Targeting BMI-1 in B cells restores effective humoral immune responses and controls chronic viral infection

Di Pietro, Andrea; Polmear, Jack; Cooper, Lucy; Damelang, Timon; Hussain, Tabinda; Hailes, Lauren; O’Donnell, Kristy; Udupa, Vibha; Mi, Tian; Preston, Simon; Shtewe, Areen; Hershberg, Uri; Turner, Stephen J.; La Gruta, Nicole L.; Chung, Amy W.; Tarlinton, David M.; Scharer, Christopher D.; Good-Jacobson, Kim L.

doi:10.1038/s41590-021-01077-y

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Published: 29 November 2021

Targeting BMI-1 in B cells restores effective humoral immune responses and controls chronic viral infection

Nature Immunology volume 23, pages 86–98 (2022)Cite this article

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Abstract

Ineffective antibody-mediated responses are a key characteristic of chronic viral infection. However, our understanding of the intrinsic mechanisms that drive this dysregulation are unclear. Here, we identify that targeting the epigenetic modifier BMI-1 in mice improves humoral responses to chronic lymphocytic choriomeningitis virus. BMI-1 was upregulated by germinal center B cells in chronic viral infection, correlating with changes to the accessible chromatin landscape, compared to acute infection. B cell-intrinsic deletion of Bmi1 accelerated viral clearance, reduced splenomegaly and restored splenic architecture. Deletion of Bmi1 restored c-Myc expression in B cells, concomitant with improved quality of antibody and coupled with reduced antibody-secreting cell numbers. Specifically, BMI-1-deficiency induced antibody with increased neutralizing capacity and enhanced antibody-dependent effector function. Using a small molecule inhibitor to murine BMI-1, we could deplete antibody-secreting cells and prohibit detrimental immune complex formation in vivo. This study defines BMI-1 as a crucial immune modifier that controls antibody-mediated responses in chronic infection.

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Main

Chronic viral infections, such as human immunodeficiency virus and hepatitis C, are long-standing global health burdens that have evaded control by an antibody-mediated vaccine. The inability to clear virus from the body can cause irreparable and potentially fatal organ damage if left untreated. SARS-CoV-2 is the newest virus with the potential to cause a persisting infection¹, with severe disease correlating to dysregulation in antibody responses². Efficiently treating patients who battle to clear the infection has proven problematic. Thus, it is critical to understand why the immune system fails to produce effective adaptive responses to chronic virus. While progress in this area has focused on T cell dysfunction, the mechanistic changes that lead to sustained B cell dysfunction have remained elusive.

Antibody production is a critical component of effective antiviral immune responses. During a T cell-dependent response, antigen-activated B cells form an early wave of antibody-secreting cells (ASCs), followed by a germinal center (GC) reaction within which B cells undergo affinity maturation. The generation of a high-affinity repertoire is a finely regulated process driven by transcription factors. Among them, c-Myc is transiently expressed in the GC light zone (LZ) and is responsible for selection of antigen-specific clones for further expansion in the dark zone (DZ)^3,4,5, followed by differentiation into high-affinity ASCs and memory B cells. Antibodies help clear infections through several key mechanisms: direct neutralization, non-neutralizing functions and mediating affinity maturation of GC B cells. However, antibodies can be a double-edged sword during chronic infection. Ineffective humoral responses are underpinned by three major alterations: polyclonal B cell expansion^6,7,8, altered B cell memory⁹ and changes to the quantity and quality of antibody. Excessive IgG in the serum leads to an overload of immune complexes⁶ (ICs) that suppress antibody-mediated innate effector functions and counterintuitively limits viral clearance^10,11. IC deposition during chronic infection can cause destructive tissue pathology in both patients and in mice^12,13. However, quantity is not the sole consideration for understanding how antibodies contribute to protection versus pathogenicity, as it does not necessarily correlate with antibody efficacy^10,14. Efficacy arises from neutralizing capacity and Fc-mediated functions, both critical for clearing viral infection^15,16,17,18. Despite the evidence of humoral dysregulation during chronic infection, few therapeutic options exist for modulating humoral responses to recover functionality and drive viral clearance in chronic infection.

Epigenetic modifiers, such as chromatin-remodeling histone modifiers and DNA methyltransferases, are vital regulators underpinning B cell fate^19,20. During chronic viral infection, histone modifiers regulate functional differences between exhausted CD8⁺ T cells and effector memory subsets^21,22. Epigenetic regulators have been shown to dysregulate B cells in lymphomas^23,24 and in autoimmune conditions²⁵. Yet, the B cell-intrinsic roles of epigenetic regulators in humoral dysfunction during chronic infection are undetermined. We hypothesized that the chronic inflammatory environment generated by persistent infections could perturb the expression and activity of epigenetic modifiers in the GC during the production of high-affinity ASCs. Using a combination of deep-sequencing, genetic tools and small molecule inhibitors, we demonstrate that a dysfunctional B cell fate in viral infection is dictated by the histone modifier BMI-1.

Results

BMI-1 is increased in B cells during chronic viral infection

To elucidate how chronic viral infection disrupts B cell differentiation and identify key intrinsic determinants of dysfunction, we used the comparative lymphocytic choriomeningitis virus (LCMV) model. C57BL/6 mice were infected with either LCMV-WE or LCMV-Docile, two closely related strains of LCMV, to induce either an acute (WE) or chronic (Docile) infection. Using this model, we investigated whether chronic infection resulted in changes to the epigenomic landscape within responding B cells. GC B cells (CD19⁺IgD^loCD95⁺CD38^loCD138⁻) were isolated 14 d after infection and both assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq was undertaken (Fig. 1a). ATAC-seq was used to determine regions of chromatin accessibility within the genome that may be altered during chronic infection and correlated to changes in gene expression as determined by RNA-seq. Principal-component analysis (PCA) showed that samples within the same group (WE or Docile) clustered together. Overall, 889 differentially accessible regions (DARs) were identified between acute and chronic GC B cells, with an overall increase (561 DARs) in accessibility in chronic infection (Fig. 1b,c and Extended Data Fig. 1a–c). The frequency of DARs in the promoter-transcription start site region was 11%, with the majority of DARs within the intergenic and intron regions (Extended Data Fig. 1b). In accordance with increased chromatin accessibility, RNA-seq identified 545 differentially expressed genes (DEGs), with 505 genes upregulated in chronic GC B cells compared to acute GC B cells (Fig. 1d and Extended Data Fig. 1d–h; select ASCs and interferon (IFN)-regulated genes indicated). To determine which of these DEGs may be directly regulated by changes to chromatin accessibility at their loci, overlap analysis of the datasets was performed. A total of 39 DEGs in GC B cells responding to LCMV-WE or LCMV-Docile had corresponding DARs identified by ATAC-seq (Extended Data Fig. 1i,j; several different DARs were identified for individual genes). Ingenuity Pathway Analysis (IPA) was then utilized to gain further insight into the gene networks altered in chronic infection. In accordance with an increase of ASCs in chronic infection, IPA revealed differentiation and quantity of plasma cells, as well as GC B cells, in the top ten disease and biological functions altered across the two conditions (Fig. 1e,f). This was despite excluding CD138⁺ cells from the sort-purified GC B cells used for ATAC-seq and RNA-seq analyses.

**Fig. 1: Gene expression and chromatin accessibility regions in GC B cells during chronic viral infection are distinct from acute viral infection.**

The increase in chromatin accessibility regions led us to hypothesize that epigenetic modifiers that repress gene expression may be dysregulated during chronic viral infection. Two important complexes that are known regulators of B cell fate are the Polycomb repressive complexes (PRCs). PRC1 and PRC2 consist of several subunits with the ability to support and deposit H2AK119Ub1 and H3K27me3, respectively, regulating expression of target genes required for formation and maintenance of GCs^23,24,26. We observed 49 PRC1 targets (Fig. 1g) and 80 PRC2 targets (Extended Data Fig. 1k) with altered chromatin accessibility in chronic GC B cells. In our DEG dataset, 63 PRC1 targets (Fig. 1g) and 74 PRC2 targets were differentially expressed (Extended Data Fig. 1k). Among the overlapped targets from the two datasets, the analysis identified seven altered PRC1 targets (Gfi1, Gnaz, Ifi27, Oas3, Sclo4a1, Stat1 and Tmem184b) and eight altered PRC2 targets (Evc, Gnaz, Max, Mx1, Oas3, Plk2, Stat1 and Xaf1).

We next assessed whether expression of PRC components themselves was altered in chronic GC B cells. Expression of components of the canonical PRC1 (Fig. 1h), non-canonical PRC1, canonical PRC2 or PRC2 co-factors (Extended Data Fig. 1l) were mostly unchanged in chronic infection, with Ring1b, Suz12, Rbbp4 and Bcor decreased, whereas Bmi1 was increased compared to acute GC B cells. The increase in Bmi1 was a surprising finding. Previous reports have not observed BMI-1 in murine GC B cells²⁶, although it has been detected in the LZ of GC in human tonsils²⁷ and is expressed in human ASCs²⁸. BMI-1 is a Polycomb group protein that activates the catalytic component RING1B of PRC1 (ref. ²⁹), which in turn deposits monoubiquitin at lysine 119 of histone H2A³⁰. Functionally, BMI-1 allows stable silencing of gene expression, regulating lineage commitment, self-renewal, proliferation and survival^{31,32,33,34,35,36}. BMI-1 was originally identified as an oncogenic collaborator of c-Myc^37,38 that is upregulated in Myc-induced B cell malignancies³⁹ and myeloma^34,40; however, its role during humoral responses to infection has not been determined.

We proposed that Bmi1 upregulation in chronic GC B cells may be connected to the increase in plasma cell gene networks. To understand murine Bmi1 expression dynamics, we used a tamoxifen-inducible Bmi1^CreERT2Rosa26^EYFP reporter mouse to temporally examine Bmi1 expression on a per-cell basis. We first assessed which B cell subsets express BMI-1 in a tractable, non-infectious model, the hapten carrier 4-hydroxy-3-nitrophenylacetyl-keyhole limpet hemocyanin (NP-KLH). Bmi1^CreERT2Rosa26^EYFP mice were immunized with NP-KLH precipitated in the adjuvant alum, administered with tamoxifen at d3–5 and assessed at d7 to detect the yellow fluorescent protein (YFP) expressed under the control of Rosa26 regulatory sequences in BMI-1⁺ cells (Extended Data Fig. 2a). Flow cytometric analyses revealed that B220^loCD138^hi ASCs upregulated Bmi1 (Extended Data Fig. 2b). We undertook further analysis to determine whether BMI-1-expressing cells were enriched in a particular subset of ASCs. Enhanced YFP (EYFP) expression was modulated in the IgA⁺ and IgG1⁺ populations in secondary lymphoid tissues (Extended Data Fig. 2c) and EYFP⁺ was significantly enriched in plasma cells compared to plasmablasts (Extended Data Fig. 2d).

Bmi1^CreERT2Rosa26^EYFP reporter mice were infected with LCMV-WE or LCMV-Docile and tamoxifen was administered every 24 h from d9–11 (Fig. 1i). Infection with LCMV-Docile induced a significant increase in the frequency of EYFP⁺ lymphocytes, compared to LCMV-WE (Fig. 1j,k). In both GC B cells and in B220^loCD138^hi ASCs, BMI-1 was increased ~threefold in chronically infected mice, compared to acutely infected mice, with a higher proportion within the ASC population (Fig. 1l). Within the GC population, the increase in EYFP⁺ cells in LCMV-Docile-infected mice was localized to the LZ population, whereas DZ cells had an equivalent frequency of EYFP⁺ cells following acute or chronic infection (Extended Data Fig. 2e). Furthermore, when steady-state ASCs were assessed for EYFP expression, EYFP⁺ cells were significantly increased in the bone marrow compared to the spleen and mesenteric lymph nodes (Fig. 1m). Thus, Bmi1 expression was enriched in the ASC population compared to GC B cells, correlating with an increase in tissues with higher frequencies of long-lived plasma cells. The increase in Bmi1 transcript in EYFP⁺ subsets, as well as increased Bmi1 transcript and BMI-1 protein in B cell subsets responding to LCMV-Docile compared to LCMV-WE from wild-type mice, was confirmed by quantitative PCR with reverse transcription (RT–qPCR) and immunoblot analyses (Extended Data Fig. 2f–k). Combined, BMI-1 is conventionally enriched in ASCs and differentially expressed in GC B cells responding to either acute or chronic strains of LCMV.

Bmi1 deletion in B cells accelerates LCMV-Docile clearance

We next asked whether modulation of BMI-1 in B cells in chronic infection determined productive outcomes to viral infection. To delete Bmi1 in mature B cells, we generated Bmi1^f/fCd23^Cre/+ mice by crossing mice that carry floxed alleles of Bmi1 with mice expressing a Cre recombinase under the control of the B cell-specific Cd23 promoter. Bmi1^f/fCd23^Cre/+ and control Cd23^Cre/+ mice were infected with LCMV-Docile and assessed throughout infection. Notably, Bmi1^f/fCd23^Cre/+ mice had significantly reduced viral titers (fivefold change) compared to controls at d28 after infection (Fig. 2a). Time-course analyses demonstrated that viral titers were similar between control and BMI-1-deficient mice at d7; however, a 2.4-fold decrease was detected by d14 (Fig. 2b), concomitant with a significant increase in weight gain compared to Cd23^Cre/+ mice (Extended Data Fig. 3a). At d14 after infection, spleens from Bmi1^f/fCd23^Cre/+ mice were noticeably smaller than control mice (Fig. 2c) and splenocyte numbers were reduced (Fig. 2d). Frequencies of innate, CD4⁺ and CD8⁺ T cell populations were comparable between Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice (Extended Data Fig. 3b–f). Functionally, there was no difference in killing capacity of gp33⁺CD8⁺ T cells (Extended Data Fig. 3g) or production of IFN-γ on a per-cell basis (Extended Data Fig. 3h). Infection with LCMV disrupts splenic architecture^41,42,43, particularly B cell follicles and T cell zones (Fig. 2e; Cd23^Cre/+ mice; spleens from uninfected mice are shown in Extended Data Fig. 3i). In contrast, spleens from Bmi1^f/fCd23^Cre/+ mice displayed intact follicular structures and T cell areas (Fig. 2e; Bmi1^f/fCd23^Cre/+). Thus, depletion of BMI-1 in mature B cells resulted in normalized splenic size and architecture and accelerated viral clearance.

**Fig. 2: Conditional deletion of *Bmi1* in B cells results in accelerated clearance of chronic viral infection.**

Increase in antibody function in the absence of BMI-1

Changes to antibody quality and quantity contribute to ineffective humoral responses during chronic viral infection. Given that B cell-specific deletion of BMI-1 led to accelerated viral clearance, we hypothesized that the efficacy of antibody, either through neutralizing or non-neutralizing effector function, was increased in Bmi1^f/fCd23^Cre/+ mice. To test this, LCMV-Docile-infected Bmi1^f/fCd23^Cre/+ and Cd23^Cre/+ mice were assessed for neutralizing antibody capacity. Neutralizing antibodies formed to chronic LCMV emerge late in the response, increasing approximately 2 months after infection⁴⁴. By contrast, antibodies from Bmi1^f/fCd23^Cre/+ mice had an increased capacity to reduce plaque formation in vitro at d14, compared to antibodies from control mice (Fig. 3a).

**Fig. 3: Pleiotropic function of BMI-1 in the humoral response to chronic viral infection.**

The tuning of antibody quality occurs by glycosylation of a conserved N-linked glycan of the IgG constant region (Fc), which can be dynamically modulated by the pathogen-influenced microenvironment during an immune response^45,46. Shifts in glycosylation can have antiviral or anti-inflammatory effects, regulate complement activation and enhance the effectiveness of neutralizing antibodies^16,47. Therefore, to elucidate the efficacy of antibody produced in BMI-1-deficient mice, we assessed IgG Fc N-linked glycan profiles. While most glycosylation patterns remained unchanged in the absence of BMI-1 (Extended Data Fig. 4a), there was a significant decrease in G2Z1F, whereas the corresponding G2Z1 remained unchanged (Fig. 3b). Afucosylated forms of IgG can increase antibody efficacy⁴⁸, enhance antibody-dependent cellular cytotoxicity (ADCC)⁴⁹ and induce effective innate cell function during chronic viral infection⁵⁰. Specifically, the G2Z1F glycoform has been shown to be enriched in the IgG2c subset and is thought to act as a dampener of this pro-inflammatory isotype⁵¹. To determine whether antibody efficacy was increased by reduction of this glycoform, we compared the ability of antibody from Bmi1^f/fCd23^Cre/+ and Cd23^Cre/+ mice to induce ADCC. Using natural killer (NK) cells from wild-type mice, ADCC was significantly enhanced using sera collected from LCMV-Docile-infected Bmi1^f/fCd23^Cre/+ mice, compared to sera from Cd23^Cre/+ mice (Fig. 3c). Taken together, these data reveal BMI-1 as a key regulator of antibody efficacy in chronic viral infection.

We next investigated the kinetics of ASC production. The frequency of CD19⁺IgD⁺ cells (enriched in naive B cells) was comparable between Bmi1^f/fCd23^Cre/+ mice and controls, as was the ability of CD19⁺IgD⁻ to form GC B cells (Extended Data Fig. 4b–e). At d14, the total number of CD19⁺IgD⁺ and GC B cells was significantly reduced. BMI-1-deficient GC B cells were able to isotype switch to IgG2c and to maintain detectable virus-specific IgG2c (Extended Data Fig. 4f,g). In contrast, B cell-specific deletion of BMI-1 altered the dynamics of ASCs. In LCMV-Docile-infected Cd23^Cre/+ mice, ASC formation peaked at d14 before declining to low levels by d21 (Fig. 3d–f). In comparison, ASC frequency, but not total number, was increased in Bmi1^f/fCd23^Cre/+ mice at d7, before a significant decrease in both frequency and number at d14 compared to controls (Fig. 3f). Notably, there was not a complete absence of ASCs; in fact, the reduction of d14 ASC frequency and number (Fig. 3e) resembled that observed in LCMV-WE (acute)-infected mice. Similarly, GC B cell frequency and number (Fig. 3g) and circulating ICs (Fig. 3h) in BMI-1-deficient mice infected with LCMV-Docile, resembled that observed in control mice infected with LCMV-WE. To confirm that changes to the ASC population were B cell-intrinsic, 1:1 mixed bone-marrow chimeras were established with Ly5.1 and Bmi1^f/fCd23^Cre/+ bone marrow (Fig. 3i). Infected chimeras showed a significant reduction in BMI-1-deficient ASCs compared to Ly5.1 (Fig. 3j). Thus, induction of neutralizing antibodies, augmentation of antibody-dependent effector function and reduction in circulating ICs are consistent with more potent immune responses in LCMV-Docile-infected Bmi1^f/fCd23^Cre/+ mice.

Relationship of c-Myc and BMI-1 in chronic viral infection

Affinity maturation is highly regulated to avoid the survival and expansion of potentially autoreactive or cancerous clones. A critical regulator of the selection process is c-Myc^3,4. Given the role of BMI-1 in Myc-induced B cell malignancies³⁹ and the heterogeneity of both Bmi1 and Myc^3,4 expression in the GC, we hypothesized that BMI-1 and c-Myc may collaborate to regulate the efficacy of the antibody response to infection. EYFP⁺ and EYFP⁻ GC B cells were isolated from BMI-1 reporter mice infected with either LCMV-WE or LCMV-Docile (Fig. 4a). Of note, Myc expression was selectively expressed in the EYFP⁺ GC B cell subset, with a 21-fold increase compared to EYFP⁻ GC B cells (Fig. 4b). The fold change of Myc expression in EYFP⁺ GC B cells responding to chronic infection was considerably higher compared to the acute WE-infected cohort, in which there was a twofold, nonsignificant increase in EYFP⁺ over EYFP⁻ (Fig. 4b). We next investigated whether Bmi1 deletion affected Myc expression. Total GC B cells from LCMV-Docile-infected Cd23^Cre/+ mice had increased expression of Myc compared to LCMV-WE (Fig. 4c). B cell-specific BMI-1 deletion selectively reduced Myc expression in LCMV-Docile-infected mice to the level observed in LCMV-WE infection (Fig. 4c). In contrast, there was no significant change in gene expression of other reported targets of BMI-1, Bcl2l11 (encoding Bim)³⁴ and Pmaip1 (encoding Noxa)³³ in acute or chronically infected Bmi1^f/fCd23^Cre/+ mice (Extended Data Fig. 4h).

**Fig. 4: Regulation of c-Myc in BMI-1-deficient mice.**

Within the GC, c-Myc is localized to the LZ in immunization models^3,4. Given the increase in Myc in chronic infection, we hypothesized that c-Myc would no longer be tightly regulated within the GC. Indeed, histological analyses of Cd23^Cre/+ mice infected with LCMV-Docile revealed c-Myc expression throughout the GC (Fig. 4d and Extended Data Fig. 5a). In contrast, GCs in Bmi1^f/fCd23^Cre/+ mice were mostly absent of c-Myc expression, with only a few cells evidently expressing c-Myc (Fig. 4d,e and Extended Data Fig. 5b). Therefore, conditional deletion of BMI-1 restored regulated expression of c-Myc within the GC.

Given the changes in expression and distribution of c-Myc, we next examined whether somatic hypermutation and/or affinity maturation of GC B cells was altered at d14 post-infection (Extended Data Fig. 5c–f). No clear differences were observed in mutation levels (Extended Data Fig. 5c). To examine affinity maturation in BMI-1-sufficient versus BMI-1-deficient GC B cells, the ratio of non-synonymous to synonymous mutations (N/S) (Extended Data Fig. 5d) and selection pressure (Extended Data Fig. 5e,f) was assessed. The ratio of N/S in the complementarity-determining regions (CDRs) was decreased in GC B cells isolated from Cd23^Cre/+ mice responding to chronic infection, compared to acute infection, suggestive of reduced affinity maturation. In contrast, GC B cells isolated from LCMV-Docile-infected Bmi1^f/fCd23^Cre/+ mice had a N/S ratio comparable to that of Cd23^Cre/+ mice infected with LCMV-WE (Extended Data Fig. 5d). We then quantified the link to selection pressure more precisely with BASELINe (Extended Data Fig. 5e,f). B cell receptor (BCR) sequences from either Cd23^Cre/+ or Bmi1^f/fCd23^Cre/+ mice infected with LCMV-WE had more positive selection in the CDR region than those infected with LCMV-Docile; however, the trend toward positive selection returned in Bmi1^f/fCd23^Cre/+ mice responding to chronic infection, compared to Cd23^Cre/+ mice, although it was not completely restored to that of acute infection. In contrast to the CDR, there was little change in the negative selection detected in the framework regions (Extended Data Fig. 5f). Taken together, affinity maturation seemed to be improved in Bmi1^f/fCd23^Cre/+ mice during chronic infection.

The elevation in BMI-1 and c-Myc expression in chronic GC B cells prompted us to ask whether there was a particular pathogen-influenced microenvironmental change that may induce these differences in B cell transcriptional programs. A key candidate was IL-21, a cytokine that regulates B cell behavior^52,53 and can induce context-specific Myc expression⁵⁴. Notably, IL-21 production by helper T cells in chronic viral infection is substantially higher than in acute viral infection⁵⁵. We therefore tested whether there was a dose-dependent induction of Bmi1 and Myc in in vitro-stimulated B cells, co-cultured with increasing concentrations of IL-21 (Fig. 4f–h). While B cells cultured with low IL-21 concentrations did not increase Bmi1 (Fig. 4f) or Myc (Fig. 4g) over the baseline stimuli (CD40L, IL-4 and IL-5), significant upregulation was evident at high IL-21 levels (Fig. 4f,g). The observed increase was not simply due to a change in plasmablast frequency within the cultures, as there was no significant change in plasmablast frequency between 25–50 ng ml⁻¹ IL-21 (Fig. 4h). Together, these data reveal that the disruption in c-Myc regulation during chronic infection correlated with increased IL-21 and that conditional deletion of BMI-1 restores expression and localization of c-Myc-expression in GC B cells.

H2AK119Ub1 is decreased at key loci in chronic infection

The non-canonical form of PRC1 (which excludes BMI-1) and PRC2 are required for GC formation²⁶. The ability of BMI-1-deficient B cells to form GCs and upregulation of BMI-1 in ASCs, suggest a switch between the non-canonical and the canonical forms of PRC1 to regulate B cell fate. This switch seemed to be disrupted in chronic GC B cells, with upregulation of Bmi1, Myc and ASC-related genes (Fig. 5a). Accordingly, c-Myc-target genes were significantly enriched in chronic GC B cells, compared to the acute gene expression profile (Fig. 5b,c), concomitant with identification of several c-Myc targets within DARs (Fig. 5c).

**Fig. 5: H2AK119Ub1 is decreased at key loci in B cells responding to chronic viral infection.**

Given that the non-canonical PRC1 deposits H2AK119Ub1 at key loci in GC B cells, it was unclear how changes in BMI-1 expression affected PRC1 function, if at all. We first asked whether changes in H2AK119Ub1 and H3K27me3, the histone marks regulated by PRC1 and PRC2, respectively, were altered at key loci in acute versus chronic GC B cells. There was a clear change in H2AK119Ub1 in control GC B cells between LCMV-WE- and LCMV-Docile-infected mice (Fig. 5d). B cells from LCMV-Docile mice had a significant decrease in H2AK119Ub1 at Myc and Prdm1 loci (Fig. 5d), whereas H2K27me3 was unchanged (Fig. 5e). B cell-specific BMI-1 deletion in chronically infected mice elevated H2AK119Ub1 at Myc and Prdm1 loci in unswitched B cells, with a less prominent increase at Irf4 in IgD⁻ B cells. While there was a trend increase in H3K27me3 in the absence of BMI-1, in most cases this change was not significant (Fig. 5e). Thus, conditional deletion of BMI-1 restored monoubiquitination at H2AK119 in chronic B cells to similar levels as control B cells responding to acute viral infection.

To determine whether the BMI-1-dependent change in H2AK119Ub1 regulated gene expression, we performed RNA-seq analysis of acute versus chronic GCB from Cd23^Cre/+ or Bmi1 Cd23^Cre/+ mice (Extended Data Fig. 6a). Using the DEGs identified in both Fig. 1 and Extended Data Fig. 6 datasets to explore whether BMI-1-deficiency restored chronic GC B cell gene expression to resemble that of acute GCB, we identified five distinct signatures (Fig. 5f). BMI-1 deletion partially restored gene profiles to that of control GCB responding to acute infection. Among these five clusters (C1-5), genes listed in C1 and C4 showed similar expression patterns between BMI-1-sufficient acute GC B cells and BMI-1-deficient chronic GC B cells (magnified heat maps; Fig. 5f). Taken together, these results underscore the crucial role of BMI-1 in regulating gene expression in B cells responding to chronic infection.

BMI-1 regulates ASCs in response to NP-KLH immunization

The differences observed in BMI-1 expression and function in B cells responding to acute versus chronic viral infection prompted us to investigate the specific role of BMI-1 during B cell differentiation in a tractable, non-infectious model. Thus, Bmi1^f/fCd23^Cre/+ and Cd23^Cre/+ mice were immunized with NP-KLH in alum and humoral responses were assessed. Histological analyses revealed the loss of splenic IgG1^bright cells at d7 after immunization (Fig. 6a). Correspondingly, there was a significant decrease in the total frequency of BMI-1-deficient ASCs (Fig. 6b), antigen-specific IgG1⁺ ASCs (Fig. 6c) and antibody at d14 and d28 (Fig. 6d,e), although d7 IgG1 and IgM antibody levels were comparable between BMI-1-deficient mice and controls (Extended Data Fig. 6b). As ASCs can arise from either GC-independent or GC-dependent pathways, we assessed whether the defect in ASCs was due to a loss of GC B cells. Bmi1^f/fCd23^Cre/+ B cells formed GCs, isotype switched and produced appropriate DZ and LZ ratios, in a manner comparable to Cd23^Cre/+ mice (Extended Data Fig. 6c–e). The ability of B cells to form GCs in Bmi1^f/fCd23^Cre/+ mice is consistent with a previous study demonstrating that NP⁺IgD^lo B cell could expand after immunization in Bmi1 germline knockouts⁵⁶. While only a small fraction of GC B cells was EYFP⁺ after immunization, Myc was ~sixfold upregulated in EYFP⁺ compared to EYFP⁻ GC B cells (Extended Data Fig. 6f). To assess whether modulating BMI-1 expression affected affinity maturation, Vh186.2 in GC B cells was sequenced (Fig. 6f,g and Extended Data Fig. 6g). The overall number of mutations (Fig. 6f) and CDR3 length (Extended Data Fig. 6g) was comparable between Bmi1^f/fCd23^Cre/+ and Cd23^Cre/+ mice; however, there was an increase in mutations in CDR1 (P = 0.0594) and a significant decrease in mutations in CDR3 (P = 0.0448) in the absence of BMI-1 (Fig. 6f). Next, the affinity-enhancing replacement of the codon encoding for amino acid tryptophan by that encoding leucine at position 33 (W33L) of Vh186.2 was measured. The percentage of the W33L mutation was increased in GCB from Bmi1^f/fCd23^Cre/+ compared to Cd23^Cre/+ mice (71% compared to 55%, respectively) (Fig. 6g). Therefore, while GC B cells could form in Bmi1^f/fCd23^Cre/+ mice, B cell-specific BMI-1 deletion led to an increase of high-affinity variants within GC B cells, but a decrease in ASC output.

**Fig. 6: BMI-1 regulates plasma cell survival.**

The data presented thus far suggested that BMI-1 was important for GC to ASC differentiation. We next asked whether BMI-1 directly regulated ASC survival independent of its role in differentiation. To specifically determine whether steady-state ASCs relied on BMI-1 for their survival, we generated Bmi1^f/fPrdm1^{Rosa26CreERT2} mice in which BMI-1 is inducibly deleted in Prdm1 (the gene encoding Blimp-1)-expressing cells. Uninfected Bmi1^f/fPrdm1^{Rosa26CreERT2} and control mice were administered with tamoxifen and ASCs were assessed 7 and 14 d later (Fig. 6h). Tamoxifen administration significantly reduced the frequency of ASCs at d14, and ASC number at both time points (Fig. 6i), but not GC B cells (Extended Data Fig. 6h–j) demonstrating that BMI-1 is an important regulator of ASC survival.

Small molecule inhibition of BMI-1 depletes ASCs

Targeting detrimental antibodies and ICs is a therapeutic goal for chronic infectious and inflammatory disorders. Given that BMI-1 regulated ASC survival, we assessed small molecule inhibition of BMI-1 to modulate ASC and IC formation. First, naive B cells were stimulated in vitro to induce plasmablast differentiation, which was significantly reduced by PTC-209 (ref. ⁵⁷) in a dose-dependent fashion (Fig. 7a and Extended Data Fig. 7a). While plasmablasts were reduced, the ability of B cells to undergo division was unaffected across doses (Extended Data Fig. 7b), with the exception of 1.25 μM (not shown). Next, C57BL/6 mice were infected with LCMV-Docile and after 2 d to allow initial activation of the immune response, were given daily administrations of PTC-028, an inhibitor that targets and degrades BMI-1 through hyperphosphorylation⁵⁸ (Fig. 7b). LCMV-Docile-infected mice that received the vehicle control lost weight before stabilization at 85% of the original body weight at d8 after infection (Fig. 7c). In contrast, LCMV-Docile-infected mice administered with PTC-028 lost <5% body weight up to d3 after infection, but then recovered by d5 (Fig. 7c). Similar to Bmi1^f/fCd23^Cre/+ mice, ASC frequency and number were significantly reduced after PTC-028 treatment, compared to controls (3.3-fold and 1.8-fold, respectively; Fig. 7d). Concomitantly, total (Fig. 7e) and LCMV-specific IgG2c IC (Fig. 7f) were significantly decreased in the PTC-028 group compared to vehicle controls. While liver viral titers were not significantly reduced with this dosage regimen (not shown), splenic architecture was restored in mice administered PTC-028, similar to that observed in Bmi1^f/fCd23^Cre/+ mice (Extended Data Fig. 7c). Collectively, these data reveal BMI-1 as a viable therapeutic target to deplete ASCs and reduce IC formation in immune disorders.

**Fig. 7: BMI-1 can be targeted to deplete plasma cells in vitro and in vivo.**

Discussion

Antibodies are a key modulator of both innate and adaptive arms of an immune response. However, in chronic infections, the quality and quantity of antibodies are dysregulated and antibodies can themselves become pathogenic. Defining the mechanistic steps that shift humoral responses from effective to ineffective when viral infections persist has been elusive. Here, we identify BMI-1 as a critical regulator of B cell differentiation, which can be targeted therapeutically. Deletion of BMI-1 in B cells led to pleiotropic effects on the immune response: accelerated viral clearance, reduced splenomegaly coupled with improved lymphoid architecture, increased neutralizing capability and effector function of antibody. Expression of Myc, the transcription factor that is required for B cell selection in the GC^3,4, was concentrated in Bmi1-expressing cells and regulated by BMI-1 in chronic viral infection. Further, the preservation of lymphoid architecture, reduction in excessive IC or changes to the N-glycan profile of antibody within ICs may work collectively to support affinity maturation in the GC^11,59,60. These results highlight the critical direct and indirect roles of B cells controlling effective versus ineffective immune responses to persistent viral infection.

c-Myc is expressed in proliferating cells but is known to be tightly regulated in cells undergoing selection in the LZ^3,4. Similarly, in acute infection BMI-1 was expressed in only a small fraction of GC B cells but upregulated upon ASC differentiation. Chronic viral infection deregulated expression of both BMI-1 and c-Myc, concomitant with an increase in GC B cells and ASCs. Increased c-Myc may result in the disruption of GC B cell division and selection, skewing cells toward premature differentiation⁵. BMI-1 and c-Myc cooperate in lymphoid transformation^37,38 and can directly and indirectly regulate each other in a context-dependent manner^61,62,63. Although a number of pathogen-influenced changes in the microenvironment have been recorded in chronic infection^41,64,65, we reasoned that cytokine differences within the GC may mediate sustained induction of BMI-1 and c-Myc. One key cytokine that is notably increased in chronic LCMV infection, compared to acute, is IL-21 (ref. ⁵⁵). Accordingly, high concentrations of IL-21, but not low levels, induced BMI-1 and c-Myc in stimulated B cells. Persisting GCs have been proposed to be beneficial in inducing broadly neutralizing antibodies⁶⁶. However, we highlight the importance of appropriate regulation of this process under sustained inflammatory pressure. In particular, BMI-1 plays an essential role in dictating whether B cells recruited into a response against infection will contribute to a successful humoral response.

The specific role of BMI-1 in ASCs reveals the importance in the composition of the heterogeneous PRC1 during B cell differentiation, changing from non-canonical²⁶ to canonical components during an immune response to regulate GC and ASC biology, respectively. Here, H2AK119Ub1 was decreased at key loci in GC B cells in chronic infection, compared to acute, suggesting a deregulation in PRC function. BMI-1 deletion increased H2AK119Ub1, in a seemingly paradoxical manner as BMI-1 is known to support the ubiquitin ligase activity of RING1B²⁹. This suggests that the aberrant recruitment of BMI-1 into the PRC1 in chronic GCs disrupts the non-canonical function of the PRC1, concomitant with downregulation of BCOR, a regulator of GC biology²⁶. It also suggests that this dynamic process is regulated by microenvironmental signals within the GC. Understanding the relationship between cytokine signaling and resultant biochemical changes in the composition of the PRCs may implicate other chronic inflammatory mediators that lead to detrimental B cell differentiation.

Small molecule inhibitors of epigenetic regulators are rapidly emerging as important therapeutic tools and as such are in clinical trials for a number of disorders^67,68. Excessive IC deposition induces substantial tissue damage in chronic infections and other antibody-mediated diseases in which depleting pre-existing ASCs is a therapeutic goal^69,70. We propose that BMI-1 could be targeted with a small molecule inhibitor to deplete pre-existing detrimental plasma cells. This strategy may also be used to tackle pathogenic antibody-dependent enhancement, in which antibody produced from initial viral exposure leads to multi-organ damage upon re-exposure. In identifying BMI-1 as a critical determinant of productive antibody responses to chronic viral infection, this study has important therapeutic implications for immune disorders in which pathogenic antibodies play a role in morbidity.

Methods

Mice

All animal experimentation was performed following the Australian National Health and Medical Research Council Code of Practice for the Care and Use of Animals for Scientific Purposes guidelines and in accordance with institutional regulations. The Monash Animal Ethics Committee approved all procedures. Bmi1^f/fCd23^cre mice were generated by crossing Cd23^cre mice provided by M. Busslinger (Institute of Molecular Pathology)⁷¹ with Bmi1^f/f mice provided by S. Morrison (University of Texas Southwestern)⁷². Bmi1^CreER mice (010531; Jackson Laboratories)⁷³ were crossed with Rosa26^EYFP mice⁷⁴. Bmi1^f/fPrdm1^{Rosa26CreERT2} mice were generated by crossing Bmi1^f/f with Prdm1^{Rosa26CreERT2} mice provided by D. Tarlinton (Monash University). Ly5.1 mice were kindly provided by C. Zaph (Monash University). All mice were on a C57BL/6 background, backcrossed and maintained at the Monash Animal Research Platform. Both male and female mice were used in the experimental research project, between 42 d and 4 months for intact mice; up to 6 months for bone-marrow chimera experiments.

For immunization, mice were injected intraperitoneally (i.p.) with 100 µg 100 µl⁻¹ per mouse of NP₁₃-KLH precipitated in adjuvant alum. For LCMV infection, 100 μl of LCMV-WE (3 × 10³ p.f.u.) or LCMV-Docile (2 × 10⁶ p.f.u.) was given intravenously (i.v.) per mouse tail vain. For tamoxifen administration, Bmi1^CreERT2Rosa^EYFP and Bmi1^f/fPrdm1^{Rosa26CreERT2} mice were treated with tamoxifen (Sigma) dissolved in peanut oil and ethanol at 5 mg ml⁻¹. Then, 90 µl of tamoxifen or vehicle control (peanut oil and ethanol) was administered to each mouse for 3 d consecutively via oral gavage at indicated time points.

Flow cytometry, cell sorting and antibodies

Single-cell suspensions were resuspended in PBS 2% FCS and stained for flow cytometric analysis. Live or fixed cells were analyzed on the Fortessa or Canto (BD); cytometry data were acquired with FACSDiva software (BD) and analyzed with FlowJo (TreeStar). FcγRII/III (2.4G2; supernatant) and Normal Rat Serum (Sigma-Aldrich) were used to block nonspecific binding. For sort purification, cells were stained with fluorochrome-labeled antibodies (Supplementary Table 1) and purified by FACSAria or Influx (BD). Gating strategies used are shown in Extended Data Fig. 8.

Histology

Immunofluorescence

Splenic portions were fixed using periodate–lysine–paraformaldehyde fixative for 6–8 h at 4 °C. Tissues were transferred to a 30% sucrose solution and incubated overnight at 4 °C. Spleens were then embedded in OCT (Tissue-Tek) and kept frozen at −80 °C until staining. Seven-micrometer tissue sections were cut via microtome (Leica) and mounted on Superfrost Plus slides. Sections were fixed with cold acetone (Sigma) for 10 min, blocked with PBS–5% FCS and stained using the indicated antibodies (Supplementary Table 2). Slides were observed under ×4 and ×10 magnification using an Olympus IX71 inverted microscope. Images were captured with a mounted high-sensitivity cooled SPOT RT KE CCD camera; white balance and exposure were set using SPOT 5.1 imaging software. Confocal microscopy images were acquired on a Leica SP8 laser scanning confocal microscope on a DMi8 inverted stand. Overview images were acquired with a multi-immersion objective HC PL APO ×20/0.75 IMM CS2 objective with oil immersion. For higher resolution images an HC PL APO ×63/1.40 CS2 oil immersion objective was used. Image resolution was 1,024 × 1,024. Quantitation of PNA⁺Myc⁺ cells within IgD^lo regions, as well as 100 µm scale bars and pseudo-colors, were assigned to raw images using ImageJ software. Brightness and contrast were applied equally to each image. Threshold and despeckle tools were used for quantification analysis.

Immunohistochemistry

Splenic portions were frozen in OCT (Tissue-Tek). Seven-micrometer sections were cut using a microtome (Leica) at −20 °C and fixed with acetone. Staining was performed using described antibodies (Supplementary Table 3). Slides were viewed under ×4 and ×10 magnification using an Olympus CKX41 microscope and images were captured with a mounted Nikon DP-12 camera. Raw images were taken using the Nikon NIS-Element software platform and ImageJ was used to add 100-μm scale bars to each image.

Deletion analysis

Sort-purified subsets were processed using the NucleoSpin Tissue kit (Macherey-Nagel, 740952.250). Genomic DNA was amplified using the following primers: Bmi1 LoxP forward (5ʹ-GCTAGCATTCCTGGTTTTGC-3ʹ), Bmi1 LoxP reverse (5ʹ-GGCACAGTGATGAGGTGTTG′3ʹ) and Bmi1 excised (5ʹ-CACGAGGTGCTTCTTTCCTC-3ʹ).

RNA purification and quantification

Sort-purified cell subsets were centrifuged, lysed in RLT buffer (QIAGEN) and passed through a gDNA eliminator spin column (QIAGEN) to remove gDNA. Total RNA was extracted using the RNeasy Plus Micro kit (QIAGEN, cat. no. 74034) according to manufacturer’s instructions. First-strand complementary DNA synthesis was performed with a High-Capacity cDNA Reverse Transcription kit (Applied Biosystems, cat. no. 4368814). RT–qPCR was performed with Rotor-Gene Q using the QuantiNovaSYBR Green PCR kit (QIAGEN, cat. no. 208054). The forward and reverse primers for Bmi1 (ref. ⁷⁵) were 5ʹ-AATTAGTCCCAGGGCTTTTCAA-3ʹ and 3ʹ-TCTTCTCCTCATCTGCAACTTCTC-5ʹ; Myc: 5ʹ-GCTGTTTGAAGGCTGGATTTC-3ʹ and 3ʹ-GATGAAATAGGGCTGTACGGAG-5ʹ; Bcl2l11: 5ʹ-CGACAGTCTCAGGAGGAACC-3ʹ and 3ʹ-CCTTCTCCATACCAGACGGA-5ʹ; and Pmaip1: 5ʹ-GAACGCGCCAGTGAACCCAA-3ʹ and 3ʹ-CTTTGTCTCCAATCCTCCGG-5ʹ. The housekeeping Ku70 gene was used as an internal control for normalization. To assess Ku70 expression, the following set of primers was used: 5ʹ-TCGCCTTTACTGAGAAGGTGAC-3ʹ and 3ʹ-TGCTGCAGGACTGGATTCTC-5ʹ. The 2^-𝜟𝜟Ct method was used to determine relative changes in gene expression.

ChIP–qPCR

Sort-purified subsets were processed as published⁷⁶. Briefly, sorted cells were incubated with 0.6% formaldehyde for 10 min. Crosslinking was stopped by addition of glycine to a final concentration of 0.25 M and cross-linked chromatin was sonicated to reduce DNA size to 200–500 base pairs (bp). Sonicated chromatin was then used for ChIP analysis. Immunoprecipitation was undertaken with mouse antibodies against H3K27me3 (Millipore, 07-449) and H2AK119Ub1 (Cell Signaling Technology, 8240). RT–qPCR was performed as described above. Primers used to amplify a fragment including the transcription start site of indicated genes were: Prdm1 5ʹ-GAGTAGTCAGTCGCTCGCTC-3ʹ and 3ʹ- GGTCCCTCCTTTTCTACGGC-5ʹ; Irf4 5ʹ-GAGCTGAAGAAAGCCAGGGT-3ʹ and 3ʹ-CTTTATAGAGCCGGAGGCGG; and Myc 5ʹ-GCGCGAGCAAGAGAAAATGG-3ʹ and 3ʹ- GCTCCGGGGTGTAAACAGTA-5ʹ. Data were normalized by dividing the total copies of no-antibody control, H3K23me3 or H2AK119Ub1 to copies of the total input. Each value was then multiplied by a factor of 100 to obtain the frequency (%) of input.

ELISA and ELISpot

For ELISA, LCMV-WE and Docile viral lysates were used to coat 96-well high-binding plates (Sarstedt) overnight at 4 °C. Plates were then blocked with PBS 1% BSA for 1 h, washed and loaded with serially diluted serum for a 4 h incubation at 37 °C. Plates were washed again and incubated for 1 h at 37 °C with IgG2c conjugated to horseradish peroxidase (HRP; Southern Biotech), followed by washing and development with OPD-substrate solution (Sigma-Aldrich). For ELISPOT and ELISA of NP-KLH samples, multiscreen HA plates (Millipore) were coated overnight with NP₁₂BSA or NP₁BSA for quantification of NP-specific IgG1⁺ ASCs. Plates were blocked, washed and loaded with samples prepared in RPMI 5% FCS, 50 μM 2-mercaptoethanol and 2 mM glutamine before an overnight incubation at 37 °C. Plates were washed and subsequently incubated for 1 h at 37 °C with secondary antibody (IgG1) conjugated to alkaline phosphatase (Southern Biotech) for 1 h. Plates were washed and developed with the BCiP/NBT reaction (Sigma-Aldrich). For ELISA analysis, high-binding plates were coated with NP₁₂BSA, serum samples followed by IgG1-HRP incubated as described above, before detection with OPD-substrate solution. A non-linear fit is shown (GraphPad Prism).

IC precipitation

ICs were precipitated from mouse sera^11,77. Briefly, serum was incubated with a 1:1 ratio of PBS 5% PEG6000 solution overnight at 4 °C. The serum–PEG samples were mixed with RPMI 2.5% PEG6000 in a 1:3 ratio and spun down at 2,000g for 30 min at 4 °C. PEG precipitates were resuspended to the initially used serum volume in cold PBS and then quantified by ELISA.

T cell cytotoxicity assay and IFN-γ production

Cytotoxicity capacity of CD8⁺ T cells were assessed as previously described⁷⁸. Briefly, CD8⁺ T cells were negatively enriched using the CD8α⁺ T cell (mouse) isolation kit (Miltenyi Biotec). Splenocytes from Ly5.1 mice were used as targets, labeled with cell trace violet (CTV; Molecular Probes) and loaded with gp_33–41 peptide at 0.1 µM. Enriched CD8⁺ T cells were plated with peptide-pulsed (CTV high) or unpulsed (CTV low) splenocyte target cells from Ly5.1 mice in either a 1:1 ratio of 2.5:1 ratio and incubated for 5 h at 37 °C, stained for surface markers, propidium iodide (Molecular Probes), anti-CD45.1:APC-Cy7 (A20; BioLegend) and anti-CD45.2:PE (104; BioLegend) and assessed by flow cytometry. For cytokine production, approximately 1 × 10⁶ splenocytes were stimulated in vitro for 5 h with IL-2 10 U ml⁻¹ (Peprotech) and brefeldin A (BD Cytofix/Cytoperm) in the presence or absence of 1 µM H2-Db gp_33–41 peptide. Cells were surface stained with anti-CD8:BUV395 (53-6.7, BD Biosciences), then fixed and permeabilized for intracellular expression of IFN-γ:FITC (XMG1.2, BD Biosciences) and analyzed by flow cytometry.

Bone-marrow chimeras

Lethally irradiated Ly5.1 mice (2 × 4.5 Gy) were reconstituted with 50% Ly5.1 and 50% Ly5.2-Cd23^Cre/+ or 50% Ly5.2-Bmi1^f/fCd23^Cre/+ bone marrow.

Plaque-forming unit assay and infectivity reduction assay

Livers were weighed and the calculated volume of DMEM (Gibco) was added to obtain 500 mg ml⁻¹ of suspension. One stainless steel bead (QIAGEN) was added per sample and liver tissues were homogenized using the TissueLyser LT (QIAGEN) for 5 min at 50 oscillations s⁻¹. Beads were removed and samples were spun for 10 min at 15,000 r.p.m. (4 °C) to pellet cell debris. For the p.f.u. assay, homogenized liver suspensions were serially diluted and added onto MC57G cells. An overlay mixture of 2% methylcellulose solution and 2× DMEM (Gibco) was added after virus adsorption and plates incubated (37 °C for 48 h). Plates were then stained with primary VL-4 antibody (previously described⁷⁹) and the secondary HRP goat anti-rat IgG (Jackson ImmunoResearch). Plaques were developed (HRP Substrate kit; Vector) and counted to quantify viral load. For infectivity reduction assays, sera from LCMV-infected experimental mice were inactivated at 56 °C for 30 min to eliminate pre-existing virus. Sera were then diluted and incubated for 90 min at 37 °C with a known quantity of LCMV (30 p.f.u.). The antibody-virus mixtures were added onto MC57G cells and plaques were calculated. The neutralization capacity of the serum was expressed in percentage of plaque reduction, referring to the no-serum control sample.

ADCC

Target cells MC57G were cultured in complete DMEM (DMEM, 10%, NaPyr, L-Glu, NEAa, 2-ME and HEPES, pH 7.2–7.5), labeled with CTV and infected with LCMV-Docile at a multiplicity of infection of 1, 24 h before the cytotoxicity assay. Splenic NK cells were isolated from naive C57BL/6 mice using nylon wool filters (Polysciences) and seeded in complete RPMI-1640 (RPMI-1640, 5% FCS, 1% penicillin, streptomycin and 2 mM glutamine) supplemented with 250 ng ml⁻¹ IL-2 for 5 d. ADCC was initiated by the addition of serum, previously collected from mice infected with LCMV-Docile, to the target cells. NK cells were combined in a 10:1 effector:target ratio. The plate was further incubated for 2 h at 37 °C. Cells were collected and stained with Fixable Viability Stain (BD Biosciences) to evaluate the frequency of dead cells by FACS.

IgG purification

IgG antibodies were purified from plasma according to manufacturer’s protocol (Melon Gel IgG Purification kit, Thermo Fisher Scientific). Purified IgG antibody samples were centrifuged through 100 kDa Amicon Ultra filters (Merck) at 14,000g for 10 min to remove excess serum proteins and buffer was exchanged into PBS. Purity was confirmed via SDS–PAGE (Bio-Rad) and IgG concentrations were measured using a nanodrop spectrophotometer (Bio-Rad).

IgG N-linked glycan profiling

IgG N-linked glycosylation patterns were measured^80,81 according to ProfilerPro glycan profiling LabChip GXII Touch protocol on the LabChip GXII Touch instrument (PerkinElmer). Microchip capillary electrophoresis laser-induced fluorescence (CE-LIF) analysis of digested and labeled N-linked glycans was performed. The relative prevalence of major N-linked glycan profiles of IgG was analyzed using the LabChip GX Reviewer (PerkinElmer) software. Peaks were assigned based on the migration of known standards and glycan digests⁴⁵. Peak area and relative prevalence of each glycan pattern were calculated.

In vitro B cell stimulation

B cells were isolated using a B cell negative enrichment kit (STEMCELL). Cells were resuspended in RPMI 5% FCS, 50 μM 2-mercaptoethanol and 2 mM glutamine. Then, 5 × 10⁴ labeled B cells were cultured with CD40L (R&D Systems) in combination with IL-4 and IL-5 (R&D Systems) and in the presence of increasing IL-21 (Peprotech) at the indicated ng ml⁻¹ concentrations.

Small molecule inhibitors

In vitro, 5 × 10⁴ CTV-labeled B cells were cultured with LPS (Sigma) in combination with 50 ng ml⁻¹ IL-4 (R&D Systems) in the presence of DMSO or PTC-209 (Sapphire Bioscience) at the indicated concentrations. In vivo, mice were infected with LCMV-Docile and administered with 15 mg kg⁻¹ body weight per mouse of PTC-028 (Sapphire Bioscience) small molecule inhibitor or vehicle control (0.5% hydroxypropyl methylcellulose and 1% Tween 80) via oral gavage every 24 h.

ATAC sequencing

Briefly, 50,000 cells were pelleted and washed with PBS. One-step permeabilization and tagmentation method was used by resuspending the cell pellets in Digitonin (EZSolution), Tween-20 (Sigma) buffer. High-molecular-weight tagmented DNA was then removed by incubating samples with a 0.7× ratio of Agencourt AMPureXP beads (Invitrogen). The same process was applied using a 1.2× ratio of beads to exclude low-molecular-weight DNA. Purified DNA was then pre-amplified with indexed primers and HiFi HotStart Polymerase Ready Mix (KAPA Biosystems). After estimation of addition PCR cycles required⁸², the genomic library was fully amplified using the SYBR qPCR Master Mix with primers included in the Illumina Library Quantification kit for Bio-Rad iCycler (KAPA Biosystems). Libraries were sequenced on a HiSeq2500 using 50-bp paired-end Illumina chemistry. For data processing, reads were aligned to the NCBI37/mm10 build of the Mus musculus genome using Bowtie2 (ref. ⁸³) and PCR duplicate reads were flagged using the Picard MarkDuplicates tool. Enriched accessible regions were determined using MACS2 (ref. ⁸⁴) and reads overlapping peaks were annotated for each sample using GenomicRanges⁸⁵ tool in R. DARs were determined using DESeq2 (ref. ⁸⁶). PCA was performed using the vegan package in R.

RNA sequencing

Briefly, 250,000 cells were pelleted and washed with PBS. RNA purification was performed using the RNeasy Micro kit (QIAGEN) or Direct-zol RNA Microprep kit (Zymo Research), according to the manufacturer’s instructions. Total mRNA was quantified with the Qubit Fluorometer (Invitrogen) and RNA integrity number values were generated for quality assignment by using the Agilent Bioanalyzer. Quality control-assessed libraries were then sequenced at the Illumina NextSeq500. For data processing, reads were aligned to the NCBI37/mm10 build of the M. musculus genome using the Subread aligner. Library counts were obtained using featureCounts and log₂fold change were processed applying the DeSeq2 function. A PCA plot was generated using the PCAplot tool included in the DeSeq2 library. Volcano plot and quality control analysis were acquired using the Degust tool⁸⁷. K-means analysis and heat maps were carried out using the kmeans and heatmap.2 tools in RStudio. PRC and Myc target analyses plots were generated using information about targets from published datasets^{88,89,90,91,92,93}.

IPA and GSEA

QIAGEN’s IPA software was used for functional pathway analysis of DEGs identified in this study. Analysis was performed using expression cutoff of 0.58-fold and FDR of 0.05. GSEA was performed on unfiltered normalized counts to identify signaling pathway that are differentially activated in GC B cells between acute and chronic infection. Analysis was executed by using the MSigDB collection of the Hallmark gene set covering principal molecular functions.

Vh sequencing

Splenocytes from NP-KLH-immunized mice were pooled and single GC B cells were sort-purified into 96-well plates containing PBS on a BD FACSAria equipped with a plate sorter and prepared for sequencing using nested primers for Vh186.2 and Cγ1 as previously described⁹⁴. DNA Sanger sequencing was performed by Macrogen, Singapore, using 3ʹ primers from the second round of PCR. Vh186.2 sequences were aligned to germline using IMGT V-Quest and sequences of poor quality, those not identified as Vh186.2, miscalls, incomplete sequences or those containing stop codons were excluded from further analysis. The number and frequency of mutations was identified by comparison to the germline sequence. The number of sequences with the position 33 mutation, tgg (Trp) -> ttg, ctt or tta (Leu), was counted.

LCMV BCR sequencing

Methods were adapted from previous work⁹⁵. Sort-purified bulk GC B cells (B220⁺IgD^loCD38^-CD95⁺CD138⁻) were processed with a NucleoSpin Tissue mini kit for DNA (Macherey-Nagel, 740952), according to manufacturer’s instructions. Genomic DNA was pre-amplified for mouse antibody heavy-chain library construction (using primers listed in Supplementary Table 4), amplicons were purified and Illumina NexteraXT adaptors were added to the Igh library⁹⁵. Paired-end sequencing using Illumina MiSeq V2 chemistry and 251-bp paired-end reads was performed (Hudson Institute). For data processing, high-throughput BCR data of raw sequences from eight mice along with their metadata were stored, annotated and analyzed with ImmuneDB⁹⁶. V and J gene assignment was performed using the Anchoring method⁹⁷. For clone assignment, sequences in each mouse were divided into bins of common Vh and Jh gene assignment and the same CDR3 length. All of the sequences with 85% or more similarity in their CDR3 amino acids were grouped into the same clone. For mutation analysis, the number of unique mutations per clone were calculated in CDRs and framework regions, defined previously⁹⁸. We considered both the overall levels of non-synonymous and synonymous mutations and the overall ratio of non-synonymous to synonymous mutations in each region of each mouse. For selection pressure analysis, the overall selection pressure in CDRs and framework regions was assessed across all clones of each mouse. Selection pressure analysis was performed with BASELINe (Bayesian Estimation of Antigen-driven Selection in Ig Sequences)⁹⁹, part of the Shazam package¹⁰⁰.

Immunoblot

Sort-purified GC B cells were resuspended in RIPA buffer (Sigma, R0278) supplemented with Roche complete mini protease inhibitor cocktail (Roche, 11836170001). Protein lysates were prepared with 4× Laemmli Sample Buffer (Bio-Rad, 1610747) supplemented with β-mercaptoethanol. Samples were run on 12% SDS–PAGE gels (Bio-Rad, 1610174). Transfer blotting on nitrocellulose membranes was performed (40 min at 90 V). Membranes were blocked in 5% milk. Primary antibodies were: BMI-1 (D20B7; Cell Signaling Technology, 6964) and histone H3 (D1H2; Cell Signaling Technology, 4499). Secondary antibodies were peroxidase AffiniPure goat anti-rabbit IgG (H + L) (Jackson ImmunoResearch, 111-035-144). H3 controls of samples were run in parallel due to technical limitations of developing both H3 and BMI-1 on the same blot. Membranes were developed using Clarity Western ECL Substrate (Bio-Rad, 1705060) and images were taken at the ChemiTouch (Bio-Rad) at different exposure times. Intensity of bands was quantified using the ‘lane and bands’ tool in Image Lab software (Bio-Rad, v.6.1).

Statistical analysis

The Mann–Whitney nonparametric two-tailed test, unpaired Student’s t-test, Wilcoxon matched-pairs signed-rank test or a one-way analysis of variance was used to analyze the significance of data (GraphPad Prism). Statistical values are ****P < 0.0001, ***P < 0.001, **P < 0.01 and *P < 0.05. Data are represented as mean ± s.e.m. No statistical methods were used to predetermine sample sizes but our sample sizes are similar to those reported in previous publications^20,101. Data distribution was assumed to be normal but this was not formally tested. There was no randomization to data collection or organization of experimental conditions. Animals were assigned to experimental groups based on genotype, sex and age matching. Mice assessed to be nonresponders to stimuli were excluded from analyses. Data collection and analysis were not performed blind to the conditions of the experiments.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

RNA-seq, ATAC-seq and LCMV BCR sequencing data has been deposited to the Gene Expression Omnibus under accession code GSE163365. Source data are provided with this paper.

Code availability

Code for RNA-seq and ATAC-seq analysis followed typical pipelines from public R packages. RNAsik pipeline¹⁰² was used to analyze RNA-seq data, Bowtie2/MACS2 was used to analyze ATAC-seq data. All codes are available upon request.

References

Zohar, T. et al. Compromised humoral functional evolution tracks with SARS-CoV-2 mortality. Cell 183, 1508–1519 (2020).
Article CAS PubMed PubMed Central Google Scholar
Woodruff, M. C. et al. Extrafollicular B cell responses correlate with neutralizing antibodies and morbidity in COVID-19. Nat. Immunol. 21, 1506–1516 (2020).
Article PubMed PubMed Central CAS Google Scholar
Dominguez-Sola, D. et al. The proto-oncogene MYC is required for selection in the germinal center and cyclic reentry. Nat. Immunol. 13, 1083–1091 (2012).
Article CAS PubMed PubMed Central Google Scholar
Calado, D. P. et al. The cell-cycle regulator c-Myc is essential for the formation and maintenance of germinal centers. Nat. Immunol. 13, 1092–1100 (2012).
Article CAS PubMed PubMed Central Google Scholar
Finkin, S., Hartweger, H., Oliveira, T. Y., Kara, E. E. & Nussenzweig, M. C. Protein amounts of the MYC transcription factor determine germinal center B cell division capacity. Immunity 51, 324–336 (2019).
Article CAS PubMed PubMed Central Google Scholar
Hunziker, L. et al. Hypergammaglobulinemia and autoantibody induction mechanisms in viral infections. Nat. Immunol. 4, 343–349 (2003).
Article CAS PubMed Google Scholar
Recher, M. et al. Deliberate removal of T cell help improves virus-neutralizing antibody production. Nat. Immunol. 5, 934–942 (2004).
Article CAS PubMed Google Scholar
Lane, H. C. et al. Abnormalities of B cell activation and immunoregulation in patients with the acquired immunodeficiency syndrome. N. Engl. J. Med. 309, 453–458 (1983).
Article CAS PubMed Google Scholar
Cooper, L. & Good-Jacobson, K. L. Dysregulation of humoral immunity in chronic infection. Immunol. Cell Biol. 98, 456–466 (2020).
Article PubMed Google Scholar
Wieland, A. et al. Antibody effector functions mediated by Fcγ-receptors are compromised during persistent viral infection. Immunity 42, 367–378 (2015).
Article CAS PubMed PubMed Central Google Scholar
Yamada, D. H. et al. Suppression of Fcγ-receptor-mediated antibody effector function during persistent viral infection. Immunity 42, 379–390 (2015).
Article CAS PubMed PubMed Central Google Scholar
Appel, G. B. Immune-complex glomerulonephritis–deposits plus interest. N. Engl. J. Med. 328, 505–506 (1993).
Article CAS PubMed Google Scholar
Oldstone, M. B. & Dixon, F. J. Pathogenesis of chronic disease associated with persistent lymphocytic choriomeningitis viral infection. II. Relationship of the anti-lymphocytic choriomeningitis immune response to tissue injury in chronic lymphocytic choriomeningitis disease. J. Exp. Med. 131, 1–19 (1970).
Article CAS PubMed PubMed Central Google Scholar
Lu, L. L., Suscovich, T. J., Fortune, S. M. & Alter, G. Beyond binding: antibody effector functions in infectious diseases. Nat. Rev. Immunol. 18, 46–61 (2018).
Article CAS PubMed Google Scholar
Straub, T. et al. Nucleoprotein-specific nonneutralizing antibodies speed up LCMV elimination independently of complement and FcγR. Eur. J. Immunol. 43, 2338–2348 (2013).
Article CAS PubMed Google Scholar
Chung, A. W. et al. Dissecting polyclonal vaccine-induced humoral immunity against HIV using systems serology. Cell 163, 988–998 (2015).
Article CAS PubMed PubMed Central Google Scholar
Wang, T. T. et al. Anti-HA glycoforms drive B cell affinity selection and determine influenza vaccine efficacy. Cell 162, 160–169 (2015).
Article CAS PubMed PubMed Central Google Scholar
Caskey, M., Klein, F. & Nussenzweig, M. C. Broadly neutralizing anti-HIV-1 monoclonal antibodies in the clinic. Nat. Med. 25, 547–553 (2019).
Article CAS PubMed PubMed Central Google Scholar
Zhang, Y. & Good-Jacobson, K. L. Epigenetic regulation of B cell fate and function during an immune response. Immunol. Rev. 288, 75–84 (2019).
Article CAS PubMed Google Scholar
Kealy, L. et al. The histone methyltransferase DOT1L is essential for humoral immune responses. Cell Rep. 33, 108504 (2020).
Article CAS PubMed Google Scholar
Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).
Article CAS PubMed PubMed Central Google Scholar
Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).
Article CAS PubMed PubMed Central Google Scholar
Beguelin, W. et al. EZH2 is required for germinal center formation and somatic EZH2 mutations promote lymphoid transformation. Cancer Cell 23, 677–692 (2013).
Article CAS PubMed PubMed Central Google Scholar
Caganova, M. et al. Germinal center dysregulation by histone methyltransferase EZH2 promotes lymphomagenesis. J. Clin. Invest. 123, 5009–5022 (2013).
Article CAS PubMed PubMed Central Google Scholar
Tanaka, S. et al. Tet2 and Tet3 in B cells are required to repress CD86 and prevent autoimmunity. Nat. Immunol. 21, 950–961 (2020).
Article CAS PubMed Google Scholar
Beguelin, W. et al. EZH2 and BCL6 cooperate to assemble CBX8–BCOR complex to repress bivalent promoters, mediate germinal center formation and lymphomagenesis. Cancer Cell 30, 197–213 (2016).
Article CAS PubMed PubMed Central Google Scholar
Raaphorst, F. M. et al. Cutting edge: Polycomb gene expression patterns reflect distinct B cell differentiation stages in human germinal centers. J. Immunol. 164, 1–4 (2000).
Article CAS PubMed Google Scholar
Tarte, K., Zhan, F., De Vos, J., Klein, B. & Shaughnessy, J. Jr. Gene expression profiling of plasma cells and plasmablasts: toward a better understanding of the late stages of B cell differentiation. Blood 102, 592–600 (2003).
Article CAS PubMed Google Scholar
Cao, R., Tsukada, Y. & Zhang, Y. Role of Bmi-1 and Ring1A in H2A ubiquitylation and Hox gene silencing. Mol. Cell 20, 845–854 (2005).
Article CAS PubMed Google Scholar
Wang, H. et al. Role of histone H2A ubiquitination in Polycomb silencing. Nature 431, 873–878 (2004).
Article CAS PubMed Google Scholar
Jacobs, J. J., Kieboom, K., Marino, S., DePinho, R. A. & van Lohuizen, M. The oncogene and Polycomb-group gene BMI-1 regulates cell proliferation and senescence through the ink4a locus. Nature 397, 164–168 (1999).
Article CAS PubMed Google Scholar
Cantor, D. J. et al. Impaired expression of rearranged immunoglobulin genes and premature p53 activation block B cell development in BMI1 null mice. Cell Rep. 26, 108–118 (2019).
Article CAS PubMed PubMed Central Google Scholar
Yamashita, M. et al. Bmi1 regulates memory CD4 T cell survival via repression of the Noxa gene. J. Exp. Med. 205, 1109–1120 (2008).
Article CAS PubMed PubMed Central Google Scholar
Jagani, Z. et al. The Polycomb group protein BMI-1 is essential for the growth of multiple myeloma cells. Cancer Res. 70, 5528–5538 (2010).
Article CAS PubMed Google Scholar
Lessard, J. & Sauvageau, G. Bmi-1 determines the proliferative capacity of normal and leukaemic stem cells. Nature 423, 255–260 (2003).
Article CAS PubMed Google Scholar
Oguro, H. et al. Poised lineage specification in multipotential hematopoietic stem and progenitor cells by the Polycomb protein Bmi1. Cell Stem Cell 6, 279–286 (2010).
Article CAS PubMed Google Scholar
Haupt, Y., Alexander, W. S., Barri, G., Klinken, S. P. & Adams, J. M. Novel zinc finger gene implicated as myc collaborator by retrovirally accelerated lymphomagenesis in E mu-myc transgenic mice. Cell 65, 753–763 (1991).
Article CAS PubMed Google Scholar
van Lohuizen, M. et al. Identification of cooperating oncogenes in E mu-myc transgenic mice by provirus tagging. Cell 65, 737–752 (1991).
Article PubMed Google Scholar
Dierks, C. et al. Essential role of stromally induced hedgehog signaling in B cell malignancies. Nat. Med. 13, 944–951 (2007).
Article CAS PubMed Google Scholar
De Vos, J. et al. Comparison of gene expression profiling between malignant and normal plasma cells with oligonucleotide arrays. Oncogene 21, 6848–6857 (2002).
Article PubMed CAS Google Scholar
Teijaro, J. R. et al. Persistent LCMV infection is controlled by blockade of type I interferon signaling. Science 340, 207–211 (2013).
Article CAS PubMed PubMed Central Google Scholar
Louten, J., van Rooijen, N. & Biron, C. A. Type 1 IFN deficiency in the absence of normal splenic architecture during lymphocytic choriomeningitis virus infection. J. Immunol. 177, 3266–3272 (2006).
Article CAS PubMed Google Scholar
Odermatt, B., Eppler, M., Leist, T. P., Hengartner, H. & Zinkernagel, R. M. Virus-triggered acquired immunodeficiency by cytotoxic T cell-dependent destruction of antigen-presenting cells and lymph follicle structure. Proc. Natl Acad. Sci. USA 88, 8252–8256 (1991).
Article CAS PubMed PubMed Central Google Scholar
Bergthaler, A. et al. Impaired antibody response causes persistence of prototypic T cell-contained virus. PLoS Biol. 7, e1000080 (2009).
Article PubMed PubMed Central CAS Google Scholar
Mahan, A. E. et al. Antigen-specific antibody glycosylation is regulated via vaccination. PLoS Pathog. 12, e1005456 (2016).
Article PubMed PubMed Central CAS Google Scholar
Francica, J. R. et al. Innate transcriptional effects by adjuvants on the magnitude, quality, and durability of HIV envelope responses in NHPs. Blood Adv. 1, 2329–2342 (2017).
Article CAS PubMed PubMed Central Google Scholar
Hessell, A. J. et al. Fc receptor but not complement binding is important in antibody protection against HIV. Nature 449, 101–104 (2007).
Article CAS PubMed Google Scholar
Nimmerjahn, F. & Ravetch, J. V. Divergent immunoglobulin g subclass activity through selective Fc receptor binding. Science 310, 1510–1512 (2005).
Article CAS PubMed Google Scholar
Shinkawa, T. et al. The absence of fucose but not the presence of galactose or bisecting N-acetylglucosamine of human IgG1 complex-type oligosaccharides shows the critical role of enhancing antibody-dependent cellular cytotoxicity. J. Biol. Chem. 278, 3466–3473 (2003).
Article CAS PubMed Google Scholar
Wieland, A. et al. Enhancing FcγR-mediated antibody effector function during persistent viral infection. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aao3125 (2018).
Kao, D. et al. IgG subclass and vaccination stimulus determine changes in antigen specific antibody glycosylation in mice. Eur. J. Immunol. 47, 2070–2079 (2017).
Article CAS PubMed Google Scholar
Zotos, D. et al. IL-21 regulates germinal center B cell differentiation and proliferation through a B cell-intrinsic mechanism. J. Exp. Med. 207, 365–378 (2010).
Article CAS PubMed PubMed Central Google Scholar
Good, K. L., Bryant, V. L. & Tangye, S. G. Kinetics of human B cell behavior and amplification of proliferative responses following stimulation with IL-21. J. Immunol. 177, 5236–5247 (2006).
Article CAS PubMed Google Scholar
Sarosiek, K. A. et al. Novel IL-21 signaling pathway up-regulates c-Myc and induces apoptosis of diffuse large B-cell lymphomas. Blood 115, 570–580 (2010).
Article CAS PubMed PubMed Central Google Scholar
Elsaesser, H., Sauer, K. & Brooks, D. G. IL-21 is required to control chronic viral infection. Science 324, 1569–1572 (2009).
Article CAS PubMed PubMed Central Google Scholar
Bhattacharya, D. et al. Transcriptional profiling of antigen-dependent murine B cell differentiation and memory formation. J. Immunol. 179, 6808–6819 (2007).
Article CAS PubMed Google Scholar
Kong, Y. et al. Targeting of BMI-1 with PTC-209 inhibits glioblastoma development. Cell Cycle 17, 1199–1211 (2018).
Article CAS PubMed PubMed Central Google Scholar
Dey, A. et al. Evaluating the mechanism and therapeutic potential of PTC-028, a novel inhibitor of BMI-1 function in ovarian cancer. Mol. Cancer Ther. 17, 39–49 (2018).
Article CAS PubMed Google Scholar
Hangartner, L., Zinkernagel, R. M. & Hengartner, H. Antiviral antibody responses: the two extremes of a wide spectrum. Nat. Rev. Immunol. 6, 231–243 (2006).
Article CAS PubMed Google Scholar
Lofano, G. et al. Antigen-specific antibody Fc glycosylation enhances humoral immunity via the recruitment of complement. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aat7796 (2018).
Wang, H. B. et al. Sp1 and c-Myc regulate transcription of BMI1 in nasopharyngeal carcinoma. FEBS J. 280, 2929–2944 (2013).
Article CAS PubMed Google Scholar
Huang, R. et al. MYCN and MYC regulate tumor proliferation and tumorigenesis directly through BMI1 in human neuroblastomas. FASEB J. 25, 4138–4149 (2011).
Article CAS PubMed PubMed Central Google Scholar
Cho, J. H., Dimri, M. & Dimri, G. P. A positive feedback loop regulates the expression of polycomb group protein BMI1 via WNT signaling pathway. J. Biol. Chem. 288, 3406–3418 (2013).
Article CAS PubMed Google Scholar
Harker, J. A., Lewis, G. M., Mack, L. & Zuniga, E. I. Late interleukin-6 escalates T follicular helper cell responses and controls a chronic viral infection. Science 334, 825–829 (2011).
Article CAS PubMed PubMed Central Google Scholar
Wilson, E. B. et al. Blockade of chronic type I interferon signaling to control persistent LCMV infection. Science 340, 202–207 (2013).
Article CAS PubMed PubMed Central Google Scholar
Fallet, B. et al. Chronic viral infection promotes efficient germinal center B cell responses. Cell Rep. 30, 1013–1026 (2020).
Article CAS PubMed PubMed Central Google Scholar
Arrowsmith, C. H., Bountra, C., Fish, P. V., Lee, K. & Schapira, M. Epigenetic protein families: a new frontier for drug discovery. Nat. Rev. Drug Discov. 11, 384–400 (2012).
Article CAS PubMed Google Scholar
Morera, L., Lubbert, M. & Jung, M. Targeting histone methyltransferases and demethylases in clinical trials for cancer therapy. Clin. Epigenetics 8, 57 (2016).
Article PubMed PubMed Central CAS Google Scholar
Hiepe, F. & Radbruch, A. Plasma cells as an innovative target in autoimmune disease with renal manifestations. Nat. Rev. Nephrol. 12, 232–240 (2016).
Article CAS PubMed Google Scholar
Low, M., Infantino, S., Grigoriadis, G. & Tarlinton, D. Targeting plasma cells: are we any closer to a panacea for diseases of antibody-secreting cells? Immunol. Rev. 270, 78–94 (2016).
Article CAS PubMed Google Scholar
Kwon, K. et al. Instructive role of the transcription factor E2A in early B lymphopoiesis and germinal center B cell development. Immunity 28, 751–762 (2008).
Article CAS PubMed Google Scholar
Mich, J. K. et al. Prospective identification of functionally distinct stem cells and neurosphere-initiating cells in adult mouse forebrain. eLife 3, e02669 (2014).
Article PubMed PubMed Central CAS Google Scholar
Sangiorgi, E. & Capecchi, M. R. Bmi1 is expressed in vivo in intestinal stem cells. Nat. Genet. 40, 915–920 (2008).
Article CAS PubMed PubMed Central Google Scholar
Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).
Article CAS PubMed PubMed Central Google Scholar
Nowak, K. et al. BMI1 is a target gene of E2F-1 and is strongly expressed in primary neuroblastomas. Nucleic Acids Res. 34, 1745–1754 (2006).
Article CAS PubMed PubMed Central Google Scholar
Russ, B. E. et al. Distinct epigenetic signatures delineate transcriptional programs during virus-specific CD8(⁺) T cell differentiation. Immunity 41, 853–865 (2014).
Article CAS PubMed PubMed Central Google Scholar
Lux, A., Yu, X., Scanlan, C. N. & Nimmerjahn, F. Impact of immune complex size and glycosylation on IgG binding to human FcγRs. J. Immunol. 190, 4315–4323 (2013).
Article CAS PubMed Google Scholar
Quinn, K. M. et al. Metabolic characteristics of CD8(⁺) T cell subsets in young and aged individuals are not predictive of functionality. Nat. Commun. 11, 2857 (2020).
Article CAS PubMed PubMed Central Google Scholar
Battegay, M. et al. Quantification of lymphocytic choriomeningitis virus with an immunological focus assay in 24- or 96-well plates. J. Virol. Methods 33, 191–198 (1991).
Article CAS PubMed Google Scholar
Lopez, E. et al. Low pH exposure during immunoglobulin G purification methods results in aggregates that avidly bind fcγ receptors: implications for measuring Fc dependent antibody functions. Front. Immunol. 10, 2415 (2019).
Article CAS PubMed PubMed Central Google Scholar
Mahan, A. E. et al. A method for high-throughput, sensitive analysis of IgG Fc and Fab glycosylation by capillary electrophoresis. J. Immunol. Methods 417, 34–44 (2015).
Article CAS PubMed Google Scholar
Scharer, C. D. et al. ATAC-seq on biobanked specimens defines a unique chromatin accessibility structure in naive SLE B cells. Sci. Rep. 6, 27030 (2016).
Article CAS PubMed PubMed Central Google Scholar
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Article CAS PubMed PubMed Central Google Scholar
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Article PubMed PubMed Central Google Scholar
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
Article CAS PubMed PubMed Central Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article PubMed PubMed Central Google Scholar
Powell, D. Degust: interactive RNA-seq analysis. Zenodo https://doi.org/10.5281/zenodo.3258932 (2015).
Kim, J. et al. A Myc network accounts for similarities between embryonic stem and cancer cell transcription programs. Cell 143, 313–324 (2010).
Article CAS PubMed PubMed Central Google Scholar
Schuhmacher, M. et al. The transcriptional program of a human B cell line in response to Myc. Nucleic Acids Res. 29, 397–406 (2001).
Article CAS PubMed PubMed Central Google Scholar
Gargiulo, G. et al. In vivo RNAi screen for BMI1 targets identifies TGF-β/BMP-ER stress pathways as key regulators of neural- and malignant glioma-stem cell homeostasis. Cancer Cell 23, 660–676 (2013).
Article CAS PubMed Google Scholar
Douglas, D. et al. BMI-1 promotes Ewing sarcoma tumorigenicity independent of CDKN2A repression. Cancer Res. 68, 6507–6515 (2008).
Article CAS PubMed PubMed Central Google Scholar
Wiederschain, D. et al. Contribution of Polycomb homologues Bmi-1 and Mel-18 to medulloblastoma pathogenesis. Mol. Cell. Biol. 27, 4968–4979 (2007).
Article CAS PubMed PubMed Central Google Scholar
Nuytten, M. et al. The transcriptional repressor NIPP1 is an essential player in EZH2-mediated gene silencing. Oncogene 27, 1449–1460 (2008).
Article CAS PubMed Google Scholar
Piovesan, D. et al. c-Myb regulates the T-Bet-dependent differentiation program in B cells to coordinate antibody responses. Cell Rep. 19, 461–470 (2017).
Article CAS PubMed Google Scholar
Staupe, R. P. et al. Chronic viral infection promotes early germinal center exit of B cells and impaired antibody development. Preprint at bioRxiv https://doi.org/10.1101/849844 (2019).
Rosenfeld, A. M., Meng, W., Luning Prak, E. T. & Hershberg, U. ImmuneDB: a system for the analysis and exploration of high-throughput adaptive immune receptor sequencing data. Bioinformatics 33, 292–293 (2017).
Article CAS PubMed Google Scholar
Zhang, B., Meng, W., Luning Prak, E. T. & Hershberg, U. Discrimination of germline V genes at different sequencing lengths and mutational burdens: a new tool for identifying and evaluating the reliability of V gene assignment. J. Immunol. Methods 427, 105–116 (2015).
Article CAS PubMed PubMed Central Google Scholar
Giudicelli, V., Brochet, X. & Lefranc, M. P. IMGT/V-QUEST: IMGT standardized analysis of the immunoglobulin (IG) and T cell receptor (TR) nucleotide sequences. Cold Spring Harb. Protoc. 2011, 695–715 (2011).
PubMed Google Scholar
Yaari, G., Uduman, M. & Kleinstein, S. H. Quantifying selection in high-throughput Immunoglobulin sequencing data sets. Nucleic Acids Res. 40, e134 (2012).
Article CAS PubMed PubMed Central Google Scholar
Gupta, N. T. et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–3358 (2015).
Article CAS PubMed PubMed Central Google Scholar
Good-Jacobson, K. L., O’Donnell, K., Belz, G. T., Nutt, S. L. & Tarlinton, D. M. c-Myb is required for plasma cell migration to bone marrow after immunization or infection. J. Exp. Med. 212, 1001–1009 (2015).
Article CAS PubMed PubMed Central Google Scholar
Tsyganov, K., Perry, A. J., Archer, S. K. & Powell, D. RNAsik: a pipeline for complete and reproducible RNA-seq analysis that runs anywhere with speed and ease. J. Open Source Softw. 3, 583 (2018).
Article Google Scholar

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Acknowledgements

We thank J. Groom (Walter and Eliza Hall Institute), I. Parish (Peter MacCallum Cancer Centre), C. Zaph and J. Rossjohn for critical reading of this manuscript and/or discussions; M. Pellegrini (Walter and Eliza Hall Institute), M. Degli-Esposti (Monash University) and A. Papa (Monash University) for generously providing, respectively, LCMV stocks, IL-2 and c-Myc antibody; J. Sun and members of the Good-Jacobson laboratory for technical assistance; O. Chernyavskiy and the staff of Monash Micro Imaging for the provision of instrumentation training and technical support; Monash FlowCore, Bioinformatics and Animal Research Platforms; and MHTP Medical Genomics Facility. This work was supported by a Bellberry-Viertel Senior Medical Research Fellowship (K.L.G.-J.); National Health and Medical Research Council (NHMRC) Career Development Fellowships 1108066 (K.L.G.-J.) and 1140509 (A.W.C.); American Association of Immunologists Careers in Immunology Fellowship and Travel for Techniques program (A.D.P. and K.L.G.-J.); Australian Research Council Future Fellowship FT170100174 and Discovery Project DP200102776, NHMRC Ideas grant 1182086 (N.L.L.G.); NHMRC Program grant 1054925 and Investigator award 1175411 (D.M.T. and K.O.D.); Australian Research Council Discovery Project DP170102020 and NHMRC Ideas grant 1183478 (S.J.T.); Monash University Biomedicine Discovery Institute Scholarship (L.C.); Monash University Scholarship for Excellence (J.P.); Monash University Graduate Scholarship and Monash International Postgraduate Research Scholarship (T.H.); Postgraduate Scholarship from the Bonn University – Melbourne University joining PhD program (V.U.); National Institutes of Health P01 AI106697 and the European Union’s Horizon 2020 Research and Innovation program 825821 (U.H. and A.S.). The contents of this document can under no circumstances be regarded as reflecting the position of the European Union.

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Authors and Affiliations

Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
Andrea Di Pietro, Jack Polmear, Lucy Cooper, Tabinda Hussain, Lauren Hailes, Nicole L. La Gruta & Kim L. Good-Jacobson
Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
Andrea Di Pietro, Jack Polmear, Lucy Cooper, Tabinda Hussain, Lauren Hailes, Vibha Udupa, Stephen J. Turner, Nicole L. La Gruta & Kim L. Good-Jacobson
Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
Timon Damelang, Vibha Udupa & Amy W. Chung
Department of Immunology & Pathology, Alfred Research Alliance, Monash University, Melbourne, Victoria, Australia
Kristy O’Donnell & David M. Tarlinton
Department of Microbiology, Monash University, Clayton, Victoria, Australia
Vibha Udupa & Stephen J. Turner
Department of Microbiology and Immunology, School of Medicine, Emory University, Atlanta, GA, USA
Tian Mi & Christopher D. Scharer
Divisions of Immunology and Molecular Immunology, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
Simon Preston
Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia
Simon Preston
Department of Human Biology, Faculty of Science, University of Haifa, Haifa, Israel
Areen Shtewe & Uri Hershberg

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Andrea Di Pietro
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Jack Polmear
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Lucy Cooper
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Tabinda Hussain
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Tian Mi
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Simon Preston
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Uri Hershberg
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Nicole L. La Gruta
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Kim L. Good-Jacobson
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Contributions

K.L.G.-J. conceived the study. K.L.G.-J., A.D.P., N.L.L.G. and A.W.C. designed research. A.D.P., J.P., L.C., L.H., T.D., V.U., K.O.D. and T.H. performed the research. S.J.T., N.L.L.G., A.W.C., C.D.S. and D.M.T. provided additional intellectual input. T.M., A.S., U.H., A.D.P. and C.D.S. analyzed deep-sequencing data. S.P. provided reagents and technical expertise. D.M.T. provided mice. K.L.G.-J. wrote the manuscript.

Corresponding author

Correspondence to Kim L. Good-Jacobson.

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Competing interests

K.L.G.-J. has received funding from GSK for a separate project. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks Deepta Bhattacharya, Tri Phan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Quality control for deep-sequencing dataset and overlap between differentially chromatin accessible regions and differentially expressed genes.

a, Data quality for the ATAC-seq was evaluated by calculating the Fraction of read in peaks (FRIP Score). b, Chromatin accessibility regions pie chart. c, Volcano plot for all significantly differentially accessible regions (DARs). d, Metrics for RNA-seq showing the sequencing library size of assigned reads. e, PCA plot in two dimensions of differential expressed genes in acute (salmon) and chronic (green) GC B cells. f, Histogram of the nominal p-values calculated by DESeq for synthetic data RNA-seq. g, Volcano plot for differentially expressed genes (DEGs) identified by RNA-seq. h, Within Sum of Square (WSS) plot for the optimal number of clusters determined by the K-mean analysis. i, Dot plot showing differential chromatin accessibility regions (DARs) or expressed genes (DEGs) (yellow dots) and the overlap between the two datasets (black dots). j, Heat maps for overlapped DARs (ATAC-Seq, left panel) and DEGs (RNA-Seq, right panel). k, PRC2 targets assessed within DARs (upper panels) or DEGs (lower panels); ****P < 0.0001,Wilcoxon matched-pairs signed rank test, two-tailed p-value. l, Plots of normalised counts for non-canonical PRC1 (top), PRC2 and PRC2 co-factors (bottom); n = 2 mice per group, data represent mean ± SEM.

Source data

Extended Data Fig. 2 BMI-1 expression in B cell subsets.

a, Schematic of NP⁻KLH in alum immunization and tamoxifen administration of Bmi1^CreERT2Rosa26^EYFP reporter mice. b, Flow cytometric representative plots EYFP expression in CD19⁺IgD^loCD95^hiCD38^lo (GC) and B220^loCD138^hi cells from immunized Bmi1Rosa26eYFP reporter mice. c, EYFP^- and EYFP⁺ frequencies within B220^loCD138^hi ASC of either IgG, IgA or IgM immunoglobulin isotypes were assessed by flow cytometry; n = 6 mice per group; data represent mean ± SEM; mLN: *P = 0.0411, Spleen: *P = 0.04152, **P = 0.0043 (Mann-Whitney U test, two-tailed p-value). d, Frequency of EYFP⁺ PB (B220^intCD138^hi) and PC (B220⁻CD138^hi) in mesenteric lymph nodes and spleen in immunized mice; n = 6 mice per group, combined from two independent experiments; data represent mean ± SEM; **P < 0.01 (Mann-Whitney U test, two-tailed p-value). e-i, Bmi1^CreERT2Rosa26^EYFP reporter mice and wild-type controls were infected with either LCMV-WE or LCMV-Docile as per schematic in Fig. 1. e, GC B cells were segregated into DZ and LZ subsets and assessed for EYFP⁺ frequency; WE: n = 5 mice, Docile: n = 3 mice; Data represent mean ± SEM. *P = 0.0357 (Mann-Whitney U test, two-tailed p-value). f-i, Assessment of Bmi1 by RT-qPCR in sort-purified populations as follows: f, EYFP⁻and EYFP⁺ lymphocytes in LCMV-Docile-infected mice (2^-𝜟𝜟Ct method relative to EYFP⁻); n = 7 mice per group; data represent mean ± SEM; ***P = 0.0006 (Mann-Whitney U test, two-tailed p-value). g, EYFP⁻and EYFP⁺ GC B cells responding to either LCMV-WE or LCMV-Docile (relative to acute EYFP⁻ GC B cells); n = 3 mice per group; data represent mean ± SEM; **P < 0.01 (Welch’s U test, two-tailed p-value). h, GC B cells from wild-type mice infected with either LCMV-WE or LCMV-Docile (relative to acute GC B cells), d14 post-infection; n = 10 mice per group; data represent mean ± SEM; ****P < 0.0001 (Mann-Whitney U test, two-tailed p-value). i, EYFP⁺ GC B cells and ASCs (relative to GC B cells); n = 3 mice per group; data represent mean ± SEM. *P = 0.0211 (Welch’s U test, two-tailed p-value). j-k, Kinetics of (j) Bmi1 and (k) BMI-1 expression in GC B cells isolated from wild-type mice infected with either LCMV-WE or LCMV-Docile. H3 loading control in bottom panel. RT-qPCR values for (j) combined from 1-3 experiments per timepoint (d7: WE n = 5, Docile n = 6; d14: n = 10 per group; d28: WE n = 4, Docile n = 3; d14 data also shown in h). Data represent mean ± SEM. **P < 0.01 (Mann-Whitney U test, two-tailed p-value). Gels and blots for (k) were processed in parallel, using the same samples.

Source data

Extended Data Fig. 3 Quantitation of innate and adaptive cell subsets.

Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice were infected with LCMV-Docile. a, Mouse weights assessed at the indicated time points post-infection (n = 18 Cd23^Cre/+ and n = 19 Bmi1^f/fCd23^Cre/+ mice; data represent mean ± SEM; **P = 0.0058, Mann-Whitney U test, two-tailed p-value). b, Innate immune cell subsets; c, CD4⁺ T cells; d, CD8⁺ T cells were assessed at d7. b-d, n = 8 mice per group, combined from two independent experiments (Mann-Whitney U test, two-tailed p-value). e, CD4⁺ T cells and f, CD8⁺ T cells were assessed at d21. n = 6 mice per group, combined from two independent experiments (Mann-Whitney U test, two-tailed p-value). g, Sort-purified CD44^hiCD8⁺ cells from Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice at d14 post-infection were seeded in wells with splenocytes so that effector (gp33⁺CD8⁺) to target ratio was either 2.5:1 or 1:1; n = 4 mice per group (Mann-Whitney U test, two-tailed p-value). h, Mean fluorescence intensity of IFNγ in gp33⁺CD8⁺ T cells; n = 5 mice per group (Mann-Whitney U test, two-tailed p-value). i, Histological analysis of uninfected spleens from Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice from an individual experiment; B220 (cyan), CD3 (magenta). Scale bar = 100μm.

Source data

Extended Data Fig. 4 Humoral responses to infection of BMI-1-deficient B cells.

Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice were infected with LCMV-Docile. a, Relative abundance of specific types of N-glycan structures (G0, agalactosylated; G1, monogalactosylated; G2, digalactosylated; F, fucosylated; Z, sialylated; M, mannose) of serum antibody at d14 post-infection. Data represent mean ± SEM, n = 5 per group, combined from two independent experiments. b, Frequency and c, number of unswitched B cells at the indicated time points; d, Frequency of GC B cells within the CD19⁺IgD^lo population and e, number of GC B cells in LCMV-Docile-infected mice at indicated time points. f, Frequency of IgG2c⁺ cells within the GC B cell population in LCMV-Docile-infected mice at the indicated time points. b-f, Data represent mean ± SEM; d7: n = 6-10 mice per group, d14: n = 12-16 Cd23^Cre/+ and n = 12-15 Bmi1^f/fCd23^Cre/+ mice, d21: n = 6 mice per group, d28: n = 6-8 Cd23^Cre/+ and n = 6-9 Bmi1^f/fCd23^Cre/+ mice; combined from at least two experiments per time point; *P < 0.05, **P < 0.01 (Mann-Whitney U test, two-tailed p-value). g, LCMV-specific serum IgG2c at d14 and d28 post-infection with LCMV-Docile; d14: n = 12 Cd23^Cre/+ and n = 15 Bmi1^f/fCd23^Cre/+ mice, d28: n = 10 Cd23^Cre/+ and n = 11 Bmi1^f/fCd23^Cre/+ mice; combined from at least four experiments per time point. h, Bcl2l11 and Pmaip1 expression in GC B cells isolated from Cd23^Cre/+ or Bmi1^f/fCd23^Cre/+ mice, d14 post-infection. Data represent mean ± SEM, n = 4 per group.

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Extended Data Fig. 5 Additional images of Myc expression within GCs.

a, Cd23^Cre/+ and b, Bmi1^f/fCd23^Cre/+ mice were infected with LCMV-Docile and GCs assessed at d7 post-infection as detailed in Fig. 4. Panels show individual staining (IgD, CD3, PNA and Myc; combined top row). Representative images of n = 4 mice per genotype from two individual experiments. Scale bar = 100μm. c, Number of unique synonymous and non-synonymous mutations per clone in CDR and FW regions of GC B cell V_H gene repertoire. GC B cells were isolated from Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice infected with either LCMV-WE or LCMV-Docile, d14 post-infection. d, Log ratio of non-synonymous to synonymous mutations in total CDR and total FW regions; median values shown. The boxplots (c, d) visualize the median (middle hinge), two hinges (first and third quartiles, 25^th and 75^th, respectively), two whiskers (values no larger than the inter-quartile range, 1.5*IQ), and all individual outlying points. e-f, Selection pressure in V_H CDR and FW regions; (e) shows individual mice, (f) selection pressure of pooled mice per genotype and infection.

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Extended Data Fig. 6 GC B cells are unaffected in Bmi1^f/fPrdm1^CreERT2 mice.

a, Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice were infected with either LCMV-WE or LCMV-Docile, GC B cells sort-purified 14d post-infection, and gene expression assessed by RNA-seq. Shown are DEGs identified across the four groups. b, Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice were immunized with NP⁻KLH in alum and serum NP-binding IgG1 and IgM assessed at d7 post-immunization; n = 8 Cd23^Cre/+ and n = 7 Bmi1^f/fCd23^Cre/+ mice, combined from two independent experiments. c, Antigen-specific GC B cells were assessed over time; d7: n = 8 Cd23^Cre/+ and n = 7 Bmi1^f/fCd23^Cre/+ mice, d14: n = 5 mice per group, d28: n = 9 Cd23^Cre/+ and n = 10 Bmi1^f/fCd23^Cre/+ mice; combined from at least two experiments per time point. d, Frequency of IgG1⁺ within the GC B cell population at d14 and d28 post-immunization; d14: n = 5 mice per group, d28: n = 9 Cd23^Cre/+ and n = 10 Bmi1^f/fCd23^Cre/+ mice. e, DZ and LZ GC B cell frequencies were assessed in immunized Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice; d7: n = 8 Cd23^Cre/+ and n = 7 Bmi1^f/fCd23^Cre/+ mice, d14: n = 5 mice per group, d28: n = 6 mice per group. Data represent mean ± SEM. f, Bmi1^CreERT2Rosa26^EYFP reporter mice and wild-type controls were immunized and treated as per schematic in Extended Data Fig. 2. EYFP^- and EYFP⁺ GC B cells were isolated and assessed for Myc as per Fig. 4; n = 9 mice per group, combined from two independent experiments (**P = 0.0019, Mann-Whitney U test, two-tailed p-value). g, CDR3 length of V_H186.2 sequences from GC B cells; n = 42 cells from Cd23^Cre/+ and n = 44 cell from Bmi1^f/fCd23^Cre/+ mice, combined from n = 3 mice per group. h, Naïve Bmi1^f/fPrdm1^CreER/+ and controls were administered tamoxifen as per schematic in Fig. 6a. Representative flow cytometric plots of GC B cells in mesenteric lymph nodes. i, Assessment of GC B cell number by flow cytometry; n = 5 mice per group, combined from two independent experiments per time point. Data represent mean ± SEM. j, Deletion analysis within B220^loCD138^hi ASC in Bmi1^f/fPrdm1^CreER/+ mice; representative of two independent experiments.

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Extended Data Fig. 7 PC are decreased in response to a small molecule inhibitor to BMI-1.

a, b, Representative flow cytometric plots of CTV-labelled wild-type B cells cultured with LPS and IL-4, and in the presence of PTC-209 at the indicated concentrations. Cells were assessed by flow cytometry for (a) ASC markers and (b) cell division, as determined by the dilution of CTV. c, Representative images of histological analysis of spleens 12d post-infection from Cd23^Cre/+ and Bmi1^f/fCd23^Cre/+ mice treated with either the vehicle control or PTC-028; B220 (cyan), CD3 (magenta). Two examples of B cell follicles per individual mouse; two individual mice within each experimental group shown. Scale bar = 100μm.

Extended Data Fig. 8 Flow cytometry gating scheme.

a, Gating strategy for sort purification of GCB for RNA-seq and ATAC-seq analysis (Figs. 1, 5; ED Figs. 1, 6). b, Gating strategy for GCB, GCB DZ and LZ distribution, and ASC (“Plasmacells”) used throughout the manuscript, and EYFP gating strategy for Bmi1 reporter mice (Fig. 1; Extended Data Fig. 2). c, Gating strategy for innate cell and T cell subsets (Extended Data Fig. 3). Note either B220 or CD19 were used to gate B cells.

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Di Pietro, A., Polmear, J., Cooper, L. et al. Targeting BMI-1 in B cells restores effective humoral immune responses and controls chronic viral infection. Nat Immunol 23, 86–98 (2022). https://doi.org/10.1038/s41590-021-01077-y

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Received: 20 January 2021
Accepted: 20 October 2021
Published: 29 November 2021
Issue Date: January 2022
DOI: https://doi.org/10.1038/s41590-021-01077-y

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Abstract

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Main

Results

BMI-1 is increased in B cells during chronic viral infection

Bmi1 deletion in B cells accelerates LCMV-Docile clearance

Increase in antibody function in the absence of BMI-1

Relationship of c-Myc and BMI-1 in chronic viral infection

H2AK119Ub1 is decreased at key loci in chronic infection

BMI-1 regulates ASCs in response to NP-KLH immunization

Small molecule inhibition of BMI-1 depletes ASCs

Discussion

Methods

Mice

Flow cytometry, cell sorting and antibodies

Histology

Immunofluorescence

Immunohistochemistry

Deletion analysis

RNA purification and quantification

ChIP–qPCR

ELISA and ELISpot

IC precipitation

T cell cytotoxicity assay and IFN-γ production

Bone-marrow chimeras

Plaque-forming unit assay and infectivity reduction assay

ADCC

IgG purification

IgG N-linked glycan profiling

In vitro B cell stimulation

Small molecule inhibitors

ATAC sequencing

RNA sequencing

IPA and GSEA

Vh sequencing

LCMV BCR sequencing

Immunoblot

Statistical analysis

Reporting Summary

Data availability

Code availability

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Acknowledgements

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