DIS3 mutations enhance AID-driven translocations during B-cell activation, promoting transformation to multiple myeloma

Domain: biology
Published: 2026-03-14
Editorial outcome: accept

Abstract
DIS3, a key nuclear RNA-degrading enzyme, is essential for immunoglobulin class switch recombination (CSR), promoting activation-induced cytidine deaminase (AID) activity on both DNA strands to induce double-strand DNA breaks. During somatic hypermutation, AID-dependent lesions predominantly occur on the non-template DNA strand. Dominant mutations impairing DIS3 exoribonucleolytic activity are common in multiple myeloma (MM), but their role in carcinogenesis remains unclear. Here we show, using a knock-in mouse model that, the clinically relevant DIS3 G766R variant causes chromosomal translocations in B-cells, characterised by aberrant AID activity signatures. The mice develop pristine-induced plasmacytomas, modeling early stage MM. In clinical MM samples, DIS3 mutations correlate with IGH translocations and AID-driven lesions in driver genes. Mechanistically, mutated DIS3 accumulates on chromatin-bound RNA, particularly at aberrant AID target sites, promoting mutations on both DNA strands. This results in increased AID-dependent double-strand DNA breaks, fostering microhomology-mediated oncogenic rearrangements. Translocations occur specifically during CSR, which remains functionally intact. The DIS3 G766 R mutation does not disrupt chromatin architecture in activated B cells but exploits spatial proximity to permanently juxtapose enhancers and proto-oncogenes, facilitating transformation. Thus, gain-of-function DIS3 mutations enhance AID promiscuity, driving IGH translocations and MM development without broadly affecting B-cell physiology.

Introduction
DIS3 is a critical gene for the function of every cell, which at the same time is frequently mutated in multiple myeloma (MM)1–3. It encodes a catalytic subunit of the eukaryotic nuclear exosome complex, a ribonuclease involved in the processing of stable RNA species, such as ribosomal RNA (rRNA) and degradation of cryptic transcription products, including promoter upstream transcripts (PROMPTs) and enhancer RNA (eRNA)4–6. Most prominently, DIS3 degrades products of spurious RNA polymerase II activity: low-level, unannotated, cryptic transcription that can account for up to 70% of the genome6. This activity links the nuclear exosome to the regulation of super-enhancer activity, in part by affecting chromatin topology7.

The specific role of the nuclear exosome, particularly DIS3, in B-cells is associated with immunoglobulin diversification via class switch recombination (CSR) and somatic hypermutation (SHM)8–10, processes involving a large number of trans-acting factors. During maturation of B cells, activation-induced cytidine deaminase (AID) deaminates cytosine residues in DNA, leading to the production of uracil and generating G:U mismatches that recruit DNA repair machinery. Uracil bases are then excised by the repair enzyme uracil-DNA glycosylase, resulting in abasic sites. This is followed by the cleavage of the DNA backbone by apurinic endonuclease, which creates single-strand breaks, that can be filled in by error-prone DNA polymerases, resulting in point mutations or indels (in SHM). Mismatches on both DNA strands lead to staggered double-stranded DNA breaks, repaired by classical non-homologous end joining (c-NHEJ) or microhomology mediated alternative end joining pathway (a-EJ), resulting in large deletions that constitute class switch recombination11,12. Specific DNA targeting mechanisms ensure that AID activity is largely restricted to the immunoglobulin heavy chain locus (IGH) where SHM and CSR take place13,14. It involves transcription and rapid RNA degradation15. Target regions are generally GC-rich, contain tandemly repeated DGYW DNA sequence motifs, have the potential to form G-quadruplexes, and are prone to RNA polymerase stalling and to RNA-DNA hybrids (R-loops) formation16–22. In vitro, AID predominantly targets the non-template strand during transcription. In vivo, AID mutates both DNA strands, necessitating a special mechanism involving the DIS3/exosome to displace the nascent RNA9,23. The full mechanistic details are not yet known. Nevertheless, tight regulation of AID is critical, as its off-target activity is well-known to cause genetic lesions leading to B-cell lineage cancers24–28. Interestingly, super-enhancer regions in B-cells, which possess most of the features required by AID, are critical sites for AID recruitment and serve as hotspots for AID-mediated genomic instability29.

The Igh locus forms a topologically associating domain (TAD), which tightly regulates both the transcriptional activity of regions undergoing switch recombination and promotes deletional CSR in cis. In naïve B-cells, loop extrusion juxtaposes the 3' Igh regulatory region (3' RR) enhancers with the Sμ region. B-cell activation, transcription and the loading of the cohesin complex result in the generation of dynamic subdomains that directionally align the donor Sμ region with one of the downstream S regions for deletional CSR30,31. Upon DIS3 KO, the chromosome architecture is abrogated, and CSR is lost8, adding another layer of complexity to the DIS3 function in B-cells.

MM is the second most prevalent hematological malignancy among adults. It originates during the terminal differentiation of B lymphocytes into plasma cells, which reside in the bone marrow and produce massive amounts of antibodies32,33. MM cells, like plasma cells, have already undergone all genome editing steps associated with the maturation of activated B lymphocytes, including CSR and SHM20,34. MM is classified based on its genetic lesions into hyperdiploid (HRD), distinguished by trisomies of several chromosomes, and non-hyperdiploid (non-HRD), characterized by chromosomal translocations32,35. Many translocations in MM involve the IGH locus, leading to the overexpression of oncogenes such as FGFR3, MMSET (NSD2), CCND3, CCND1, MYC, and MAF2,3,32,35,36. As murine pristane-induced peritoneal plasmacytoma (PCT) models the occurrence of translocations driving neoplastic transformation as observed in MM37. In the BALB/c strain of mice, these chromosomal alterations typically originate at the Igh locus, directly or indirectly leading to the upregulation of the Myc locus38–40. These events are strictly dependent on the promiscuous activity of AID24,27. Furthermore, clinical data clearly indicate that AID-induced point mutations in a subset of MM driver genes are crucial for the development of human MM41.

10 to 20% of MM cases harbor somatic point mutations in the DIS3 gene. These mutations are specific to non-HRD MM, and, notably, DIS3 is the only RNA exosome subunit associated with cancer2,42,43. Exosome dysfunction typically disrupts post-transcriptional regulation of gene expression, leading to reduced cell proliferation and increased cell death5,44–47 At the same time, exosome depletion or broader dysfunctions in nuclear RNA 3' end processing4,48–56 often exhibit mutagenic potential in various models. In this context, it was recently shown that oncogenic mutations in one of the cyclin-dependent protein kinases (CDK13) impair the function of cofactors of the nuclear RNA exosome, specifically ZC3H18 and SKIV2L2, resulting in deficiencies in nuclear RNA surveillance and RNA exosome activity57. Notably, MM-associated DIS3 mutations are very specific to the protein regions responsible for its exoribonucleolytic activity. These mutations inhibit or, in the case of recurrent mutations (D479, D488, and R780), completely abolish the exoribonucleolytic activity of this enzyme, exerting dominant-negative effects58. Furthermore, these mutations arise early in MM progression42,59, presumably driving the disease2,3,60. However, they are counter-selected in later stages of MM due to their inhibitory effects on cell proliferation58.

Analyses of clinical samples and MM cell lines did not reveal the exact oncogenic mechanism of DIS3 gene mutation. Conflicting conclusions were drawn on whether this mechanism arises as a consequence of transcriptome deregulation43 or not58. Studies in cellular models, which suggested a generalized genome destabilization effect, primarily used DIS3 knockdown (KD) models and cell lines outside the B-cell lineage, failing to replicate the fact that only point mutations, rather than complete knockdowns, have been observed in MM patients61. Our attempts to generate mouse lines with recurrent MM DIS3 alleles failed because of strong dominant negative effects leading to early embryonic lethality58. Therefore, to study the mechanistic implications of DIS3 dysfunctions in the development of B cell tumors, we decided to focus on a milder, yet clinically relevant DIS3 variant with glycine 766 mutated to arginine (DIS3G766R)3. DIS3G766R is predicted to be highly pathogenic (Supplementary Fig. S1a), and previous experimental validation revealed that on a protein level, it leads to DIS3 protein stalling on structured RNA substrates5,62. Still, single-stranded RNA can be degraded5,62.

Herein, we generate a DIS3G766R knock-in mouse model to further our understanding of the effect of clinical DIS3 variant on B-cell development, antibody maturation, and DIS3-mediated carcinogenesis in MM. Our analysis, supported by genomic studies of clinical samples, allows us to propose a model whereby the DIS3 mutations lead to aberrant accumulation of DIS3 at sites of AID activity. The presence of DIS3 at these sites drives oncogenic chromosomal translocations. In contrast to DIS3 loss-of-function mutations, however, MM-associated DIS3 variants do not abrogate CSR or affect genome architecture.

Results
Heterozygous MM-associated Dis3G766R/+ mutation leads to subtle deregulation of non-coding regions of the genome.

To study the role of DIS3G766R in a native genomic context, we introduced the G766R in the mouse Dis3 locus in the C57BL/B background (Supplementary Fig. S1b). Similar to the complete Dis3 knockout, the homozygous Dis3G766R mutation is embryonically lethal (Supplementary Fig. S1c) in agreement with the pathogenic character of this clinically observed DIS3 mutation. At the same time, heterozygous Dis3G766R animals did not exhibit developmental phenotypes nor hematologic abnormalities (Supplementary Fig. S1d,e).

As a next step, we profiled the transcriptomes and proteomes of in vitro activated WT and Dis3G766R/+ B-cells isolated from mice. Consistent with the lack of developmental phenotypes, the effect of Dis3G766R/+ on mRNA levels was very subtle, contrasting the massive gene expression change and activation of genes related to DNA damage response upon DIS3 inactivation5,58,61,62. We found 16 differentially expressed genes (DEGs) in naïve, 3 DEGs in B cells upon 3 days of activation, and no DEG B-cells activated for 5 days (Fig. 1a, Supplementary Fig. S2a). Furthermore, not a single transcript was upregulated over 2-fold. We also analyzed transcriptional responses accompanying differentiation after in vitro activation of B-cells and observed no significant effect of heterozygous Dis3G766R mutation (Fig. 1b, Supplementary Fig. S2b). Accordingly, there were no significant changes at the proteome level of Dis3G766R/+ and Dis3+/+ primary B-cells activated for 3 days in vitro (Fig. 1c).

Given the limited effect of Dis3G766R/+ on the steady-state transcriptome we aimed to test whether RNA/DNA hybrids accumulate in exosome-deficient cells, potentially destabilizing the genome. For this reason, we used DNA/RNA hybrids immunoprecipitation coupled with high-throughput RNA-sequencing (DRIP-seq). Comparing the DRIP-seq over the exons of the annotated transcriptome of Dis3G766R/+ and Dis3+/+ B-cells, we scored virtually no effect of the mutation (Fig. 1d).

Major DIS3 substrates represent non-protein coding RNA species with massive (up to 100-fold) accumulation of PROMPTs at the steady state of DIS3 dysfunctional cells6. In the case of Dis3G766R/+ B-cells compared to WT, no accumulation was detected when looking at individual ncRNA species. However, global analysis of the RNA-seq signal centered over active transcriptional start sites (TSS) revealed focal enrichments in regions corresponding to RNA Pol II promoter-proximal pausing and upstream regions from which PROMPTs originate (Fig. 1e and Supplementary Fig. S2d,e). Notably, in Dis3G766R/+ B-cells, DRIP-seq analysis revealed a much more prominent accumulation of R-loops in the promoter-proximal pausing regions and over PROMPTs (Fig. 1f). Although R-loops engaged RNA represent only a fraction of DIS3 substrates, their highly transient nature and the sensitivity of DRIP-seq allow for their effective detection. This indicates a transient yet detectable impact on the transcriptome, consistent with DIS3’s known role. Similar transcriptomic effects were also observed for embryonic fibroblasts (MEFs) (Supplementary Fig. S2f,g).

Interestingly, the subtle effects of the Dis3G766R/+ on the transcriptome have some impact on the physiology of mice. In contrast to the heterozygous Dis3 knockout mice, which do not differ in size from WT, Dis3G766R/+ animals were smaller than their WT littermates (Fig. 1g, Supplementary Fig. S3a-d). This reduced size corresponds to a negative impact on cell proliferation both in activated B-cells and MEFs (Fig. 1h, Supplementary Fig. S3e,f), aligning with a conserved phenotype of DIS3 dysfunction, which includes reduced mitotic progression and increased cell death.5,44–47

We conclude that primary B cells do not show any detectable defects on the annotated protein-coding transcriptome or proteome. Instead, the molecular phenotype is limited to unstable R-loop forming on non-protein coding RNA species vulnerable to the biochemically mild heterozygous DIS3G766R variant.

MM DIS3 variants do not affect class switch recombination and genome architecture in B-cells

The nuclear exosome has been demonstrated to be involved in CSR. Both the dysfunction of the nuclear exosome complex9 and Dis3 KO8 abrogate CSR. Therefore, we wanted to analyze the effect of DIS3G766R/+ mutation on CSR using in vitro-activated primary mouse B-cells. As revealed by cytometric analysis, on average 20% of cells have undergone the switch to the IgG1 heavy chain class at day 3 post-activation in both genotypes, proving that the heterozygous DIS3G766R mutation does not affect CSR (Fig. 2a, Supplementary Fig. S3g). Likewise, the expression of various immunoglobulin heavy- and light-chain classes at days 3 and 5 post-activation was comparable between the two genotypes (Supplementary Fig. S3h). Furthermore, immunization of mice with two anti-COVID19 vaccines (mRNA and protein-based) shows unaltered production of specific α-Spike IgG1 antibodies, proving unaffected IgM to the isotype IgG CSR as well as SHM (Fig. 2b). The lack of a CSR defect in the mouse model is consistent with data from clinical samples: our analysis of the CoMMpass multiple myeloma genomic dataset revealed no disruption of CSR in patients with either somatic or germline DIS3 mutations (Fig. 2c).

To look for potentially more subtle differences between DIS3G766R/+ and WT mice, we analyzed splenic germinal center (GC) B cells using flow cytometry. Analysis demonstrated a significant expansion of splenic germinal center (GC) B cells (CD95+ GL7+) upon immunization compared with naïve controls (Fig. 2d). However, the magnitude of the GC response was indistinguishable between genotypes (Fig. 2d, Supplementary Fig. S4a–b), and the distribution of B cells within the dark zone (DZ, CXCR4high CD86low) and light zone (LZ, CXCR4low CD86high) remained comparable (Fig. 2e), resulting in similar DZ:LZ ratios, which on average approached 2 (Supplementary Fig. S4c), indicating that both the quantitative and qualitative features of the GC reaction are preserved in Dis3+/G766R mice.

These results together demonstrate both the proper functioning of humoral response in Dis3G766R/+ mice in vivo as well as unaffected CSR in MM patients with a DIS3 mutation.

As a next step in DIS3G766R/+ model characterization, we wanted to assess the structure of the Igh locus chromatin architecture, which plays a crucial role in CSR. As we wanted to enhance our power to detect loops, we employed MicroC, a high-throughput micrococcal nuclease (MNase)-based chromosome conformation capture technique63. MicroC libraries were generated using activated splenic primary B-cells, with two independent replicates representing individuals for each genotype. We sequenced a total of ~1.2 billion (around 600 million reads per condition) MicroC reads. As expected, we observed a plaid pattern of intrachromosomal contacts in both wild-type and mutant B cells. The overall interactomes were similar between conditions; we detected 20,603 and 20,279 loops in the wild-type and heterozygous mutant B cells, respectively (Supplementary Data S1 and S2). Our analysis confirmed the presence of the previously described loops anchored by 3’RR/Eµ and Sγ1/Sµ, in Dis3G766R/+ and Dis3+/+ (Fig. 2f-i). Loop anchors intersected ChIP-seq peaks of CTCF and Rad2164 (Fig. 2h-i). Moreover, the normalized MicroC signal representing DNA interactions between the 3′RR and the Eµ (blue rectangle, Fig. 2f) did not show statistically significant differences between Dis3G766R/+ and Dis3+/+ B-cells (Fig. 2g). These results contrast with the previously reported observation in Dis3 KO B-cells, which showed abolished interactions8.

Taken together, we demonstrate that Dis3G766R/+ B cells, despite exhibiting a mild phenotype primarily related to non-coding transcription, display no overt changes in physiology. This includes maintaining exosome function, which is critical for germinal center B-cell differentiation, antibody maturation, CSR, SHM, and preserving the chromatin architecture of the Igh locus.

MM-specific Dis3G766R mutation drives murine plasmacytoma

To check the potential general predisposition of Dis3G766R/+ mice to develop cancer, 33 individuals of Dis3G766R/+ and 30 WT mice were aged under standard conditions for over two years. We observed no significant effect of the MM-associated Dis3G766R/+ allele on survival (Fig. 3a), nor any signs of accelerated cancer development. To test for an increased spontaneous transformation, we have also cultured MEF cells of both genotypes according to the 3T3 protocol, which models early cancer development and genetic stability65. Dis3G766R/+ MEFs did not show an increased frequency of spontaneous immortalization (Supplementary Fig. S5a), arguing against a generalized tendency toward cancerous transformation in cells with MM-associated DIS3 point mutations.

Since DIS3 mutations are specific to MM and not commonly found in other types of cancer, we decided to specifically investigate the development and progression of malignancies associated with a B-cell lineage. We employed a well-known in vivo model using pristane, an adjuvant that induces chronic inflammation, injection into the peritoneal cavity24,27, which leads to the development of plasmacytoma in susceptible mouse strains66–68. Both Dis3G766R/+ and WT C57BL/6 mice exhibited similar immune responses to the pristane injection, indicated by the incidence of diffuse alveolar hemorrhage (Fig. 3b). Despite the natural resistance of the C57BL/6 strain to plasmacytoma, the Dis3G766R/+ mice developed the disease, with a rate significantly higher than WT controls (risk ratio of 7.19; p = 0.0073; Fig. 3c). Plasmacytoma cases displayed characteristic symptoms with atypical plasma cells in an ascites fluid (Supplementary Fig. S5b-d). Notably, Dis3G766R/+ mice developed the condition not only more frequently and rapidly but also accumulated more ascite fluid than WT (Supplementary Fig. S5e-h).

As plasmacytoma is known to depend on aberrant AID-dependent translocations during CSR24,27, we wanted to investigate the frequency of translocations in Dis3G766R/+ and Dis3+/+ mice. DNA was isolated from the plasmacytomas, and deep sequencing was performed. The analysis of mate-pair and whole-genome sequencing data revealed that plasmacytomas from Dis3G766R/+ mice had significantly higher rates of chromosomal translocations involving Igh locus (Fig. 3d, Supplementary Data S3- S6). In addition, more chimeric reads originating from two different genomic locations were identified in Dis3G766R/+ (Fig. 3e), indicating increased genome structural instability. Because plasmacytomas are very rare in C57BL/6 mice and largely dependent on the Dis3G766R/+ genotype, we additionally backcrossed Dis3G766R/+ mice to the plasmacytoma-prone BALB/c background, where plasmacytomas develop more frequently, providing sufficient WT control material for comparison. Notably, the difference in translocation-indicating reads frequency between Dis3G766R/+ and WT genotypes was observed in both mouse strains (Fig. 3e).

These results imply that the oncogenic effect of the Dis3G766R/+ variant is exerted through the increased mutator effect driving translocations, particularly from the Igh locus, which agrees with the prevalence of DIS3 mutation in non-HDR MM.

Dis3-dependent translocations in mouse plasmacytomas have AID target characteristics

Considering that transient transcription and its degradation of the Igh switch locus is essential for AID-dependent recombination7,9,17, we aimed to characterize translocations in Dis3G766R/+ plasmacytomas. A meta-analysis of genomic regions surrounding chromosomal breaks detected in plasmacytomas revealed striking enrichment of AID occupancy determined previously by chromatin immunoprecipitation (ChIP-seq) (Fig. 4a). Further analysis showed that both sites of genomic structural variants (SV) and regions of AID activity are GC-rich (Fig. 4b), enriched with AID-specific motifs (Fig. 4c), and are in close proximity to regions predicted to form G-quadruplexes (Fig. 4d). RNA-seq read coverage over SV regions indicates enrichment of steady-state RNA levels over the random genome (Fig. 4e-f, Supplementary Fig. S6). Most of them showed low-level pervasive transcription known to be predominantly cleared by DIS3. When transcribed (Fig. 4g), regions of translocations are known to be prone to RNA polymerase stalling and R-loop formation. Indeed, regions involved in structural chromosomal aberrations in our plasmacytoma samples show a level of DRIP-seq accumulation (Fig. 4h-j, Supplementary Fig. S6a-e) like AID off-target loci (Fig. 4h)69. Moreover, Dis3G766R/+ B-cells accumulate and display an asymmetric distribution of RNA/DNA hybrids with a 5’ to 3’ slop on both DNA strands (Fig. 4H). Since DIS3G766R protein is less efficiently degrading structured RNA5,6, the observed asymmetry could reflect the staling of mutant DIS3 along its processive 3’ to 5’ activity. Analogously, the switch regions in the Igh locus show a roughly 25% increase in Dis3G766R/+ DRIP-seq signal (Fig. 4i). This can be explained by the fact that Dis3 mutants, including the MM Dis3G766R variant, are expected to have difficulties processing G-quadruplexes enriched in translocating regions (Fig. 4d) and representing very stable RNA secondary structures. Similarly, the TSS regions of MM driver genes, known as AID targets69, have a significantly stronger DRIP-seq signal compared to the average of all active genes in B lymphocytes (Supplementary Fig. S6f). Consequently, these regions in plasmacytoma are expected to be susceptible to AID-dependent DNA damage18,70. A snapshot of the immunoglobulin heavy chain, constant fragments genomic region (the best studied in the context of AID activity) exemplifies all the correlations mentioned above (Fig. 4k).

We conclude that chromosomal translocations caused by MM-associated DIS3G766R mutation display AID target characteristics.

Mutational profiles revealed signatures of promiscuous AID activity and a prevalence of translocations in patients with MM DIS3 alleles

Having established that specifically in B-cell lineage MM DIS3 alleles lead to increased genome instability, our objective was to test this finding against clinical data from the CoMMpass MM study, comparing patients with and without DIS3 mutations. Patients were categorized based on the type of Dis3 mutation, distinguishing between the recurrent mutations (D479, D488, and R780), other DIS3 variants, and WT DIS3. These groups consisted of 22 patients with recurrent mutations, 48 patients with other variants, and 993 patients with WT DIS3, respectively.

First, analysis of translocations involving IGH locus revealed their significantly higher occurrence in patients with DIS3 mutations (64.3% of non-recurrent DIS3 mutated patients, 64.5 % of recurrent DIS3 mutated patients, and 33.2 in the rest of the patients) (Fig. 5a-b, Supplementary Fig. S7a-b), agreeing with the specificity of DIS3 mutations for non-HD MM and the role of DIS3 in AID-dependent translocations. Then, to characterize the effect of MM DIS3 alleles on point mutations, we analyzed 96 COSMIC (Catalogue of Somatic Mutations in Cancer) mutational classes of single-base substitution (SBS) profiles71, estimating the contribution of each of them (Fig. 5c-g, Supplementary Fig. S7c,d). We identified age-associated, exome-wide SBS5 as the primary mutational signature in patients of all DIS3 genotypes (Fig. 5c, Supplementary Fig. S7c), in agreement with the late onset of MM and previous analysis41,72,73. Similarly, MM-specific mutations detected in DIS3 in CoMMpass study patients and mutations reported in the COSMIC database show a predominance of age-associated SBS5 (Fig. 5d). Despite the exosome complex involvement in SHM, only a small fraction of mutations across the exome could be attributed to mutation signatures associated with AID activity across all the DIS3 genotypes (Fig. 5c), specifically SBS84 (reflecting canonical AID according to the most recent classification) and SBS85 (concordant with indirect effects of AID activity)74.

To be more specific, we next analyzed transcribed PROMPT regions toward which DIS3 is targeted. These indeed displayed distinct somatic mutation patterns compared to the entire exome. PROMPTs featured a significantly higher proportion of substitutions that can be attributed to DNA damage, especially SBS3, associated with the erroneous repair of double-strand DNA breaks in a BRCA-dependent mechanism (Fig. 5e). This, in principle, could be explained by the reported DIS3-dependent increase in genome instability accompanying RNA:DNA hybrid accumulation61. However, despite PROMPTs being the most prominent DIS3 substrates, the somatic MM DIS3 alleles do not affect their AID-dependent mutagenesis (Fig. 5e). Moreover, the contribution of AID-dependent signatures in all transcribed PROMPT regions was limited (Fig. 5e).

To narrow down the search for the effect of DIS3 mutation even more, we analyzed known AID targets, which also include a subset of PROMPTs. There, the contribution of AID-dependent signatures is significant and higher in patients with MM DIS3 alleles, especially the recurrent ones (Fig. 5f). Further analysis of genomic regions encompassing known AID-dependent MM driver genes69 where mutations are under positive selection pressure during the carcinogenesis revealed a very strong overrepresentation of signatures related to both AID-dependent mutagenesis and defects in the repair of DNA lesions typical for CSR and SHM (namely: BER SBS10b, SBS30, and MMR SBS6, SBS26) (Fig. 5f-g). Gratifyingly, patients with MM-specific DIS3 variants exhibit a drastic increase in the contribution of these signatures, especially SBS26 (35% in recurrent mutated DIS3 patients, 23% in non-recurrent mutated DIS3 patients, and 15% in the rest of the patients; Fig. 5g). These results strongly support the positive effect of MM DIS3 variants on AID-dependent mutagenesis.

All analyses presented above indicate that although MM-associated DIS3 alleles do not alter the mutational profile genome-wide, they enhance AID-related mutagenesis occurring within a very limited time window of naive and memory B-cell activation rounds. Furthermore, these mutations are positively selected in very specific loci during MM development.

Mutant DIS3 stalling on chromatin-bound substrates, resulting in increased AID-dependent dsDNA breaks in activated B-cells

Given the correlation between MM DIS3 alleles with AID-dependent mutations and translocations in both patients’ samples and the murine model, we next wanted to gain insights into the mechanism, which would have to be related to DIS3 association to chromatin.

We found the immunoprecipitation of DIS3 on chromatin to be extremely challenging, possibly due to the indirect and transient nature of the DIS3 interaction with chromatin. Thus, to experimentally verify the direct involvement of MM DIS3 protein variants, we analyzed chromatin occupancy of the G766R mutant and WT DIS3 protein using the DamID methodology. This approach is based on fusing a protein of interest to DNA adenine methyltransferase (Dam), which modifies adjacent adenines in DNA, providing sufficient sensitivity to detect even weak interactions between DNA and its associated factors75, which in the case of DIS3, will represent both RNAs engaged in R-loops and RNAs more loosly associated with the chromatin. We modified the established mouse B-cell lymphoma CH12F3-2A cell line capable of cytokine-induced Igh CSR. Even before analyzing the sequencing data, we noticed 2.5-fold higher library yields from cells that express DIS3G766R compared with WT DIS3 (Fig. 6a). Such an increase can be explained by a higher chromatin occupancy of the mutated protein and agrees with the previously observed stalling of DIS3 mutant on RNA substrates6.

DIS3 has a very broad pattern of association with chromatin. However, PROMPT regions, which give rise to the most potent DIS3 substrates (Fig. 6b), and sites of translocations mapped in plasmacytomas show significant enrichment for DIS3 of both genotypes, coinciding with native transcription (Fig. 6b,c). Notably, known AID off-target genes are among the most highly DIS3-occupied loci in the genome (Fig. 6b). Moreover, DIS3 occupies the vicinity of switch regions in the Igh locus (Supplementary Fig. S7e), agreeing with the role of DIS3 in CSR. The data presented above indicate that DIS3 acts together with AID to drive translocations. To support cooperation with an orthogonal methodology, we have analyzed interactions between DIS3 and AID using Proximity ligation assay (PLA) (Fig. 6d-e). Indeed, in CH12F3-2A cells, we see PLA-positive spots, the number of which is higher upon in vitro activation (Fig. 6e and Supplementary Fig. S8a).

To further explore causality, we examined DNA damage accompanying B-cell activation in vitro using γ-H2AX, an established marker of dsDNA breaks76. Naïve B-cells present a low level of DNA damage, which, however, increased significantly in cells 3 days post-activation (Fig. 6f,g). In contrast to naïve, activated B-cells demonstrated a moderate, yet statistically significant, increase of DNA damage in Dis3G766R/+ compared to Dis3+/+ (Fig. 6f,g), further supporting the role of MM DIS3 alleles in B-cell specific cancerogenesis. To directly test whether the observed increase in DNA damage depends on AID activity, we employed the CH12F3-2A cell line in both AID-proficient (AID+/+) and AID-deficient (AID–/–) backgrounds. DIS3WT or DIS3G766R were expressed via lentiviral transduction, followed by γ-H2AX dsDNA breaks measurements (Fig. 6h, Supplementary Fig. S8b). Consistent with our primary B-cell data, upon CH12F3-2A cells activation, DIS3G766R expression led to a statistically significant increase in DNA damage compared to DIS3 WT in AID+/+ cells (Fig. 6h). Importantly, this effect was strictly AID-dependent, as γ-H2AX levels were highly elevated in DIS3G766R expressing AID+/+ cells but not in their AID–/– counterparts. This demonstrates that AID activity is required for the accumulation of DNA damage in the presence of mutant DIS3.

Finally, as AID preferentially targets the coding DNA strand during transcription77, and displacement of nascent RNA by the nuclear exosome is crucial for AID activity on the template strand9, we analyzed cytidine somatic mutations in plasmacytoma cells from Dis3+/+ and Dis3G766R/+ mutant mice, comparing their orientation to strand-specific RNA-seq data from B-cells at various activation stages. In DIS3 WT cells, mutations showed a clear strand bias consistent with canonical AID activity (Fig. 6i, Supplementary Data S7-S8). This asymmetry was notably reduced in Dis3G766R/+ cells, indicating that the DIS3 mutation disrupts AID strand specificity and suggesting that mutant DIS3 stalling increases AID access to the template strand. These findings strongly support the model in which mutant DIS3 stalling increases AID access to the template strand, resulting in a more symmetrical mutation pattern. When processed by DNA repair pathways, such symmetry increases the likelihood of staggered double-strand breaks—an essential step for both CSR and oncogenic chromosomal translocations translocations9,78. What is more, nucleotide-resolution mapping of breakpoints revealed microhomology-mediated repair in both genotypes, consistent with AID-dependent B-cell neoplasms and MM79,80, with Dis3G766R/+ cells (Supplementary Fig. S8c) exhibiting modestly longer microhomologies (7 bp vs. 5 bp in WT), suggestive of increased reliance on alternative end joining.

All these results imply that the oncogenic effect of DIS3 mutations is exerted through the direct impact of the mutant protein in cooperation with AID on DNA integrity, promoting staggered double-strand breaks, fostering oncogenic chromosomal rearrangements.

Dis3G766R allele does not affect the chromatin architecture but hijacks it for joining enhancers with proto-oncogenes

DIS3 and the RNA exosome complex were postulated to be essential for suppressing eRNAs from super-enhancers17 and PROMPTs at active TSSs81. We therefore tested whether translocations in plasmacytomas correlate with these regulatory regions. Using publicly available ChIP-seq data from B-cells 3 days post-activation64, we defined active enhancers and promoters as regions significantly enriched for H3K4me1 and H3K4me3, respectively (FDR < 0.01), overlapping with H3K27ac peaks. Translocating loci were localized in vicinity to either enhancer-specific H3K4me1 enrichments or active promoter-specific H3K4me3 ChIP-seq peaks with a median distance of 33kb or 25kb for loci detected in WT and Dis3G766R/+ plasmacytomas, respectively (Fig. 7a and Supplementary Fig. S9a-c). The annotated chromosomal breaks also exhibited a non-random distribution with respect to their proximity to p300 ChIP-seq peaks (Fig. 7b and Supplementary Fig. S9c) a marker of active enhancers as well as super enhancers (Supplementary Fig. S9b).

To further investigate the relationship between chromatin architecture and translocations, we studied TADs by analyzing positions of CTCF and Rad21 ChIP-seq peaks identified in B-cells 3 days post-activation64 in relation to the translocations found by us in plasmacytomas. Indeed, translocating loci were localized significantly closer to CTCF and Rad21 ChIP-seq peaks than expected from a random distribution in the genome (Fig. 7c,d, and Supplementary Fig. S9d). We detected a median distance of translocating loci from the CTCF peak to be 10.1kb or 8.6kb, and from the Rad21 peak to be 10kb or 8.5kb, in WT and Dis3G766R/+ plasmacytomas, respectively.

Since, in contrast to Dis3 ablation, the Dis3G766R allele does not interfere with the chromatin architecture of the Igh locus, we closely examined MicroC data for translocations originating in the Igh locus. Translocations identified in the plasmacytomas are in regions with strong interaction, indicating that they are in close physical proximity within the three-dimensional nuclear space (Fig. 7e and Supplementary Fig. S10). Similarly to the Igh locus, these interactions do not depend on the Dis3 variant (Fig. 7e and Supplementary Fig. S10).

In sum, we demonstrated that although the Dis3 mutant does not alter chromatin architecture, it increases the frequency of translocations between genomic regions involved in transcription during B-cell activation. This leads to a permanent joining of active promoters and enhancers in developing B cells.

Methods
All procedures were approved by the First Local Ethical Committee in Warsaw affiliated at the University of Warsaw, Faculty of Biology (approval numbers WAW/092/2016, WAW/177/2016, WAW/642/2018). Housing in animal facilities was performed in conformity with local and European Commission regulations under the control of veterinarians and with the assistance of trained technical personnel.

CH12F3-2A cell line

The mouse CH12F3-2A B-cell lymphoma cell line (RCB Cat# RCB2809, RRID:CVCL_E068) was acquired from the Ricken BRC cell bank. Cells were cultured in RPMI 1640 ATCC’s modified medium (Invitrogen; A1049101) supplemented with 10% FBS (Invitrogen; 10270-106), 5% NCTC-109 medium (Gibco A/Thermo; 21340039), 50 μM 2-mercaptoethanol, penicillin/streptomycin (Sigma; P4083) at density between 0.5-1 x 106 cells/ml. Media were changed every 3 days.

To obtain lentiviral constructs for the DamID experiment, mouse WT and G766R DIS3 coding sequences were fused to the Dam methylase coding sequence. WT DIS3 cDNA was amplified and cloned into pLgw V5-EcoDam lentiviral constructs (Addgene; 59210) using SLIC. The G766R DIS3 construct was obtained by site-directed mutagenesis.

For the negative control construct, mEGFP was amplified using PCR and cloned into pLgw V5-EcoDam lentiviral constructs (Addgene; 59210) using SLIC. Lentiviral transductions of CH12F3-2A cells were carried out exactly as described previously. Transduced cells were selected using Zeocin at a concentration of 250 µg/ml for 14 days and subsequently, cell cultures were expanded and preserved in 50% FBS, 40% RPMI medium and 10% DMSO.

The mouse CH12F3-2A AID⁻/⁻ B-cell lymphoma cell line (Kerafast, Cat# ESP013, RRID:CVCL_GZ21) was obtained from Kerafast and cultured under the same conditions as the AID wild-type (WT) CH12F3-2A cells. Both CH12F3-2A WT and AID⁻/⁻ cells were stably transduced with pLVX-SFFV lentiviral constructs expressing DIS3ᵂᵀ, DIS3ᴳ⁷⁶⁶ᴿ, or mCherry (control). Brieflly, lentiviral particles were produced in HEK293T cells by transient transfection using the calcium phosphate precipitation method. Briefly, cells were seeded one day prior to transfection to achieve ~70% confluence at the time of transfection. A DNA mixture containing the packaging plasmids psPAX2 (8.6 μg) and pMD2.G (5.5 μg) together with the lentiviral expression construct (8.6 μg, encoding either mCherry, DIS3 wild type, DIS3G766R) was prepared in 55 μL of 2 M CaCl₂ and sterile water (final volume 430 μL), then added dropwise under vortexing to an equal volume of 2× HEPES-buffered saline (HBS) to form a white precipitate. The mixture was immediately added to HEK293T cultures. After 16–18 h, the medium was replaced with fresh DMEM containing 10% FBS. Viral supernatants were collected 48 h post-transfection, cleared by centrifugation (800 × g, 5 min) and filtration through a 0.45 μm PES filter.

CH12F3-2A and CH12F3-2A AID⁻/⁻ cells were transduced with viral supernatant in the presence of polybrene (1:1000) by spin infection (1.5 h, 800 × g, 32 °C). Approximately 3 × 10⁶ cells per condition were used. Following transduction, cells were cultured in RPMI 1640 supplemented with 10% FBS without antibiotics. Transduction efficiency was monitored by mCherry fluorescence using flow cytometry 48 h post-infection.

CH12F3-2A cells were activated for 2 or 3 days by supplementing the standard culture medium with 1 ng/ml TGF-β1 (Peprotech, 100-21), IL-4 (Peprotech, 214-14)5 ng/ml, 1 μg/ml 𝛂CD40 (Invitrogen, 1C10, Functional Grade, 16-0401-82). Primer sequences are provided in Supplementary Table S1.

DamID-seq protocol

The DIS3 DamID-seq procedure and library preparations were performed as described previously 87. In short: DNA from CH12F3-2A cells expressing DIS3 DamID constructs or not, as the control, were isolated using a Bioline Isolate II genomic DNA kit (BIO-52067). First, 0.5 µg of DNA was digested with DpnI (NEB; R0176) in 10 µl volume at 37°C for 16h. Next, DpnI was heat-inactivated, and the DpnI adaptor was ligated in 20 µl volume with 2.5 units of T4 DNA ligase (NEB; M0202). This was followed by DpnII digestion in a total volume of 50 µl with 10U of DpnII (NEB; R0543). DNA was amplified by PCR with Taq polymerase using the following program: samples were denatured at 95 for 8 minutes, then amplified for 19 cycles, with: 20’’ of denaturation at 94℃, 30’’ of annealing at 58℃ and 20’’ elongation 72℃ followed by a 2 min incubation at 72℃.

PCR products were cleaned up using a Bioline Isolate II PCR and Gel kit according to the manufacturer’s instructions (BIO-52060). DNA was repaired with Blunt-end ending 3′ overhang using End-It DNA End-Repair kit in 25 µl volume (Epicentre; ER81050), and 3′-A overhang was added with Klenow fragment (NEB; M0212) and dATP. This was followed by purification using CleanPCR beads (CleanNA; CPCR-0005). DNA concentration was measured by Nanodrop and adjusted to 40 ng/µl.

Illumina Y-adaptor was ligated, and the libraries were PCR amplified using standard protocols and sequenced with an average read number of 40M. Primer sequences are provided in Supplementary Table S1..

PLA procedure

In situ Proximity Ligation Assay was performed on activated CH12F3-2A cells and non- activated (naive) cells as a control. Activation was performed by seeding cells at 105 cells/ml in culture medium supplemented with 1 μg/ml anti-CD40 antibody (Invitrogen, MA1-81395), 5 ng/ml IL-4 (Peprotech, 214-14) and 1 ng/ml TGF-β1 (Peprotech, 100-21), and cells were grown for 72 h at 37°C in 5% CO2. For each experimental condition, 2×106 cells were spun (400 rcf, 4 min), washed with PBS and spun again. Next, cells were fixed with 3,7% formaldehyde in PBS for 25 min at room temperature, washed with PBS twice, permeabilized with 1% Tween-20 (v/v) in PBS for 15 min at room temperature and washed with PBS once. Then, cells were blocked with 3% BSA in PBS for 1 h at room temperature. In order to reduce Fc receptor-mediated binding by antibodies used in PLA procedure, Mouse BD Fc Block (BD Biosciences, 553142) was added to cells in amount of 1 μg/106 cells and incubated according to manufacturer’s instructions. Primary anti-AID (1:100; Invitrogen, PA5-18913) and anti-DIS3 antibodies (1:100; Proteintech, 14689-1-AP) were added to cells in blocking solution and incubated overnight at 4°C. Additionally, three background controls were performed in which cells were incubated only with anti-AID antibody or only with anti-DIS3 antibody, or without primary antibodies. Next, cells were washed with PBS twice and the in-situ PLA was performed with the Duolink In Situ PLA kits (Sigma-Aldrich) according to manufacturer instructions with the exception that the reactions and washings were carried out in 1,5 ml microcentrifuge tubes, and all the incubations were performed in thermoblock. The interaction between AID and DIS3 was detected using Duolink In Situ PLA Probe Anti-Goat MINUS kit (Sigma-Aldrich, DUO92006) and Duolink In Situ PLA Probe Anti-Rabbit PLUS kit (Sigma-Aldrich, DUO92002) against anti-AID antibody and anti-DIS3 antibody, respectively. Fluorescent signal was generated using Duolink In Situ Detection Reagents Red kit (Sigma-Aldrich, DUO92008). Cells were mounted and the nuclei were stained using the Duolink In Situ Mounting Medium with DAPI (Sigma-Aldrich, DUO82040) as follows: after the last wash, cells were spun, all the supernatant was removed carefully, cells were resuspended thoroughly but gently in 12 µl of Mounting Medium, the cell suspension was dripped onto a microscope slide, covered with a coverslip (Ø 18 mm), and gently pressed down. Edges of the coverslip were sealed with nail polish.

γH2A.X immunofluorescent staining (B-cells)

DNA double-strand breaks were stained in mouse B cells using γH2A.X as a marker. For each experimental condition, 106 cells were spun (400 rcf, 4 min), washed with PBS and spun again. All the following steps were carried out in suspension in 1,5 ml microcentrifuge tubes. Washed cells were fixed with 3,7% formaldehyde in PBS for 25 min at room temperature, washed with PBS twice, permeabilized with 1% Tween-20 (v/v) in PBS for 15 min at room temperature and washed with PBS once. Then, cells were blocked with 3% BSA in PBS for 1 h at room temperature. Primary anti-γH2A.X (1:400; Abcam, ab2893) was added to cells in blocking solution and incubated overnight at 4°C. After being washed twice with PBS, cells were incubated with goat anti-rabbit IgG secondary antibody conjugated to Alexa Fluor 555 (1:800; Invitrogen, A-21429) for 1 h at room temperature. Next, cells were washed with PBS once and the nuclei were stained using 2 μg/ml Hoechst 33342 (Invitrogen, H3570) in PBS for 5 min at room temperature. After being washed twice with PBS, cells were spun, all the supernatant was removed carefully, cells were resuspended thoroughly but gently in 12 µL of ProLong Glass Antifade Mountant (P36980, Invitrogen), the cell suspension was dripped onto a microscope slide, covered with a coverslip (Ø 18 mm), and gently pressed down.

Microscopic analysis

For Fig. 5, cells were imaged using Zeiss LSM800 confocal microscope with 40×/1.3 oil immersion apochromatic objective and GaAsP PMTs detectors. ZEN software (Zeiss, version 2.6) was used for microscope control. Imaging was performed at a controlled temperature of 22⁰C. Z-stack images were processed using Fiji/ImageJ software (version 1.53f51). For each Z-stack, the maximum intensity Z-projection was applied and then images of PLA channel were subjected to rolling-ball background subtraction with a radius set to 20 pixels and contrast enhancement with 0.025% saturated pixels.

For Fig. 5 and Fig. EV6, cells were imaged using Olympus IX81-ZDC wide-field fluorescence microscope with 40×/0.95 apochromatic objective and EM-CCD camera (Hamamatsu, C9100-02) equipped with ScanR modular imaging platform (Olympus). ScanR Acquisition software (version 2.2.0.8) was used for microscope control. Imaging was performed at a controlled temperature of 22⁰C. Image analysis was performed using ScanR Analysis software (version 3.0.0). Images were subjected to rolling-ball background subtraction with a radius set to 40 and 2 pixels for DAPI and PLA channel, respectively. Next, segmentation was performed on the DAPI channel using the Intensity algorithm. The identified objects were gated based on a scatter plot of the circularity factor relative to the area. The object that comprised the gate was classified as normal, single nucleus. PLA spots in a cell were searched for as sub-objects in the area encompassing a nucleus and a 7-pixel radius outside it, basing on PLA channel and using the Intensity algorithm. Each identified sub-object was assumed to be a PLA spot. Further numerical data processing and statistical analysis were performed using R environment. Cells containing at least one PLA spot were filtered out and for each experimental condition, a total number of spots in a cell and its mean were calculated. Statistical significance was calculated using the Wilcoxon test. All data were checked beforehand for normality using the Shapiro-Wilk test.

For Fig. 5f,g cells were imaged using Olympus IX81 wide-field fluorescence microscope with 60×/1.35 oil immersion apochromatic objective and CCD camera (Hamamatsu, Orca-R2/C10600) equipped with ScanR modular imaging platform (Olympus). ScanR Acquisition software was used for microscope control. Imaging was performed at a controlled temperature of 22⁰C. Image analysis was performed using ScanR Analysis software (version 3.0.0). Images were subjected to rolling-ball background subtraction with a radius set to 75 and 2 pixels for Hoechst and γH2A.X channel, respectively. Next, segmentation was performed on the Hoechst channel using the Intensity algorithm. The identified objects were gated based on a scatter plot of the circularity factor relative to the area. The object that comprised the gate was classified as normal, single nucleus. γH2A.X foci in a cell were searched for as sub-objects in the area encompassing a nucleus basing on γH2A.X channel and using the Intensity algorithm. Each identified sub-object was assumed to be a DNA double-strand break. Further numerical data processing and statistical analysis were performed using R environment. For each experimental condition a total number of DSB in a cell was calculated. Statistical significance was calculated using the Wilcoxon test.

For Fig. 5f, representative images obtained by the abovementioned imaging procedure were selected. Images were processed using Fiji/ImageJ software (version 1.53f51). Images of both channels were cropped to provide a more accurate visualization of γH2A.X foci in the cells. Images of γH2A.X channel were subjected to rolling-ball background subtraction with a radius set to 3 pixels and contrast enhancement with 0.01% saturated pixels. Images of Hoechst channel were subjected to rolling-ball background subtraction with a radius set to 40 pixels

γH2A.X staining and imaging od CH12F3-2A cell lines

DNA double-strand breaks were detected in mouse B cells using γH2A.X (phospho S139) as a marker. Cells (10⁶ per condition) were washed once with PBS and stained with Live/Dead Fixable Green viability dye (Invitrogen) for 30 min at room temperature, protected from light. Cells were washed twice with PBS containing 1% BSA, fixed in freshly prepared 2% paraformaldehyde (PFA) in PBS for 15 min at room temperature, and washed twice with PBS + 1% BSA. Permeabilization was performed with 0.1% Triton X-100 in PBS for 15 min at room temperature and counterstained with 2 μg/ml Hoechst 33342 (Invitrogen, H3570), followed by two washes with PBS + 1% BSA.

Cells were stained with anti-H2AX (phospho S139)-PerCP-eFluor 710 (1:200; Invitrogen, 46-9865-42; RRID:AB_2573918) diluted in permeabilization buffer (PBS + 0.1% Triton X-100 + 1% BSA) for 1 h at 4 °C, protected from light. After staining, cells were washed twice with PBS + 1% BSA and resuspended in the same buffer. For imaging, cell suspensions were transferred to Greiner µCLEAR® black poly-D-lysine–coated plates (cat. #781946) and mounted. Images were acquired using the Opera Phenix high-content confocal microscope (PerkinElmer) equipped with a 63× water-immersion objective. Three confocal planes were collected per field.

Image analysis was performed in Harmony software (PerkinElmer). Nuclei were segmented based on Hoechst 33342 signal, and only live cells—negative for the Live/Dead stain—were included in subsequent analysis. γH2A.X foci were automatically detected as discrete spots in the PerCP-eFluor 710 channel.

Mouse line generation

The constitutive knock-in G766R mice were constructed according to the strategy described in Supplementary Fig. S1b in the Institut clinique de la Souris (ICS) Illkirch, France using homologoues recombination technology in mES cells in the C57BL/6N background.

In brief, embryonic stem (ES) cells were cultured following the standard protocol 88 and then electroporated with the targeting vector. This vector carried a G766R point mutation introduced into exon 17, specifically a G to C substitution at position 2393 of the gene sequence. The mutation site was flanked by 4 and 3.5 kb homology arms (Supplementary Fig. S1B) and accompanied by a floxed CMV promoter-driven Neo-cassette expressing Cre-ER, which was inserted into the intron of the Dis3 gene. Following targeting, ES cells were selected using G418, and clones were genotyped to identify those carrying the Dis3G766R allele. These cells were treated with tamoxifen to remove the cassette, leaving a single loxP site in intron 19. ES cells carrying the LDis3G766R allele were subsequently injected into C57BL/6N blastocysts, which were then implanted into the uterine horns of pseudopregnant C57BL/6N mice.

Chimeras and their subsequent progeny were mated with C57BL/6N wild-type mice to generate germline production and expand the colony. The progeny resulting from this cross-breeding produced heterozygous mice Dis3G766R/+ as well as wild-type (WT) controls. Mice are genotyped by PCR using primers:

766seq_F gtctcaaaaacacacggcagagactc; 766seq_R gaggatgtgtgagataagatacaccgg

Dis3 WT allele migrates as a 202 bp band, whereas the mutant G766R allele gives a 281 bp amplicon.

Mice breeding conditions

Mice were bred in the animal house of Faculty of Biology, University of Warsaw maintained under conventional conditions in a room with controlled temperature (22 ±2°C) and humidity (55 ±10%) under a 12 h light/12 h dark cycle in open polypropylene cages filled with wood chip bedding (Rettenmaier). The environment was enriched with nest material and paper tubes. Mice were fed ad libitum with a standard laboratory diet (Labofeed B, Morawski). Animals were closely followed-up by the animal caretakers and researchers, with regular inspection by a veterinarian, according to the standard health and animal welfare procedures of the local animal facility. Mice were euthanized by cervical dislocation in accordance with institutional and ethical committee–approved protocols.

Mouse embryonic fibroblast primary cell cultures

Primary cell cultures of mouse embryonic fibroblasts (MEF) were isolated from E13.5 embryos as defined by the date of the vaginal plug and confirmed by characteristic features of the embryo morphology. Embryo heads were used for DNA isolation and genotyping. The corpses were gutted and mechanically disintegrated by chopping and passing the tissue fragments suspended in culture media (DMEM supplemented with 10% FBS, 50 μM 2-mercaptoethanol, penicillin/streptomycin) with a syringe through the gauge 19 needles. Disintegrated tissue fragments of a single individual were left to settle in 100 mm petri dishes and form outgrowths. After 3 days, cells were treated with 0.25% Trypsin in 1 mM EDTA for 3 min. Cells were passaged for at least 3 passages before they were collected or used for further applications. Cells were passaged every 3 days. Cells isolated from 3 independent litters in pairs, including DIS3+/+ and DIS3G766R/+ were used for the RNA-seq library preparation.

For the 3T3 protocol, MEF cells from 12 DIS3+/+ and 13 DIS3G766R/+ littermates were used. Cells passaged 2 times to disintegrate into a single cell suspension and noted. Cells were plated into a 10-cm cell culture dish at a density of 9 × 105 cells per dish. Cells were cultured at standard conditions with medium change after 2 days in culture. Three days after plating, cells were trypsinized, counted using a hemocytometer, and replated 9 × 105 into a 10-cm cell culture dish. The strict passaging regime was repeated for 30 passages or until the immortalization of cells was obtained. Cumulative cell numbers were plotted.

Pristane-induced mouse plasmacytoma (MPC) model

Six- to ten-week old mice were injected 3 times intraperitoneally, in 2-month intervals, with 0.5 ml of pristane oil (2,6,10,12-tetramethylpentadecane) (Sigma-Aldrich, Cat. No. P2870) to induce the development of mouse plasmacytoma (MPC), an established murine model of multiple myeloma 66,67. The day of the first injection is considered as day 0 of the experiment. Mice were regularly observed for abdominal swelling. The neoplastic nature of the observed ascites was confirmed by paracentesis and ascitic smear stained with Wright-Giemsa stain which had to confirm >10 atipical plasma cells. Animals were closely followed-up by the animal caretakers and researchers, with with particular attention to lupus-like syndrome signes such as alveolar hemorrhage or fur loss. Regular inspection by a veterinarian was conducted, according to the standard health and animal welfare procedures of the local animal facility. Humane endpoint: After sample collection, mice were returned to the housing facility and monitored for no longer than one month. Mice showing any signs of suffering, significant distress, or significant weight loss (greater than 15% of body weight) were humanely euthanized by cervical dislocation and subsequently sectioned.

Mice for the pristane-induced mouse plasmacytoma experiment were maintained in individually ventilated cages in a room with controlled temperature (22 ± 2°C) and humidity under a 12 h light/12 h dark cycle.

As plasmacytomas in a C57Bl/6 background are a rare event in the WT genotype, to obtain a sufficient amount of material for and better compare plasmacytomas from Dis3G766R/+ and WT mice, we have backcrossed Dis3G766R/+ mouse line into plasmacytoma prevalent BALB/C background (more than 6 crosses) and used WT and Dis3G766R/+ littermates to induce plasmacytomaTo obtain G766R mice on a BALB/C genetic background, DIS3G766R/+ mice were backcrossed to BALB/CanNCrlCmd mice for 6 generations.

Tissue collection and primary cell culture, in vitro B-cell activation, CSR efficiency assay

Splenic naïve B-cells were prepared using the CD43 negative selection commercial kit (Miltenyi Biotec.; 130-090-862). Spleens were pulled from 2-3 terminated sex- and age-matched animals mechanically disintegrated and filtered through a 70 µm cell strainer to prepare a single cell suspension essential for microbead separation. Spleen extracts were depleted from red blood cells using ACK lysis buffer. The magnetic bead, column-based enrichment of naïve B-cells was performed according to the manufacturer’s instructions. Cells were resuspended and cultured in RPMI 1640 containing 15% FBS supplemented with 100 nM 2-mercaptoethanol and penicillin/streptomycin (Sigma; P4083).

Ex vivo activation of B lymphocytes for class switch recombination, proliferation studies and RNA sequencing and was achieved by culturing them in RPMI 1640 containing 15% FBS supplemented with 100 nM 2-mercaptoethanol, 20 µg/ml LPS and 20 ng/ml IL-4. CSR efficiency was assessed in B-cells 3 days after activation. In brief, cells were collected and stained for 30 min with Alexa Fluor 555 conjugated goat antibody raised against mouse IgG1 (Invitrogen; A-21127) at 10 µg/ml. The percentage of positive cells that underwent IgG1 class switch recombination was quantified using flow cytometry on an Attune NxT Cytometer (Thermo) or CytoFLEX Flow Cytometer (Beckman Coulter) and analyzed using FlowJo (v10.7.1) software.

CellTrace Proliferation Assay

CellTrace™ Yellow Proliferation kit was used according to the manufacture’s instructions. Briefly: Cells were stained by adding 1 μl of CellTrace™ Yellow (Invitrogen, Cat #C34567) stock solution in DMSO per each ml of 106/ml cell suspension in PBS (5 μM working concentration), and subsequently incubated for 40 minutes at room temperature, while being protected from light. Afterward, five times the original staining volume of culture medium, containing FBS, was added to the cells and incubated for 5 minutes. The cells were then pelleted and resuspended in a fresh pre-warmed complete culture medium. 10 minute incubation period was allowed before analysis to enable the acetate hydrolysis of the CellTrace™ reagent. Cells were pipetted in technical triplicates, 3x105 per well, into round bottom 96 well plate in 200 μl growth medium. Cells were activated by supplementing media with 20 µg/ml LPS and 20 ng/ml IL-4 and cultured for 3 days. Naïve B-cells and unstained control cells were parallelly cultured in triplicates.

Before cytometric analysis, dead cells were stained with LIVE/DEAD fixable dyes (Invitrogen™) according to manufactures instructions and analized on a CytoFLEX Flow Cytometer (Beckman Coulter).

Data were analyzed using FlowJo (v10.7.1) software using the Proliferation functionality.

Splenic Germinal Center Phenotyping

Spleens were harvested from naïve and immunized mice (three biological replicates per genotype for naïve animals and eight individuals per genotype at day 14 post-immunization with the Moderna mRNA vaccine). Single-cell suspensions were prepared and analyzed by flow cytometry to quantify and phenotype germinal center (GC) B cells and to determine the light zone (LZ) to dark zone (DZ) ratio.

Tissue Processing

Spleens were placed into a 70-μm cell strainer and mechanically dissociated using the plunger of a 5-mL syringe. The strainer was rinsed with 5 mL of cell culture medium (RPMI-1640 supplemented with 10% FBS), and the suspension was transferred to a 15 mL Falcon tube. Cells were pelleted by centrifugation at 800 × g for 3 min at room temperature, the supernatant was discarded, and the pellet was resuspended in 1 mL of ACK lysis buffer to remove erythrocytes. After 5 min incubation at room temperature, 9 mL of culture medium was added to quench the lysis. Cells were centrifuged again (800 × g, 3 min), the supernatant discarded, and the pellet resuspended in 10 mL of culture medium. Cell concentration and viability were determined using a hemocytometer or automated cell counter.

Flow Cytometry Staining

For surface staining, 1–2 × 10⁶ splenocytes were incubated with Live/Dead Fixable Near-IR dye (Invitrogen, L34975; 1:1000 dilution) for 30 min at room temperature, protected from light. Cells were then blocked with anti-mouse CD16/CD32 (Fc block, 1 μg per 10⁶ cells; BD Biosciences) or purified mouse IgG (1–2 μg per 10⁶ cells) in PBS containing 1% BSA for 15 min on ice. Without washing out the blocking reagent, fluorochrome-conjugated antibodies were added in PBS + 1% BSA and incubated for 30 min at 4 °C, protected from light.

The following antibodies were used: anti-CD45R/B220–APC (clone RA3-6B2, BD Biosciences, Cat. #553092, used at 0.5 μL per 10⁶ cells; RRID:AB_398531), anti-GL7– CoraLite® Plus 488 (clone GL-7, Proteintech Cat No. CL488-65261; RRID:AB_3084186; used at 0.8 µg per 10^6 cells), anti-CD95– BUV395 (clone Jo2, BD Biosciences, Cat. #740254; RRID:AB_2739999; at 1 µL per 106 cells), anti-CD184 (CXCR4) Monoclonal Antibody (2B11), Invitrogen eBioscience™, Cat. #14-9991-82; RRID:AB_842770; used at 0.5 µg per 106 cells), and anti-CD86– BD Horizon™ BV510 (clone GL1, BD Biosciences Cat. #563077; RRID:AB_2737991; at 2.5 µL per 106 cells). Samples were acquired on a CytoFLEX LX Flow Cytometer (Beckman Coulter) flow cytometer and analyzed using CytExpert (v2.23, Beckman Coulter) final visualization and statistical analis were performed in R. Dead cells and doublets were excluded by standard gating. Gating strategy was demonstrated on representative samples in Supplementary Fig. S4A-B.

Germinal Center Zonal Analysis

GC B cells were identified as B220⁺ GL 7⁺ CD95⁺. Within this population, dark zone (DZ) and light zone (LZ) subsets were defined as follows:

• DZ B cells: CXCR4high CD86low

• LZ B cells: CXCR4low CD86high

The LZ:DZ ratio was calculated as the frequency of LZ B cells divided by the frequency of DZ B cells within the total GC B-cell gate.

RNA-seq, Mate-Pair and Paired-End DNA-seq

Total RNA from collected tissues and primary cultures was isolated using a standard TRIzol protocol followed by ribodepletion with a Ribo-Zero Gold rRNA Removal Kit (Human/Mouse/Rat) according to the manufacturer’s instructions (Epicentre; RZG1224). Strand-specific libraries were prepared using a TruSeq RNA Sample Preparation Kit (Illumina) and the dUTP method. Three independent replicate sample sets were prepared for each condition. DNA was purified using a SherlockAX kit optimized for small samples (A&A Biotechnology; 095-25). NEXTERA XT mate-pair and TruSeq DNA libraries were prepared according to manufacturer's instructions (Illumina; FC-131-1024 and FC-121-2001). All of library types were sequenced using Illumina NextSeq or NovaSeq platform in a paired-end mode with different average sequencing depth: MEF RNA-seq: 29M reads, Mate-pair DNA libraries: 42.7M reads, standard DNA-seq libraries: 281.5M reads (NovaSeq). The total RNA extracted from splenic primary B-cell cultures, naïve and in vitro activated for 72h and 96h has been commercially sequenced by Azenta Life Sciences. They utilized their strand-specific library protocol, achieving an average depth of 23 million 100 nucleotide paired-end reads.

DRIPc-seq (DNA/RNA hybrid immunoprecipitation followed by cDNA conversion and sequencing)

After 72h h, the culture medium was removed, and cells were washed once with cold PBS and twice with TE buffer (10 mM Tris [pH 8.0] and 1 mM EDTA). Cell lysis was performed directly on the plate by adding 0.5 ml of lysis buffer (TE supplemented with 0.625% SDS and 0.0625 mg/ml 7 Proteinase K), and the suspension was immediately transferred to a fresh 2 ml tube and incubated for 4.5 h at 37°C with gentle mixing every 15 min. Afterward, genomic DNA was purified by phenol:chloroform:isoamyl alcohol (25:24:1) extraction and ethanol precipitation. Precipitated DNA was washed with 70% ethanol, air-dried, and resuspended in 30 µl of TE buffer. Next, pure gDNA was sheared by sonication using Bioruptor Plus with a water-cooling system (Diagenode) as the following: 3 µg gDNA diluted to 200 µl in TE was placed in 1.5 ml DNA LoBind Tubes (Eppendorf) and subjected to 30 cycles of sonication (30 s ON, 90 s OFF) in “Low” mode. The length of fragments after sonication was evaluated by running DNA samples before and after shearing on a 1.5% agarose gel in 1X TAE (40 mM Tris [pH 8.0], 20 mM acetic acid, and 2 mM EDTA) at 70-80 V. To ensure specificity, nucleic acids are pre-treated with Ribonuclease H (RNase H) (NEB, cat. No. M029I7S) to eliminate RNA:DNA hybrids from the mixture. In this process, 4 μg of digested gDNA is incubated with 4 μL of NEB RNase H for 4 hours at 37°C. Following this treatment, proceed with the S9.6 immunoprecipitation step, processing both the control, RNase H-treated samples alongside the experimental samples. Afterward, 4 µg of fragmented gDNA was mixed with 10 µg of S9.6 antibody, diluted to a final volume of 1 ml with binding buffer (20 mM HEPES [pH 7.4], 150 mM NaCl, and 0.05% Triton X-100), and incubated overnight with gentle rotation in a refrigeration cabinet. The next day, input fractions that contained S9.6 were added to 50 µl of M2 anti-FLAG Magnetic Beads (Sigma) or Protein G Dynabeads (Thermo), respectively, and incubated for 2 h. Additionally, a second set of negative controls were prepared by incubating 4 µg of sonicated gDNA with beads without the bait. Following incubation, the unbound fraction was removed, and beads were washed three times with 500 µl of binding buffer for 5 min with gentle rotation in a refrigeration cabinet. Elution was performed by adding 125 µl of elution buffer (50 mM Tris [pH 8], 1 mM EDTA, 0.5% SDS, and 0.64 mg/ml Proteinase K) for 45 min at 55°C. Two replicates of each sample were pooled and purified using a DNA clean and concentrator kit (Zymo Research) according to the manufacturer’s protocol. After elution with 30 µl of elution buffer (Zymo Research), half of the eluate was treated with 2 µl of DNAse I (1 U/µl) in 1X DNase I digestion buffer (Thermo Fisher Scientific) for 1 h at 37°C. For DNase I inactivation, 1 µl of 0.5 M EDTA was added for 10 min at 65°C. RNA was purified using the RNA clean and concentrator kit (Zymo Research). The purified RNA underwent NGS library preparation and subsequent sequencing, which was conducted commercially by Azenta Life Sciences using their strand-specific protocol.

MicroC

MicroC was performed according to 89 with minor modifications. Two replicates of B cells, isolated from WT and Het mice, were activated for 72hours by incubation with LPS and IL4 (20 µg/ml LPS and 20 ng/ml IL-4 final concentration) prior to experiment. Next, cells were collected by trypsinization, washed in PBS and counted. For crosslinking, 300 mM DSG (ThermoScientific) solution in DMSO (100x) was prepared freshly and diluted to 1x in room temperature PBS. Cellular pellet was resuspended in DSG solution and incubated for 35 minutes on rotating wheel at room temperature. Next 16% methanol-free formaldehyde (Thermo Scientific) was added to a mixture to a final concentration of 1% and incubated for next 10 minutes on rotating wheel at room temperature. To quench the crosslinking reaction, Tris pH 7.5 was added to a final concentration of 0.375 M and incubated on rotating wheel for 5 minutes at room temperature. Next, cells were centrifuged at 4°C, washed once with ice-cold PBS and re-counted. Cells were divided into 5 mln cells portions, pelleted, snap frozen and stored at -80°C until needed.

Before MicroC protocol, MNase titration was performed to specify the required amount of enzyme for sufficient chromatin cleavage. Next, 10 million crosslinked activated B cells from WT and Het mice were resuspended in MB#1 buffer (10 mM Tris pH 7.5, 50 mM NaCl, 5 mM NgCl2, 1mM CaCl2, 0.1% Igepal, EDTA-free protease inhibitor cocktail (Roche)) and incubated on ice for 20 minutes to isolate nuclei. After one wash with MAB#1 buffer, nuclei were resuspended in MB#1 buffer containing MNAse and incubated for 20 minutes in 37°C with 1000 rpm rotation in thermoblock to digest chromatin. Reaction was stopped by addition of 4mM EGTA and 10 minutes incubation at 65°C. Next, nuclear pellet was washed twice in MB#2 buffer (10 mM Tris pH 7.5, 50 mM NaCl, 10 mM MgCl2, BSA), resuspended in 100 ul Repair End Master Mix (1x NEB 2.1, 2mM ATP, 5mM DTT, 50 U PNK), incubated at 37°C for 15 mins with shaking 1000 rpm. Next, 50 U Klenow was added and incubated for next 15 mins at 37°C with shaking 1000 rpm. To biotinylate, Biotin Master Mix (66 nM Biotin-dATP, 66 nM Biotin-dCTP, 66 nM dTTP, 66 nM dGTP, T4 DNA ligase buffer, 33 ug BSA) was added and incubated 45 mins 25°C with shaking 1000 rpm. To quench the reaction EDTA was added to final concentration of 30 mM, incubated at 65°C for 20 minutes and the chromatin pellet was washed once with MB#3 buffer (50 mM Tris pH 7.5, 10 mM MgCl2, 50 g BSA). Next, the chromatin pellet was resuspended in Ligation Master Mix (T4 buffer, 100 g BSA, 10 000 U T4 DNA ligase), incubated for 2.5 hours at 25°C and centrifuged. The chromatin pellet was resuspended in Exonuclease Master Mix (NEBuffer 1, 1000 U Exonuclease III), incubated for 15 mins at 37°C and centrifuged. Next, the samples were incubated overnight at 65°C in Reverse crosslinking solution (1% SDS, 200 mM NaCl, 520 ug Proteinase K, 26 ug RNAseA), DNA was purified on 1.0x AmPure beads to enrich for dinucleosome fraction, concentration was measured and aliquot was checked on agarose gel to ensure efficient MNase cleavage.

Prior to library preparation 10-15ug biotinylated chromatin was pull-down using Dynabeads MyOne Streptavidin T1 for 30 mins incubation at room temperature, washed twice with 1xTWB (5 mM Tris-HCl pH 7.5, 0.5 mM EDTA pH 8.0, 0.1% Tween-20) and once with 10 mM Tris pH 7.5.

Library was prepared using NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® according to manufacturer protocol. Briefly, beads were resuspended in 25 ul water, 3.5 ul End Prep Reaction Buffer and 1.5 ul End Prep Enzyme Mix and incubated in thermocycler for 30 mins at 20°C, followed by 30 mins at 65°C. Next, 15 ul Ligation Master Mix, 0.5 ul Ligation Enhancer and 2.5 ul unique xGen UDI-UMI Adapter with indexes (IDT) were added and incubated for 30 mins at 20°C. The beads were washed twice with 1xTWB and once with 10 mM Tris pH 7.5. Test PCR using Q5 NEB Mix was run on 10% beads to establish required number of PCR cycles and final PCR was performed accordingly. Eventually, PCR product was purified with two-sided AmPure size selection (0.6x and 1x), measured using Qubit and checked on TapeStation. The libraries were paired-end 150 bp sequenced to around 300 MR each.

Global proteomics by liquid chromatography (LC)-MS

Cell pellets were subjected to the Sample Preparation by Easy Extraction and Digestion (SPEED) protocol90. In brief, cells were solubilized in concentrated TFA and incubated for 10 minutes at RT. Samples were neutralized by adding 2 M Tris-Base buffer using 10×volume of TFA and further incubated at 95°C for 5 min after adding Tris(2-carboxyethyl)phosphine (TCEP) to a final concentration of 10 mM and 2-Chloroacetamide (CAA) to a final concentration of 40 mM. Protein concentrations were determined by turbidity measurements at 360 nm, adjusted to the same concentration using a sample dilution buffer (2M TrisBase/TFA 10:1 (v/v)) and then diluted 1:4-1:5 with water. Digestion was carried out overnight at 37°C using trypsin at a protein/enzyme ratio of 100:1. TFA was added to a final concentration of 2% to stop digestion. The resulting peptides were TMT labelled on-StageTip. TMT-labelled samples were compiled into a single TMT6 sample and concentrated using a SpeedVac concentrator. Peptides in the compiled sample were fractionated (6 fractions) using the bRP fractionation. Prior to LC-MS measurement, the peptide fractions were resuspended in 0.1% TFA, 2% acetonitrile in water. Prior to the liquid chromatography (LC)-MS measurement, the peptide fractions were resuspended in 0.1% TFA and 2% acetonitrile in water. Chromatographic separation was performed on an Easy-Spray Acclaim PepMap column (50cm length × 75 µm inner diameter; Thermo Fisher Scientific) at 55°C by applying 90 min acetonitrile gradients in 0.1% aqueous formic acid at a flow rate of 300 nl/min. An UltiMate 3000 nano-LC system was coupled to a Q Exactive HF-X mass spectrometer via an easy-spray source (all Thermo Fisher Scientific). The Q Exactive HF-X was operated in TMT mode with survey scans acquired at a resolution of 60,000 at m/z 200. Up to 18 of the most abundant isotope patterns with charges 2-5 from the survey scan were selected with an isolation window of 0.7 m/z and fragmented by higher-energy collision dissociation with normalized collision energies of 32, while the dynamic exclusion was set to 35 s. The maximum ion injection times for the survey scan and dual MS (MS/MS) scans (acquired with a resolution of 45,000 at m/z 200) were 50 and 130 ms, respectively. The ion target value for MS was set to 3e6 and for MS/MS was set to 1e5, and the minimum AGC target was set to 1e3.The data were processed with MaxQuant v. 1.6.17.0 91 and the peptides were identified from the MS/MS spectra searched against Uniprot mouse reference proteome (UP000000589) using the built-in Andromeda search engine. Reporter ion MS2-based quantification was applied with reporter mass tolerance = 0.003 Da and min. reporter PIF = 0.75. Cysteine carbamidomethylation was set as a fixed modification and methionine oxidation, glutamine/asparagine deamination, as well as protein N-terminal acetylation, were set as variable modifications. For in silico digests of the reference proteome, cleavages of arginine or lysine followed by any amino acid were allowed (trypsin/P), and up to two missed cleavages were allowed. The false discovery rate (FDR) was set to 0.01 for peptides, proteins, and sites. A match between runs was enabled. Other parameters were used as pre-set in the software. Unique and razor peptides were used for quantification enabling protein grouping (razor peptides are the peptides uniquely assigned to protein groups and not to individual proteins). Reporter intensity corrected values for protein groups were loaded into Perseus v. 1.6.10.092. Standard filtering steps were applied to clean up each dataset: reverse (matched to decoy database), only identified by site, and potential contaminant (from a list of commonly occurring contaminants included in MaxQuant) protein groups were removed. Reporter intensity corrected values were Log2 transformed. Protein groups with all values were kept. Reporter intensity values were then normalized by median subtraction within TMT channels. Student’s t-tests were performed on the these values for groups of samples constituting a given dataset. Student’s t-test (permutation-based FDR = 0.01, S0 = 0.1) was performed to return protein groups, which levels were statistically significantly changed between the sample groups investigated. This dataset has been deposited to the ProteomeXchange Consortium via the PRIDE repository with the dataset identifier PXD050438.

Bioinformatic analysis

MMRF CoMMpass study whole-genome DNA sequencing reads were mapped to the GRCh38 human reference genome, using STAR93 processed and filtered using SAMtools94. To identify translocations, significant structural variants were called using SVDetect95 with default settings. Significant translocations were filtered for those known to be commonly initiated in the Igh locus and genotype. RNA sequencing reads were mapped using STAR split read aligner. RNA sequencing samples of patients genotyped based on exome sequencing as being a DIS3 somatic mutation or a recurrent DIS3 mutation were compared to RNA samples from patients not possessing such a genotype as well as not possessing a germline DIS3 mutation or a mutant DIS3 variant as a minor clone. Reads were counted onto a gene annotation (gencode v34 basic) using FeatureCount (subread package release 2.0.0)96. Differential expression RNA-seq analyses were performed as previously using DESeq2 R package97. Whole-exome DNA sequencing reads were mapped to the GRCh38 human reference genome, using STAR split read aligner93, and single nucleotide variants were called using SAMtools and BCFtools pipeline94. Healthy tissue control whole-exome DNA sequencing data were mapped and variants that were identified in at least one control sample were defined as germline mutations. The Single Base Substitution (SBS) profiles were calculated using SigProfilerMatrixGenerator98 and fitted to the COSMIC Mutational Signatures (v3.2) using mmsig71.

To identify translocations in plasmacytoma DNA samples, mate-pair DNA sequencing data were preprocessed with NXtrim99. Both mate-pair and standard TruSeq DNA libraries were mapped by STAR split read aligner. Statistically significant structural variants were called using SVDetect with default settings. All significant structural variants from all sequencing experiments have been concatenated together according to the genotype and merged using bedtools merge to exclude loci involved in more than one structural variant to be analyzed multiple times. These annotations have been used for the meta-analysis. For the filtering to identify those involving an extended region, including the immunoglobulin heavy chain locus, custom scripts were used.

Whole genome sequencing data were used to identify structural variants (SVs) with single nucleotide breakpoint refinement by Manta (v1.6.0)100. Manta breakends with precise confidence intervals were extracted along with streaches of microhomology flanking annotations. Filtered breakpoints were formatted into tab-delimited files for microhomology analysis. Microhomology length distributions were visualized in R using histograms and density plots to infer DNA repair signatures related to AID-driven breaks. Analyses employed BEDtools, bcftools, samtools, and UCSC utilities in a Linux environment with a Python 2.7 environment for variant processing.

DamID-seq data were stripped of sequencing adapters using cutadapt and mapped to the mouse reference genome (GRCm38) using STAR aligner. Uniquely mapping reads were counted onto DpnI restriction fragments in the mm10 genome. Count tables were library size corrected. Counts for the 3 replicates of the DamOnly negative control have been averaged and subtracted from all replicates to eliminate the background signal. The annotation of putative PROMPT 3kb regions upstream and antisense to the known transcription start sites in the gencode release M25. Mean values have been calculated of DIS3 occupancy.

ChIP-seq reads were aligned to the mouse reference genome (GRCm38.p6) using bowtie2. All replicates were pooled and used to call peaks with MACS2 (v2.2.7.1) using IGG control as the background. The obtained tracks and peak annotations were utilized in downstream analysis. Super-enhancer annotations were obtained from publicly available data (GSE6206326)29. The coordinates, originally in the mm9 genome assembly, were converted to the GRCm38.p6 genome assembly using the UCSC Genome Browser's liftOver tool.

Meta-analyses of genomic regions were performed using custom scripts employing BWtool.

Structural variants hotspots were tested for nucleotide composition similarities and their relation to AID chromatin occupancy or transcription using custom scripts. The AID DNA recognition motifs tracks were prepared by calculating the density of RGYW, GAGCT, GGGGW, and GGGCT sequence occurrence in the GRCm38.p6 mouse reference genome. Two G-quadruplex prediction annotations were performed by scanning the GRCm38.p6 mouse genome for at least 4 stretches of at least three G separated by up to 7 of any nucleotides for a more stringent annotation or separated by 30 of any nucleotides for the less stringent annotation. From these annotations, strand-specific density tracks were prepared and used for meta-analysis. Single-nucleotide variants in plasmacytoma DNA were called using the SAMtools/BCFtools pipeline. Statistically supported variants that were uniquely present in a single tumor sample were defined as somatic. All possible C→T transitions and C→G, C→A transversions were identified, and their directionality was assigned to the corresponding genomic strand (+ or −). Strand-specific RNA-seq signals were then aggregated in a strand specific maner, to assess the strand asymmetry of somatic hypermutation (SHM). Results were then visualized and statistic was performed in R.

To assess the spatial relationship between chromosomal translocation breakpoints and genomic features associated with chromatin activity, we compared their distances to significant ChIP-seq peaks (H3K4me1, H3K4me3, CTCF, Rad21, p300) and super-enhancer annotation. The analysis was conducted using BEDTools. For each breakpoint, the nearest chromatin feature was identified using bedtools closest. Randomized control datasets were generated by shuffling breakpoint coordinates across the genome while preserving chromosomal distribution, using bedtools shuffle. The absolute distances between breakpoints and features in the observed and randomized datasets were compared using the Wilcoxon rank-sum test to assess enrichment or depletion of proximity.

Additionally, the relative distance distributions (fractional distances within feature intervals) were calculated using bedtools reldist. Observed relative distance frequencies were compared to those expected under the random model using Pearson's χ² test.

The likelihood of G766R mutation pathogenicity, was assessed using the PON-P2 algorithm101, with default settings. This machine learning-based classifier categorizes variants into pathogenic, neutral, and unknown classes by utilizing various information, such as evolutionary conservation of sequences, physical and biochemical properties of amino acids, GO annotations, and, if accessible, functional annotations of variation sites.

Raw Hi-C reads were trimmed using TrimGalore (v0.6.7)102,103. The fastq files were processed using the Juicer Pipeline (v2.13.07)104, using default options and aligned to the GRCm38/mm10 genome assembly. The processing was performed without any input for restriction enzyme sites as MicroC uses the Micrococcal Nuclease (MNase) which does not cleave at specific cut-sites.

Raw ligation frequencies were obtained from the .hic files using juicer tool3 with the following command: juicer dump observed NONE <input.hic> <chr12> <chr12> BP 2000 output.txt for each replicate separately. Next, the data was imported to R. The ligation frequency matrix for chromosome 12 was normalized using the Iterative Proportional Fit algorithm (matrix balancing) as described previously105 (https://pekowskalab.nencki.edu.pl/research/the-code).

Publicly available data from the following publications were used: ChIP-seq AID SRA: SRP00360523; ChIP-seq H3K4me3, H3K4me1, H3K27ac, p300, CTCF and Rad21 SRA: SRP07598564; EXOSC3 KO RNA-seq SRA: SRP0423557; GRO-seq B-cells and CH12F3-2A SRA: SRP19375830; Super enhancer annotation: GSE6206329.

Data availability

The sequencing data have been deposited in NCBI’s Gene Expression Omnibus (GEO) under the accession code GSE155631 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE155631). The raw sequencing data have been deposited in the Sequence Read Archive (SRA) under the accession code SRP275679 (https://trace.ncbi.nlm.nih.gov/Traces/?view=study&acc=SRP275679), associated with BioProject PRJNA650522 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA650522).

The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE repository under the accession code PXD050438 (https://www.ebi.ac.uk/pride/archive/projects/PXD050438).

Source Data are provided with this paper.

Code availability

All custom scripts used for analyses have been deposited in Mendeley Data and are publicly available at: 10.17632/7tdgwkzmnr.1 .

Ethical issues

All procedures were approved by the First Local Ethical Committee in Warsaw affiliated at the University of Warsaw, Faculty of Biology (approval numbers WAW/092/2016, WAW/177/2016, WAW/642/2018). Housing in animal facilities was performed in conformity with local and European Commission regulations under the control of veterinarians and with the assistance of trained technical personnel.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Activation Induced cytidine Deaminase (AID) catalyzes Class switch recombination (CSR) and somatic hypermutation (SHM) to promote antibody gene diversification. How AID identified the IgH locus to promote antibody gene specific SHM without mutating various other oncogenes in the B cell genome is an important and exciting topic of research. Previous studies have established that AID functions by associating with the exoribonuclease RNA exosome complex with both associating with a stalled RNA polymerase II Complex. While stalled RNA polII recruits AID, the RNA decay activity of RNA exosome decays the RNA associated with the stalled polII to resolve transcription stalling and stimulate RNA polII elongation and provide single strand DNA substrates for AID. Loss of RNA exosome activity lead to reduced AID activity on the IGH locus while catalytically dead RNA pol II (DIS3-depleted) lead to loss of CSR and increase in IgH chromosomal translocation( Laffleur et al , 2021). In these studies, multiple transcriptional effects on the noncoding genome of B cells were were observed due to RNA exosome loss of function. In this exciting study, the authors have identified a gain of function DIS3 mutant (DIS3766R) that promotes IgH chromosomal translocation that are associated with B cell transformation due to increased association with AID on chromatinized DNA, without affecting other aspects of RNA exosome function on B cell transcriptome. This study provides important evidence of the role of chromatin associated RNA in recruiting AID to its target sites and expands our knowledge of the mechanism of AID targeting. The identification of the DIS3766R mutation, that separates RNA exosome function in general cellular RNA degradation from its role in AID oncogenic activity, will be a widely used information in the AID targeting and B cell malignancy fields. Although many oncogenic mutations are identified in various B cell cancers, rarely are they followed up properly to establish their mechanism in malignant transformation. In this regard, this study is unique. The study has been done with significant experimental I propose publication of this manuscript. Some revisions, as suggested below, may improve the manuscript and clarify some important points.\n1. The authors could reconsider the title of the manuscript. The association of the mutation with multiple myeloma is important but could be misleading. These AID associated mutations, most likely, occur in the germinal center and persist in the plasma B cells (that are associated with MM). CSR is also unperturbed for the specific mutation being investigated here. Thus, focusing on the main point- Oncogenic DIS3 mutation supports AID activity to cause chromosomal translocations- maybe more accurate. This is a suggestion and authors can decide what they think is the best.\n2. In fig. 1F, the authors show increased R-loops over promoters. Analyses of R-loop status at promoters of AID off-target genes would be better to show here (as an additional ub figure), with an example of one IGV track of the data that reflects the quality of the DRIP-seq.\n3. In figure 3, the authors demonstrate the translocation in the DIS3+/G766R mutation. What are the targets of these translocations and are there microhomologies in the translocation junctions. Are there any evidence of asymmetric SHM at the translocation breakpoints.\n4. In figure 6, authors suggest that stalled DIS3G766R mutant protein occur on chromatin associated RNAs. Are these the same RNAs that accumulate at R-loops at the promoters? How are the authors corelating stalling DIS3 on chromatinized RNA with them being R-loop driving.\n5. Finally, in the discussion that authors could also refer to the mechanism of stabilization of single strand DNA through secondary DNA structures (PMID: 31509023) and AID phosphorylation and association with single strand DNA binding RPA (PMID: 19442251). These are the fundamental mechanism that DIS3 may collaborate with to promote oncogenic translocations.", "reviewer_2": "In this manuscript, Kulinsky et al examine the role of DIS3, a key component RNA exosome complex in driving mutations and translocations in multiple myeloma. The authors knocked-in a G766R mutation, a clinically relevant DIS3 variant, into the endogenous DIS3 locus. They find that while mice homozygous for the mutation is embryonically lethal, heterozygous DIS3(G766R) mice develop normally, allowing them to examine the contribution of this mutation to cellular transformation. The authors find that the heterozygous DIS3(G766R) mutations drive murine plasmacytomas in a pristane-induced model, and whole-genome sequencing revealed increased IgH-dependent chromosomal translocations. They also find that mutated DIS3 accumulates on chromatin bound RNA, and especially at sites that are known off-target AID sites. Employing a CH12 B lymphoma line, the authors demonstrate that there is increased DNA damage in cells expressing DIS3(G766R), and they also demonstrate increased physical proximity of Igh and protooncogenes. These results prompted the authors to propose a model wherein DIS3 mutations enhance AID promiscuity to drive IgH translocations and multiple myeloma development.\nThe mechanisms that protect the B cell genome from AID-induced collateral damage is of much relevance to our understanding of B cell transformation. The role of the RNA exosome complex in AID targeting has been extensively studied in the context of class switching and somatic hypermutation, but how (and if) mutations in RNA exosome proteins found in patients with multiple myeloma contribute to cellular transformation is poorly understood. Thus, generating a mouse model that recapitulates some aspects of the human disease is of potential interest. However, the authors need to address several issues to strengthen the work.\n1. The major claim of the authors based on the data presented is that the translocations are AID-dependent. However, other than indirect evidence based on sequence motifs etc, there is no data showing that this is indeed the case. The authors should absolutely provide data to show that the translocations they pick up are indeed AID-dependent. AID-deficient mice are generally available and there are multiple ways to delete AID from cells/cell lines. Without this direct experimental evidence, it is difficult to evaluate the major findings of the study.\n2. The authors should do a thorough analysis of the germinal center response of the knock-in mice. Are there altered GC B cell responses at homeostasis and after immunization, altered LZ:DZ ratio, effects on affinity maturation?\n3. The manipulated CH12 cells (Fig 6) are very poorly characterized. Are these clones of CH12 cells that have the KI mutation? Or are pools being analyzed? What are the DIS3 protein levels? What happens when these cells are stimulated? Do they switch like the more widely used CH12F3-2B subclone? For Fig 3D, how do the authors know that they are indeed detecting AID? Without these control data, it is nearly impossible to evaluate the data presented.", "reviewer_3": "¬¬In this manuscript the authors examine the functional effects of a heterozygous hypomorphic Dis3 mutation in a novel mouse model. They find that the mice, particularly males, are smaller, the B-cell less proliferative and susceptible to pristane induced plasmacytomas with increased chromosomal translocations. This is consistent with their provocative ref57 which postulates that Dis3 mutations are an early event which predispose to IgH translocations but are subsequently selected against because of the anti-proliferative effects. The current manuscript fully supports this novel and original hypothesis, but leaves a number of unanswered questions. There are a large number of experiments with excellent bioinformatic analyses. There are some minor points:\nTable S12 and S13 are supposed to list DIS3G766R/+ and WT plasmacytoma translocations respectively. I could not identify the IgH translocations shown in the Circos plots in Figure 3D, and I wonder if the wrong tables were uploaded. It would be helpful to highlight in the table the IgH translocations shown in the Circos plots. It is not clear how many plasmacytomas were sequenced, and whether each had a unique IgH translocation, only some had an IgH translocations, as in humans, or if some had multiple IgH translocations. Were cell lines established? Are the tumor and translocations polyclonal, clonal or subclonal? Are any translocations recurrent? Do they drive the expression of adjacent oncogenes? DO they appear to be driver or passenger mutations? It is somewhat surprising that Igh-Myc translocations were not observed as they are almost universal in other pristane-induced plasmacytoma models.\nAlthough not critical to support their main hypothesis additional characterization of DIS3G766R/+ would be nice. C57Bl/6 spontaneously develop monoclonal gammopathy with age. Is this accelerated in DIS3G766R/+? Is there any change in isotype distribution in DIS3G766R/+ compared to WT?\nThe English can be improved:\n“Taken together, by introducing Dis3G766R mutation in a heterozygous setting, we recapitulate the clinical markup of MM cells”\nI am not sure what they are trying to say, but the mouse does not reproduce clinical features of human MM (Anemia, bone disease, renal lesions)."}, "author_response": "Kuliński et al. - point-by-point response to the editors and reviewers’ comments\nWe thank the reviewers for their overall positive opinion of our manuscript. We have\naddressed all the issues requested, which substantially improved the manuscript.\nThe major limitation of the initial version of our manuscript raised by the reviewers was\nthe lack of direct evidence of the AID involvement in the DIS3-dependent translocations.\nIn a new version, we have introduced new crucial experiments and bioinformatic analysis,\nwhich significantly contributed to our statement that the mechanism of oncogenic action\nof DIS3 mutation is indeed AID-dependent.\nFirst, we demonstrate that in AID-expressing CH12F3.2a cells, a population of cells exists\nthat, upon activation, exhibits an increased number of double-stranded DNA breaks. This\nnumber is slightly increased upon expression of the MM DIS3 mutant allele. Notably, this\ngenome instability readout is strictly AID-dependent, as, regardless of the expression of\nthe DIS3 variant, a very small population with increased dsDNA breaks is observed in\nAID-KO CH12F3.2a cells.\nSecond: Typically, during somatic hypermutation (SHM), mutations are\npredominantly found on the non-transcribed strand of the transcription bubble, a\nconsequence of asymmetric DNA lesion deposition by AID. In contrast, class switch\nrecombination (CSR), which depends on the generation of double-stranded DNA\nbreaks, requires AID activity on both DNA strands. It has been postulated that DIS3\ncontributes to the targeting of AID, particularly to the template strand.\nFollowing the suggestion of Reviewer 1, we investigated the effect of DIS3 mutation\non the accumulation of somatic mutations in plasmacytoma samples, utilizing both\nDNA sequencing and strand-specific RNA sequencing data from B cells activated in\nvitro. Strikingly, we observe the expected strand asymmetry of mutations in wild-type\nmice; however, in the case of the DIS3G766R mutant, this asymmetry is lost, and\nmutations are equally distributed on both strands of the transcription bubble. This is\nprecisely the pattern one would expect in the context of increased AID-dependent\ntranslocations, which depend on double-stranded DNA breaks.\nREVIEWERS COMMENTS\nReviewer #1 (Remarks to the Author):\nActivation Induced cytidine Deaminase (AID) catalyzes Class switch recombination (CSR)\nand somatic hypermutation (SHM) to promote antibody gene diversification. How AID\nidentified the IgH locus to promote antibody gene specific SHM without mutating\nvarious other oncogenes in the B cell genome is an important and exciting topic of\nresearch. Previous studies have established that AID functions by associating with the\nexoribonuclease RNA exosome complex with both associating with a stalled RNA\npolymerase II Complex. While stalled RNA polII recruits AID, the RNA decay activity of\nstalling and stimulate RNA polII elongation and provide single strand DNA substrates for\nAID. Loss of RNA exosome activity lead to reduced AID activity on the IGH locus while\ncatalytically dead RNA pol II (DIS3-depleted) lead to loss of CSR and increase in IgH\nchromosomal translocation (Laffleur et al , 2021). In these studies, multiple\ntranscriptional effects on the noncoding genome of B cells were were observed due to\nRNA exosome loss of function. In this exciting study, the authors have identified a gain\nof function DIS3 mutant (DIS3766R) that promotes IgH chromosomal translocation that\nare associated with B cell transformation due to increased association with AID on\nchromatinized DNA, without affecting other aspects of RNA exosome function on B cell\ntranscriptome. This study provides important evidence of the role of chromatin\nassociated RNA in recruiting AID to its target sites and expands our knowledge of the\nmechanism of AID targeting. The identification of the DIS3766R mutation, that separates\nRNA exosome function in general cellular RNA degradation from its role in AID\noncogenic activity, will be a widely used information in the AID targeting and B cell\nmalignancy fields. Although many oncogenic mutations are identified in various B cell\ncancers, rarely are they followed up properly to establish their mechanism in malignant\ntransformation. In this regard, this study is unique. The study has been done with\nsignificant experimental I propose publication of this manuscript. Some revisions, as\nsuggested below, may improve the manuscript and clarify some important points.\nWe thank the reviewer for the positive opinion about our paper\n1. The authors could reconsider the title of the manuscript. The association of the\nmutation with multiple myeloma is important but could be misleading. These AID\nassociated mutations, most likely, occur in the germinal center and persist in the plasma\nB cells (that are associated with MM). CSR is also unperturbed for the specific mutation\nbeing investigated here. Thus, focusing on the main point- Oncogenic DIS3 mutation\nsupports AID activity to cause chromosomal translocations- maybe more accurate.\nThis is a suggestion and authors can decide what they think is the best.\nWe thank the reviewer for the thoughtful suggestion regarding the manuscript title. In\nresponse, we have revised the title to:\n“DIS3 Mutations Enhance AID-Driven Translocations During B-Cell Activation, Promoting\nTransformation to Multiple Myeloma.”\nThis revised title highlights the central mechanistic finding — that DIS3 mutations\npotentiate AID-dependent genomic instability during B-cell activation — while avoiding\nthe implication that class switch recombination (CSR) is functionally impaired. We have\nchosen to retain the reference to “multiple myeloma” to emphasize the disease context\nand clinical relevance of our study.\n2. In fig. 1F, the authors show increased R-loops over promoters. Analyses of R-loop\nstatus at promoters of AID off-target genes would be better to show here (as an\nadditional ub figure), with an example of one IGV track of the data that reflects the\nquality of the DRIP-seq.\ncontrols. However, we believe that incorporating these additional panels into Figure 1\nwould disrupt the logical flow of the main text and figure narrative. The relevant\nanalyses, including R-loop profiles over AID off-target genes and representative IGV\ntracks demonstrating DRIP-seq quality, are already presented in Supplementary Fig. S6,\nwhich we believe appropriately complements the main figure without redundancy.\n3. In figure 3, the authors demonstrate the translocation in the DIS3+/G766R mutation.\nWhat are the targets of these translocations and are there microhomologies in the\ntranslocation junctions.\nMicrohomology at translocation junctions:\nTo analyze potential microhomologies at translocation junctions, we initially faced\nlimitations due to the DNA sequencing data, particularly mate-pair libraries, which often\nprevent precise breakpoint identification. To address this, we curated the data to identify\nexact chromosomal breakpoints, representing a subset of mapped translocations.\nHomologous sequences immediately adjacent to these breakpoints were then\nannotated. Nucleotide-resolution mapping revealed microhomology-mediated repair in\nboth genotypes, consistent with AID-dependent B-cell neoplasms and MM79,80.\nNotably, DIS3^G766R/+^ cells exhibited slightly longer microhomologies (7 bp vs. 5 bp\nin WT), suggesting increased reliance on alternative end joining (a-EJ).\nFigure S8. (C) The distribution of microhomology lengths at chromosome breakpoints, showing slightly\nlonger stretches in DIS3G766R/+ cells (median 7 bp vs. 5 bp in WT).\nTranslocation targets:\nMapped translocations involve immunoglobulin loci and known AID-targeted genomic\nregions, with many partners overlapping highly transcribed genes and super-enhancer\nregions in B cells, consistent with previous observations in plasmacytomas and multiple\nmyeloma. For example, Myc translocations were identified and are shown in the S6.\nHowever, given the sequencing depth and mate-pair library protocol, our approach was\nnot optimized for exhaustive detection of clonal driver rearrangements. Instead, the\npipeline captures a broad spectrum of statistically supported events, reflecting the\noverall translocation landscape and sequence specificity associated with the DIS3G766R\nmutation. Accordingly, our study emphasizes the mutator phenotype and the\nmechanistic consequences of DIS3 dysfunction rather than the physiological impact of\nspecific deregulated signaling pathways.\nWe thank the reviewer for this insightful suggestion, which, as already indicated, allowed\nus to strengthen the support for AID dependence of translocations observed in\nDIS3G766R/+ mice.\nTranslocation breakpoints themselves do not provide strand-specific information relative\nto transcription. To address this limitation, we leveraged both plasmacytoma DNA\nsequencing and strand-specific RNA sequencing from in vitro-activated B cells. Rather\nthan focusing solely on breakpoints, we performed a genome-wide analysis of somatic\nmutations.\nSomatic SNVs were identified in plasmacytoma DNA, with all C→T transitions and C→G\nC→A transversions cataloged and assigned to the corresponding genomic strand (+ or\n−). Strand-specific RNA-seq signals were then aggregated in a strand-aware manner to\nevaluate SHM asymmetry.\nThis approach revealed clear differences in strand-specific mutation patterns between\nDIS3 genotypes, as described in our response to the editor and now shown in Fig. 6I of\nthe revised manuscript. In the context of DIS3^G766R^, AID loses most of its preference\nfor the coding strand, exhibiting nearly equal activity on both strands which fits into our\nproposed model.\nFigure 6. Mutant DIS3 stalling on chromatin-bound substrates, resulting in increased AID-dependent\nDNA damage in activated B-cells. (I) Directional meta-analyses of RNA-seq signals from day 3\nactivated B cells over cytidines somatically mutated in plasmacytoma samples of DIS3G766R/+ mice show\ndecreased specificity of AID mutagenic activity for the coding strand compared to DIS3+/+ counterparts.\n4. In figure 6, authors suggest that stalled DIS3G766R mutant protein occur on\nchromatin associated RNAs. Are these the same RNAs that accumulate at R-loops at the\npromoters? How are the authors corelating stalling DIS3 on chromatinized RNA with\nthem being R-loop driving.\nand RNA associated with chromatin was not sufficiently clear and may have caused\nconfusion. At the same time, due to the already quite complex nature of the manuscript,\nwe did not want to introduce too many distinctions. In this revised version, we more\nclearly state that R–loop–engaged RNAs represent only a fraction of DIS3 substrates at\nthe chromatin (Figure 1), including those associated with chromatin. It starts with the\ninital RNA-seq and DRIP seq data, and is also indicated in the DamID-related data\npresented Figure 6.\n5. Finally, in the discussion that authors could also refer to the mechanism of\nstabilization of single strand DNA through secondary DNA structures (PMID: 31509023)\nand AID phosphorylation and association with single strand DNA binding RPA (PMID:\n19442251). These are the fundamental mechanism that DIS3 may collaborate with to\npromote oncogenic translocations.\nWe thank the reviewer for this excellent suggestion. We have incorporated discussion of\nthese mechanisms, referencing the stabilization of single-stranded DNA through\nsecondary DNA structures and the role of AID phosphorylation and its interaction with\nRPA. Including these aspects has made our discussion of the proposed model more\ncomprehensive, providing additional mechanistic context for how mutant DIS3 may\ncooperate with established AID regulatory pathways to promote oncogenic\ntranslocations.\nReviewer #2 (Remarks to the Author):\nIn this manuscript, Kulinsky et al examine the role of DIS3, a key component RNA\nexosome complex in driving mutations and translocations in multiple myeloma. The\nauthors knocked-in a G766R mutation, a clinically relevant DIS3 variant, into the\nendogenous DIS3 locus. They find that while mice homozygous for the mutation is\nembryonically lethal, heterozygous DIS3(G766R) mice develop normally, allowing them\nto examine the contribution of this mutation to cellular transformation. The authors find\nthat the heterozygous DIS3(G766R) mutations drive murine plasmacytomas in a\npristane-induced model, and whole-genome sequencing revealed increased IgH-\ndependent chromosomal translocations. They also find that mutated DIS3 accumulates\non chromatin bound RNA, and especially at sites that are known off-target AID sites.\nEmploying a CH12 B lymphoma line, the authors demonstrate that there is increased\nDNA damage in cells expressing DIS3(G766R), and they also demonstrate increased\nphysical proximity of Igh and protooncogenes. These results prompted the authors to\npropose a model wherein DIS3 mutations enhance AID promiscuity to drive IgH\ntranslocations and multiple myeloma development.\nThe mechanisms that protect the B cell genome from AID-induced collateral damage is\nof much relevance to our understanding of B cell transformation. The role of the RNA\nexosome complex in AID targeting has been extensively studied in the context of class\nswitching and somatic hypermutation, but how (and if) mutations in RNA exosome\nproteins found in patients with multiple myeloma contribute to cellular transformation is\npoorly understood. Thus, generating a mouse model that recapitulates some aspects of\nthe human disease is of potential interest.\nWe thank the reviewer for the interest in our work.\nHowever, the authors need to address several issues to strengthen the work.\n1. The major claim of the authors based on the data presented is that the translocations\nare AID-dependent. However, other than indirect evidence based on sequence motifs\netc, there is no data showing that this is indeed the case. The authors should absolutely\nprovide data to show that the translocations they pick up are indeed AID-dependent.\nAID-deficient mice are generally available and there are multiple ways to delete AID from\ncells/cell lines. Without this direct experimental evidence, it is difficult to evaluate the\nmajor findings of the study.\nWe thank the reviewer for this essential comment, which helped us strengthen the key\nmechanistic conclusion of the study. As requested, in the revised version of the\nmanuscript we now provide direct experimental evidence demonstrating that the\ngenome instability caused by DIS3 mutations is AID-dependent.\nTo address this point, we employed the CH12F3-2A cell line in both AID-proficient\n(AID+/+) and AID-deficient (AID–/–) backgrounds. Cells were transduced with lentiviral\nconstructs expressing either DIS3WT or DIS3G766R, and γ-H2AX levels were quantified as a\nmarker of DNA double-strand breaks. Consistent with our primary B-cell data, DIS3G766R\nexpression caused a statistically significant increase in γ-H2AX signal compared to\nDIS3WT in AID+/+ cells (Fig. 6H). Crucially, this effect was strictly AID-dependent, as AID–/–\ncells expressing DIS3G766R did not exhibit elevated γ-H2AX levels. These findings\ndemonstrate that AID activity is required for the accumulation of DNA damage in the\npresence of mutant DIS3. No differences were observed in non-activated CH12F3-2A\ncells regardless of genotype.\nDNA damage in activated B-cells. (H) Quantification of high-throughput confocal imaging of DNA\ndouble-strand breaks, defined as the number of γ-H2AX foci per nucleus, in naïve and activated\nCH12F3-2A cells showed that DIS3G766R expression led to elevated γ-H2AX levels in an AID-dependent\nmanner. (…)statistical analyses were performed using the Wilcoxon test.\nFull experimental details, including biological replicates and representative controls are\npresented in Figure S8B.\nFigure S8. DIS3–AID interaction and Dis3G766R-dependent DNA damage in B cell. (…) (B)\nQuantification of high-throughput confocal imaging of DNA double-strand breaks, defined as the\nnumber of γ-H2AX foci per nucleus, in naïve and activated CH12F3-2A cells showed that DIS3G766R\nexpression led to elevated γ-H2AX levels in an AID-dependent manner.\n2. The authors should do a thorough analysis of the germinal center response of the\nknock-in mice. Are there altered GC B cell responses at homeostasis and after\nimmunization, altered LZ:DZ ratio, effects on affinity maturation?\nWe thank the reviewer for this important point. We have cytometrically analyzed the\ngerminal center (GC) response in both naïve and immunized DIS3+/G766R and wild-type\nmice. Following immunization, we observed an increase in the GC B-cell population in\nthe spleens in both genotypes. However, we did not detect any significant difference in\nthe LZ:DZ (light zone to dark zone) ratio between genotypes, indicating that the\norganization of the GC response remains intact. These results are now included in the\n7\nrevised version of the manuscript Fig2 D-E and supplementary Fig. S4.\nFigure 2. Unaffected immunoglobulin class switch recombination in B and MM cells with DIS3 MM\nvariants. (…) (D) Flow cytometric analysis revealed a significant expansion of splenic GC B cells\n(CD95+ GL7+) in immunized versus naïve mice, with a magnitude comparable between DIS3+/G766R and\nWT genotypes. (E) DZ (CXCR4high CD86low) and LZ (CXCR4low CD86high) composition was unaffected.\nFigure S4. Cytometric phenotype of splenic B-cells in Dis3+/+ and Dis3G766R/+ knock-in mice. (A-B)\nRepresentative flow cytometry analysis of primary speenocytes that exemplify the gating strategy.\nSplenic germinal center (GC) B-cells are defined as CD95 and GL7 positive, with dark zone (DZ) cells\nidentified as CXCR4high and CD86low and light zone (LZ) as CXCR4low CD86high. (C) B-cells in Dis3+/+\nand Dis3G766R/+ knock-in mice display comparable DZ:LZ ratios (~2). Error bars represent SEM points\nshow results of all tested individuals.\n3. The manipulated CH12 cells (Fig 6) are very poorly characterized. Are these clones of\nCH12 cells that have the KI mutation? Or are pools being analyzed? What are the DIS3\nprotein levels? What happens when these cells are stimulated? Do they switch like the\nmore widely used CH12F3-2B subclone?\nWe thank the reviewer for this valuable comment. Indeed, the initial figure description of\nthe CH12 cell line was imprecise, and we appreciate the opportunity to clarify this point.\nThe cell line used in our experiments is the CH12F3-2A clone, which is precisely the\ncommonly used B-cell model referred to by the reviewer. This clone is widely employed\n8\nto study class switch recombination (CSR) and B-cell activation. The figure label has now\nbeen corrected accordingly, and the Methods section has been updated to describe the\ncell line accurately.\nIn our experiments, we analyzed pooled populations of CH12F3-2A cells stably\nexpressing either wild-type or G766R DIS3–Dam fusions; these were not clonally\nselected. The functionality of the CH12F3-2A cells was verified, including their ability to\nundergo class switching upon cytokine stimulation, comparable to the more widely used\nCH12F3-2B subclone. These experimental details are now clearly specified in both the\nfigure legend and the Methods section.\nRegarding the DIS3 protein expression, for DamID experiments, it is essential that the\nDam-fusion proteins are expressed at very low levels. To achieve this, we utilized leaky\nexpression from an uninduced inducible promoter, resulting in protein levels that are\nbelow the detection limit of Western blotting and immunofluorescence microscopy. This\nlow expression is critical, as Dam is a highly active enzyme, and even moderate\nexpression can cause non-specific, saturating methylation. In our system, the fusion\nprotein was expressed from the pLgw V5-EcoDam vector under the minimal heat-shock\npromoter, ensuring minimal activity. EcoDam specifically methylates adenine in GATC\nsequences, a modification not naturally found in mammalian genomes, allowing precise\nmapping of protein–DNA interactions. The specificity of the obtained DamID signal was\nvalidated by digestion of negative control samples, confirming that the detected\nmethylation reflected targeted binding events.\nFor Fig 3D, how do the authors know that they are indeed detecting AID? Without these\ncontrol data, it is nearly impossible to evaluate the data presented.\nWe assume that this comment refers to Fig. 6D. All required controls mentioned by the\nreviewer are present in Supplementary Fig. S8A (which corresponds to the previous\nversion of Fig. 6F). The misleading reference in the Fig. 6D legend has now been\ncorrected to reflect what is accurate.\nReviewer #3 (Remarks to the Author):\nIn this manuscript, the authors examine the functional effects of a heterozygous\nhypomorphic Dis3 mutation in a novel mouse model. They find that the mice, particularly\nmales, are smaller, the B-cell less proliferative and susceptible to pristane induced\nplasmacytomas with increased chromosomal translocations. This is consistent with their\nprovocative ref57 which postulates that Dis3 mutations are an early event which\npredispose to IgH translocations but are subsequently selected against because of the\nanti-proliferative effects. The current manuscript fully supports this novel and original\nhypothesis, but leaves a number of unanswered questions. There are a large number of\n9\nexperiments with excellent bioinformatic analyses.\nWe thank the reviewer for their thoughtful and positive assessment of our work. We\nappreciate the recognition of the novelty and significance of our findings, as well as the\nefforts we made in our experimental and bioinformatic analyses.\nThere are some minor points:\nTable S12 and S13 are supposed to list DIS3G766R/+ and WT plasmacytoma\ntranslocations respectively. I could not identify the IgH translocations shown in the\nCircos plots in Figure 3D, and I wonder if the wrong tables were uploaded.\nWe thank the reviewer for pointing this out. We have carefully checked and confirmed\nthat the IgH translocations shown in the Circos plots of Figure 3D are present in the\noriginal BEDPE files. To improve clarity, we have now uploaded two additional BEDPE\nfiles containing only filtered translocations originating from the IgH locus:\n Table S14: BEDPE file with DIS3G766R/+ plasmacytoma translocations originating\nfrom the IgH locus.\n Table S15: BEDPE file with DIS3 WT plasmacytoma translocations originating from\nthe IgH locus.\nThese new files should make it easier to directly identify the IgH translocations visualized\nin Figure 3D\nIt would be helpful to highlight in the table the IgH translocations shown in the Circos\nplots. It is not clear how many plasmacytomas were sequenced, and whether each had a\nunique IgH translocation, only some had an IgH translocations, as in humans, or if some\nhad multiple IgH translocations. Were cell lines established? Are the tumor and\ntranslocations polyclonal, clonal or subclonal?\nPlasmacytomas isolated from five individual mice were subjected to sequencing—three\nfrom DIS3^G766R/+ animals and two from DIS3^+/+ controls. The translocation profiles\nobserved were largely subclonal /polyclonal in nature, consistent with the\nheterogeneous composition of primary tumor samples.\nAre any translocations recurrent? Do they drive the expression of adjacent oncogenes?\nDO they appear to be driver or passenger mutations? It is somewhat surprising that Igh-\nMyc translocations were not observed as they are almost universal in other pristane-\ninduced plasmacytoma models.\nNone of the identified translocations were strictly recurrent; however, several genomic\nregions, most notably the Igh locus, acted as translocation hotspots, repeatedly linking\nto distinct and sequence-unrelated loci across samples.\nGiven the sequencing depth and the mate-pair library protocol used, our approach was\nnot optimized for exhaustive detection of clonal driver rearrangements. Instead, the\nanalytical pipeline was designed to robustly capture a broad spectrum of statistically\n10\nsupported translocation events, including subclonal ones, to reflect the overall\ntranslocation landscape and sequence specificity associated with the DIS3G766R\nmutation.\nOur study thus focuses on the mutator phenotype and mechanistic implications of DIS3\ndysfunction rather than on the oncogenic potential or physiological impact of specific\nrearrangements.\nAlthough canonical Igh–Myc translocations, frequently observed in BALB/c\nplasmacytoma models, were not detected in our dataset, we did, however, identify\ntranslocations involving the Myc locus (Fig. S6). The absence of classical Igh–Myc events\ncan stem from technical factors related to sequencing depth, mate-pair library structure,\nor the necessity to filter repetitive or blacklist regions during data processing.\nAlthough not critical to support their main hypothesis additional characterization of\nDIS3G766R/+ would be nice. C57Bl/6 spontaneously develop monoclonal gammopathy\nwith age. Is this accelerated in DIS3G766R/+?\nWe thank the reviewer for this suggestion. As demonstrated in supplementary Fig. S1E,\nwe examined the proteinograms in DIS3+/+ and DIS3G766R/+ mice. At the ages analyzed,\nwe did not observe any evidence for acceleration of monoclonal gammopathy compared\nto wild-type C57Bl/6 mice. While C57Bl/6 mice can spontaneously develop monoclonal\ngammopathy with age, our data indicate that the DIS3G766R/+ mutation does not markedly\naccelerate this process. We note, however, that we did not perform an extensive clinical\ncharacterization of gammopathy, as this was beyond the primary focus of the current\nstudy.\nFigure S3. (…) (H) No significant changes in frequencies of class switching in Dis3+/+ and DIS3G766R/+\nday 0, day 3 and day 5 in vitro activated B-cells, as determined by the analysis of immunoglobulin heavy\nand light chain classes expression in RNA-seq experiments.\nIs there any change in isotype distribution in DIS3G766R/+ compared to WT?\nWe did not detect any genotype-dependent differences in isotype distribution. To\naddress this question, we analyzed the expression of immunoglobulin heavy- and light-\nchain classes in in vitro–activated primary B cells collected at days 0, 3, and 5 post-\nactivation using RNA-seq. The relative expression levels of each isotype were\ncomparable between DIS3+/+ and DIS3G766R/+ cells at all examined time points (Fig. S3H).\nThese results, together with, indicate that the DIS3G766R mutation does not alter class-\nswitch recombination efficiency or isotype usage.\nday 0, day 3 and day 5 in vitro activated B-cells, as determined by the analysis of immunoglobulin heavy\nand light chain classes expression in RNA-seq experiments.\nThe English can be improved:\n“Taken together, by introducing Dis3G766R mutation in a heterozygous setting, we\nrecapitulate the clinical markup of MM cells”\nI am not sure what they are trying to say, but the mouse does not reproduce clinical\nfeatures of human MM (Anemia, bone disease, renal lesions).\nWe agree with reviewer #3 that the statement was misleading and has been changed in\nthe revised version of the manuscript.\nWe thank the Reviewers for their positive assessments and constructive feedback, which have greatly\nstrengthened the manuscript. We are pleased that Reviewers #1 and #2 find the work ready for\npublication. We also sincerely appreciate Prof. Leif Bergsagel (Reviewer #3) for sharing his name and\nfor his detailed, expert input from a clinical perspective, which has been invaluable both in this round\nand in previous reviews. Below, we address all points raised.\nReviewer #1 (Remarks to the Author):\nThe authors have addressed many of my queries with experimental data and well thought out\narguments. This is an important study that clearly shows the role of DIS3 in stimulating AID's DNA\ndeamination activity on chromatinized DNA. It will be well followed in the B cell malignancy, RNA biology\nand antibody somatic hypermutation fields. I propose publication.\nResponse:\nWe sincerely thank Reviewer #1 for this enthusiastic endorsement and for recognizing the study's\nimpact across these key fields.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed all the issues raised by this reviewer. This is now an important contribution\nto the field.\nResponse:\nWe are grateful to Reviewer #2 for confirming that all concerns have been resolved and for highlighting\nthe work's contribution.\nReviewer #3 (Remarks to the Author):\nThe authors have responded constructively to the previous critique, substantially improving the\nmanuscript. The findings reported are highly novel and significant.\nThe following minor points remain.\nFigure S1E. Serum Protein Electrophoresis in wildtype C57Bl/6 is not expected to show a change in\nthe protein fractions reported in this figure. The authors should consult with someone proficient at\nanalyzing SPEPs (e.g., a hematologist or lab technician). The abnormality seen with age in C57Bl/6\nmice is a very faint monoclonal band in the beta or, more commonly, the gamma globulins. It is not of\nsufficient size to increase the level of total globulins in the fraction. About 30% of 1 year old C57Bl/6\nmice will have a faint monoclonal band detected on SPEP.\nResponse:\nWe thank Reviewer #3 for the insightful comment regarding the interpretation of SPEP results in\nC57Bl/6 mice. The serum protein electrophoresis analyses were performed and evaluated by a certified\ntechnician at a commercial diagnostic laboratory with expertise in SPEP interpretation.\nOur objective was to assess overt monoclonal gammopathy consistent with multiple myeloma\ndevelopment rather than to detect subtle age-associated monoclonal bands. We acknowledge that faint\nmonoclonal bands can occur in a proportion of aged C57Bl/6 mice and may not significantly alter total\nglobulin fractions.\nAs the plasmacytoma induction model proved to be the most robust and informative system for\nmechanistic analyses, further investigation of subtle age-related SPEP alterations was not pursued as\nit was beyond the scope of this study and our expertise.\nTable S12, S13, S14, S15. These bedpe files have a terminal column with numerical entries such as\n;3/4;5/6. I suspect this is supposed to be left split reads/discordant reads; right split reads/discordant\nreads supporting the presence of the SV. It is not clear to me why all 4 numbers reported for each SV\nare identical, please check that this is correct. Furthermore, they appear to use a very low cutoff to\nreport SV (3 reads supporting SV). This will be more sensitive to subclonal SV, but also introduces a lot\nof noise. I suspect a higher cutoff will provide a cleaner analysis, although they have convincingly made\ntheir point with their current analysis (if less than ideal).\nWe thank the reviewer for the careful evaluation of Tables S12–S15 and for pointing out the lack of\nclarity.\nIn the BEDPE files (SVDetect output), the relevant fields are as follows:\nField 7 – SVname: unique structural variant identifier (sequential ID + sample + SV type + annotation).\nField 8 – score: SVDetect weighted confidence score after filtering (range 0–1; higher values indicate\nstronger support).\nField 11 – NumberOfPairsAfterFiltering/previousNumberOfPairs: number of supporting discordant read\npairs after filtering relative to the initial number detected (e.g., 25/30).\nThe filtering steps include strand filtering, order filtering, and insert size filtering. For clarity, we now\nreport only the initial and final numbers of supporting reads. In many cases, the number of supporting\nreads remains unchanged after filtering because conservative read filtering had already been applied\nat the mapping stage. We have corrected the tables, introduced headers, and expanded their\ndescriptions in the Supplementary Information to improve clarity.\nRegarding the cutoff for SV detection, our approach was not optimized for exhaustive identification of\nhigh-confidence clonal driver rearrangements. Instead, the analytical pipeline was intentionally\ndesigned to capture a broad spectrum of statistically supported translocation events, including\nsubclonal variants, in order to characterize the overall translocation landscape and sequence specificity\nassociated with the DIS3G766R mutation.\nWe additionally tested analyses using higher read-support cutoffs and did not observe significant\ndifferences in the overall conclusions. Therefore, we retained the more inclusive threshold to preserve\nsensitivity for subclonal events relevant to the mutator phenotype under investigation.\nOne of the IgH SV they report in the G766R Dis3 plasmacytomas has a well-supported breakpoint (67\nreads) 170kb from Tnfrsf17 (Bcma), a gene that is sometimes similarly dysregulated in human\nmyeloma. It would be nice if they can demonstrate over-expression of Tnfrsf17 in the sample with this\ntranslocation.\nResponse:\nWe appreciate this insightful suggestion and the potential link to human myeloma. The breakpoint\nlocated ~170 kb from Tnfrsf17 (Bcma) is indeed notable.\nUnfortunately, the samples were collected exclusively for DNA-based analyses, and the animals were\neuthanized in accordance with ethical requirements at the time of collection, so no RNA material is\navailable to assess Tnfrsf17 expression.\nWhile evaluating expression would be informative, our study is focused on the mutational landscape\nand mechanistic consequences of DIS3G766R dysfunction rather than on the oncogenic potential of\nindividual structural variants. Therefore, this analysis falls beyond the scope of the current work.\nI am unable to find the MYC SV shown in Figure S6E in Table S12. The SV in S6B,C,D,E do not overlap\nDRIPseq peaks as in Figure 4K and S6A. It is not clear how they support their argument.\nResponse:\nThe MYC SV shown in Figure S6C is the following:\nchr11 109011984 109012059 chr15 61984826 61984901\nSVid_7676_Het_57p_TRANSLOC;UNBAL 1 . . 14/14\nAlthough this translocation reached a significant SVDetect confidence score and is supported by 14\nhigh-quality discordant read pairs, it did not pass the additional filtering criteria we applied for\nbreakpoints included in the meta-feature analysis.\nThe examples of thranslocations presented in Supplementary Figure 6 were selected as representative\nloci of potential biological relevance (e.g., immunoglobulin light chain variable regions, known biological\ntarget - Myc, and selected intergenic/non-coding regions). While not all breakpoints directly overlap\nDRIP-seq peaks, they display DRIP-seq or RNA-seq signal enrichment at or in close proximity to the\ntranslocation sites. This is in accordance with the meta-feature analysis shown in Fig. 4.\nFinally, breakpoint mapping by mate-pair sequencing has limited resolution, and only a subset of\nmapped SVs define the exact DNA breakpoints. Therefore, absence of direct overlap does not exclude\nspatial proximity to R-loop–enriched regions.\nI am unable to access the GSE155631. Be sure it contains the older mate-pair, and newer whole\ngenome data. In my experience mouse whole genome and mate pair DNA sequencing data is submitted\nto SRA, not GEO.\nWe have confirmed GSE155631 accessibility (public as of 11.02.2026) and SRA (accession\nSRP275679, BioProject PRJNA650522). We have revised the data accessibility section to\naccommodate\nResponse:\nWe have submitted both the raw sequencing data to the SRA and the processed analysis results\nthrough GEO, taking advantage of the common submission process they offer to ensure that all\naccession numbers are cross-referenced between databases.\nWe have confirmed that GSE155631 is publicly accessible (as of 11 February 2026). The raw data are\navailable in SRA under accession number SRP275679, associated with BioProject PRJNA650522.\nWe have revised the Data Availability section to explicitly include the SRA and BioProject accession\nnumbers."}]

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