, Mick E, Shah H, Calvo SE, Skinner OS, Sharma R, Blue SM, Yeo GW, Burge CB, et al. (2021). Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS. Mol. Cell 81, 1905–1919.e12. 10.1016/j.molcel.2021.02.033. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R103] 103.Daniels NJ, Hershberger CE, Gu X, Schueger C, DiPasquale WM, Brick J, Saunthararajah Y, Maciejewski JP, and Padgett RA (2021). Functional analyses of human LUC7-like proteins involved in splicing regulation and myeloid neoplasms. Cell Rep. 35, 108989. 10.1016/j.celrep.2021.108989. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R104] 104.Kenny CJ, McGurk MP, Schüler S, Cordero A, Laubinger S, and Burge CB (2025). LUC7 proteins define two major classes of 5′ splice sites in animals and plants. Nat. Commun 16, 1574. 10.1038/s41467-025-56577-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R105] 105.Koblan LW, Erdos MR, Wilson C, Cabral WA, Levy JM, Xiong ZM, Tavarez UL, Davison LM, Gete YG, Mao X, et al. (2021). In vivo base editing rescues Hutchinson-Gilford progeria syndrome in mice. Nature 589, 608–614. 10.1038/s41586-020-03086-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R106] 106.Gosztyla ML, Zhan L, Olson S, Wei X, Naritomi J, Nguyen G, Street L, Goda GA, Cavazos FF, Schmok JC, et al. (2024). Integrated multi-omics analysis of zinc-finger proteins uncovers roles in RNA regulation. Mol. Cell 84, 3826–3842.e8. 10.1016/j.molcel.2024.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R107] 107.Moore MJ, Wang Q, Kennedy CJ, and Silver PA (2010). An alternative splicing network links cell-cycle control to apoptosis. Cell 142, 625–636. 10.1016/j.cell.2010.07.019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R108] 108.Damodaran AP, Gavard O, Gagné JP, Rogalska ME, Behera AK, Mancini E, Bertolin G, Courtheoux T, Kumari B, Cailloce J, et al. (2025). Proteomic study identifies Aurora-A-mediated regulation of alternative splicing through multiple splicing factors. J. Biol. Chem 301, 108000. 10.1016/j.jbc.2024.108000. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R109] 109.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589. 10.1038/s41586-021-03819-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R110] 110.Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, et al. (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444. 10.1093/nar/gkab1061. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R111] 111.Quinlan AR, and Hall IM (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842. 10.1093/bioinformatics/btq033. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R112] 112.Yu G, Wang LG, Han Y, and He QY (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287. 10.1089/omi.2011.0118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R113] 113.Martin M (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10. 10.14806/ej.17.1.200. [DOI] [Google Scholar]

[R114] 114.Ramírez F, Dündar F, Diehl S, Grüning BA, and Manke T (2014). deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191. 10.1093/nar/gku365. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R115] 115.Love MI, Huber W, and Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R116] 116.Kolberg L, Raudvere U, Kuzmin I, Adler P, Vilo J, and Peterson H (2023). g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. 51, W207–W212. 10.1093/nar/gkad347. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R117] 117.Ignatiadis N, Klaus B, Zaugg JB, and Huber W (2016). Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods 13, 577–580. 10.1038/nmeth.3885. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R118] 118.Ewels P, Magnusson M, Lundin S, and Käller M (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048. 10.1093/bioinformatics/btw354. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R119] 119.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, and Durbin R; 1000 Genome Project Data Processing Subgroup (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079. 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R120] 120.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, and Gingeras TR (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R121] 121.Smith T, Heger A, and Sudbery I (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499. 10.1101/gr.209601.116. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R122] 122.Sterne-Weiler T, Weatheritt RJ, Best AJ, Ha KCH, and Blencowe BJ (2018). Efficient and Accurate Quantitative Profiling of Alternative Splicing Patterns of Any Complexity on a Laptop. Mol. Cell 72, 187–200.e6. 10.1016/j.molcel.2018.08.018. [DOI] [PubMed] [Google Scholar]

[R123] 123.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, and Ideker T (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R124] 124.Doncheva NT, Morris JH, Gorodkin J, and Jensen LJ (2019). Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J. Proteome Res 18, 623–632. 10.1021/acs.jproteome.8b00702. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R125] 125.Diebold ML, Fribourg S, Koch M, Metzger T, and Romier C (2011). Deciphering correct strategies for multiprotein complex assembly by coexpression: application to complexes as large as the histone octamer. J. Struct. Biol 175, 178–188. 10.1016/j.jsb.2011.02.001. [DOI] [PubMed] [Google Scholar]

[R126] 126.Blue SM, Yee BA, Pratt GA, Mueller JR, Park SS, Shishkin AA, Starner AC, Van Nostrand EL, and Yeo GW (2022). Transcriptome-wide identification of RNA-binding protein binding sites using seCLIP-seq. Nat. Protoc 17, 1223–1265. 10.1038/s41596-022-00680-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R127] 127.Studier FW (2005). Protein production by auto-induction in high density shaking cultures. Protein Expr. Purif 41, 207–234. 10.1016/j.pep.2005.01.016. [DOI] [PubMed] [Google Scholar]

[R128] 128.Sumida KH, Núñez-Franco R, Kalvet I, Pellock SJ, Wicky BIM, Milles LF, Dauparas J, Wang J, Kipnis Y, Jameson N, et al. (2024). Improving Protein Expression, Stability, and Function with ProteinMPNN. J. Am. Chem. Soc 146, 2054–2061. 10.1021/jacs.3c10941. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R129] 129.Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, et al. (2012). Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682. 10.1038/nmeth.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R130] 130.Van Nostrand EL, Nguyen TB, Gelboin-Burkhart C, Wang R, Blue SM, Pratt GA, Louie AL, and Yeo GW (2017). Robust, Cost-Effective Profiling of RNA Binding Protein Targets with Single-end Enhanced Crosslinking and Immunoprecipitation (seCLIP). Methods Mol. Biol 1648, 177–200. 10.1007/978-1-4939-7204-3_14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R131] 131.Perez AR, Pritykin Y, Vidigal JA, Chhangawala S, Zamparo L, Leslie CS, and Ventura A (2017). GuideScan software for improved single and paired CRISPR guide RNA design. Nat. Biotechnol 35, 347–349. 10.1038/nbt.3804. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R132] 132.Perez AR, Sala L, Perez RK, and Vidigal JA (2021). CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens. Nat. Commun 12, 6461. 10.1038/s41467-021-26722-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R133] 133.Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R, et al. (2016). Optimized sgRNA design to maximize activity and minimize offtarget effects of CRISPR-Cas9. Nat. Biotechnol 34, 184–191. 10.1038/nbt.3437. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R134] 134.Dede M, McLaughlin M, Kim E, and Hart T (2020). Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens. Genome Biol. 21, 262. 10.1186/s13059-020-02173-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R135] 135.Parrish PCR, Thomas JD, Gabel AM, Kamlapurkar S, Bradley RK, and Berger AH (2021). Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome. Cell Rep. 36, 109597. 10.1016/J.CELREP.2021.109597. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R136] 136.Thompson NA, Ranzani M, van der Weyden L, Iyer V, Offord V, Droop A, Behan F, Gonçalves E, Speak A, Iorio F, et al. (2021). Combinatorial CRISPR screen identifies fitness effects of gene paralogues. Nat. Commun 12, 1302. 10.1038/s41467-021-21478-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R137] 137.Dang Y, Jia G, Choi J, Ma H, Anaya E, Ye C, Shankar P, and Wu H (2015). Optimizing sgRNA structure to improve CRISPR-Cas9 knockout efficiency. Genome Biol. 16, 280. 10.1186/s13059-015-0846-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R138] 138.Merico D, Isserlin R, Stueker O, Emili A, and Bader GD (2010). Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984. 10.1371/journal.pone.0013984. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

RNA-coupled CRISPR screens reveal ZNF207 as a regulator of LMNA aberrant splicing in progeria

Amit K Behera

Jeongjin J Kim

Shreya Kordale

Filip Pekovic

Arun Prasath Damodaran

Bandana Kumari

Sandra Vidak

Ethan Dickson

Mei-Sheng Xiao

Gerard Duncan

Thorkell Andresson

Tom Misteli

Eugene Valkov

Thomas Gonatopoulos-Pournatzis

SUMMARY

Graphical abstract

In brief

INTRODUCTION

RESULTS

Establishing a robust screening platform for quantifying splicing regulation

Figure 1. Overview of the CRASP-seq platform for identifying alternative splicing regulators.

Application of CRASP-seq to disease-relevant splicing events

Figure 2. Genome-wide identification of disease-associated splicing regulators.

ZNF207 regulates LMNA aberrant splicing in progeria

Figure 3. CRASP-seq identifies ZNF207 as a regulator of progerin aberrant splicing.

ZNF207 depletion results in widespread alternative splicing changes

Figure 4. ZNF207 depletion induces widespread alternative splicing changes.

ZNF207 binds regulated exons and associates with the splicing machinery

Figure 5. ZNF207 interacts with the splicing machinery and binds to regulated splicing targets.

CRASP-seq coupled to high-throughput mutagenesis identifies key regions of ZNF207 for splicing regulation

Figure 6. ZNF207 N terminus is necessary and largely sufficient for splicing regulation.

ZNF207 engages in critical interactions with U1 snRNP

Figure 7. ZNF207 regulates splicing via U1 snRNP interactions.

DISCUSSION

Limitations of the study

RESOURCE AVAILABILITY

Lead contact

Materials availability

Data and code availability

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Cell culture

Cell line generation

METHOD DETAILS

Cloning CRASP-Seq lentiviral vectors

Cloning base editor library

Lentivirus production

CRASP-Seq screening protocol

Cloning ZNF207

RT-PCR assays

Immunoblotting

Co-immunoprecipitation

Quantification of U1 snRNA co-immunoprecipitated with ZNF207

ZNF207 rescue experiments

In vivo RNA crosslink assays

RT-qPCR validations

miniTurboID mass spectrometry

Immunofluorescence staining

Recombinant protein production and purification

Preparation of U1 snRNA

Electrophoretic mobility shift assay (EMSA)

Pull-down assays

In vitro splicing

eCLIP sequencing and analysis

Design of genome-wide knock-out library

Design base editor library

QUANTIFICATION AND STATISTICAL ANALYSIS

Analysis of CRASP-Seq data

Gene expression and splicing analysis from RNA-sequencing data

Gene ontology enrichment analysis

High-throughput image acquisition, quantification, and statistical analysis

Supplementary Material

Highlights.

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials