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The mitochondrial iron transporter ABCB7 is required for B cell development, proliferation, and class switch recombination in mice

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Cite this article as: eLife 2021;10:e69621 doi: 10.7554/eLife.69621

Abstract

Iron-sulfur (Fe-S) clusters are cofactors essential for the activity of numerous enzymes including DNA polymerases, helicases, and glycosylases. They are synthesized in the mitochondria as Fe-S intermediates and are exported to the cytoplasm for maturation by the mitochondrial transporter ABCB7. Here, we demonstrate that ABCB7 is required for bone marrow B cell development, proliferation, and class switch recombination, but is dispensable for peripheral B cell homeostasis in mice. Conditional deletion of ABCB7 using Mb1-cre resulted in a severe block in bone marrow B cell development at the pro-B cell stage. The loss of ABCB7 did not alter expression of transcription factors required for B cell specification or commitment. While increased intracellular iron was observed in ABCB7-deficient pro-B cells, this did not lead to increased cellular or mitochondrial reactive oxygen species, ferroptosis, or apoptosis. Interestingly, loss of ABCB7 led to replication-induced DNA damage in pro-B cells, independent of VDJ recombination, and these cells had evidence of slowed DNA replication. Stimulated ABCB7-deficient splenic B cells from CD23-cre mice also had a striking loss of proliferation and a defect in class switching. Thus, ABCB7 is essential for early B cell development, proliferation, and class switch recombination.

Introduction

Iron is critical for numerous cellular processes including ATP production, cellular metabolism, and DNA replication and damage repair (Fuss et al., 2015; Lane et al., 2015; Lill and Mühlenhoff, 2006). However, labile iron (nonprotein bound) is highly toxic to cells as it can potently induce the formation of reactive oxygen species (ROS) through the Fenton reaction (Lawen and Lane, 2013). To protect organisms and cells from excess ROS, iron homeostasis is highly regulated at both a systemic and cellular level, which balances iron storage, transport, and utilization (reviewed by Anderson and Frazer, 2017; Lawen and Lane, 2013). It is thought that most cellular iron uptake in lymphocytes occurs through transferrin receptor 1 (TfR1; CD71), which binds transferrin-bound iron at the cell surface (Lane et al., 2015; Lawen and Lane, 2013; Ned et al., 2003). Intracellular iron can be stored in ferritin, which sequesters iron to prevent it from undergoing the Fenton reaction (Lawen and Lane, 2013). Alternatively, intracellular iron can be transported to the mitochondria for storage in mitochondrial ferritin or utilization in the biosynthesis of heme and iron-sulfur (Fe-S) clusters (Lane et al., 2015), making mitochondria essential for iron homeostasis. Typically, iron uptake and storage are controlled by intracellular iron levels through the activity of iron-responsive element-binding protein 1 (IRP1) and IRP2 (Lane et al., 2015; Lawen and Lane, 2013). However, many aspects of mitochondrial iron trafficking and storage are still not well understood (Paul et al., 2017; Richardson et al., 2010).

Fe-S clusters are important cofactors for numerous proteins involved in cellular metabolism, DNA replication, and DNA damage repair (Fuss et al., 2015; Lane et al., 2015; Lill and Mühlenhoff, 2006). These proteins include ferredoxin (Lange et al., 2000); components of NADH:ubiquinone oxidoreductase (Complex I) (Ohnishi, 1998); DNA primase (Klinge et al., 2007); all of the replicative DNA polymerases (Netz et al., 2011, reviewed by Baranovskiy et al., 2018; Puig et al., 2017); the helicases Dna2, FancJ, and XPD (Mariotti et al., 2020; Rudolf et al., 2006); and the glycosylases Endo III and MutY (Cunningham et al., 1989; Porello et al., 1998). Fe-S cluster intermediates are first biosynthesized in the mitochondria, after which they are transported to the cytoplasm for maturation (Maio and Rouault, 2015; Rouault, 2012). The inner mitochondrial membrane protein ABCB7 is important for Fe-S cluster maturation as it is thought to export an Fe-S-glutathione intermediate from the mitochondria to the cytoplasm (Li and Cowan, 2015; Pondarré et al., 2006; Qi et al., 2014; Srinivasan et al., 2014). Previous work demonstrated that ABCB7 is essential for life as ABCB7-deficient embryos failed to develop extra-embryonic tissues and tissue-specific deletion revealed a requirement for development and function of numerous cell types (Pondarré et al., 2006). ABCB7 was also found to be critical for hematopoiesis as conditional deletion with Mx1-cre resulted in rapid bone marrow failure with pancytopenia (Pondarre et al., 2007), but the requirement of ABCB7 for the development of specific hematopoietic lineages was not examined. HeLa cells have been shown to accumulate mitochondrial iron in the absence of ABCB7 (Cavadini et al., 2007); however, hepatocytes and endothelial cells appeared to be viable without ABCB7 and did not have iron accumulation (Pondarré et al., 2006). Therefore, some cell types appear to possess a compensatory mechanism to export Fe-S-glutathione intermediates in the absence of ABCB7, while ABCB7 is critical for this function in other cell types.

While iron levels have been linked to the proliferation and function of peripheral lymphocytes (Jiang et al., 2019; Watanabe-Matsui et al., 2011; Yarosz et al., 2020 and reviewed by Cronin et al., 2019; Kuvibidila et al., 2003), their role in B cell development and peripheral B cell homeostasis has not been thoroughly characterized. Here, we show that ABCB7 is essential for B cell lymphopoiesis as B cell-specific conditional deletion of ABCB7 resulted in a severe block at the pro-B cell stage of development. We found that ABCB7-deficient pro-B cells accumulated iron, but did not have excess ROS or cell death. Gene expression changes indicated the near absence of pre-B cells. ABCB7-deficient pro-B cells also had reduced heavy chain recombination, and B cell development was restored upon introduction of a fully rearranged MD4 Hel-Ig transgenic B cell receptor (BCR). Interestingly, we found evidence that DNA damage was occurring in ABCB7-deficient pro-B cells independent of recombination. These results suggest that DNA damage was occurring during replication in the absence of ABCB7. Intriguingly, we found that ABCB7 was dispensable for peripheral B cell homeostasis. Using a B cell-specific CD23-cre to conditionally delete ABCB7 from peripheral B cells, we did not observe an obvious loss of B cell populations or numbers. However, we observed that CD23-cre ABCB7 cKO B cells had a striking defect in proliferation and class switching during in vitro class switch recombination (CSR) assays, but the severity of the defect was dependent upon stimulation signals received during culture. Pro-B cells bearing the fully rearranged MD4 Hel-Ig BCR had restored proliferation, likely as a result of IgM and IgD expression on these developing cells. These data demonstrate that ABCB7 is required for B cell development, proliferation, and CSR but is dispensable for peripheral B cell homeostasis.

Results

ABCB7 is required for pro-B cell development but is dispensable for peripheral B cell homeostasis

Previous literature demonstrated that ABCB7 is essential for hematopoiesis, but the role of ABCB7 in the development of specific hematopoietic lineages was not examined (Pondarre et al., 2007). To elucidate the role of ABCB7 in B cell development, ABCB7 was conditionally deleted by crossing ABCB7 floxed mice (Clarke et al., 2006) with Mb1-cre (Hobeika et al., 2006) transgenic mice, which express cre in early pre-pro-B cells (Fahl et al., 2009). Mb1-cre ABCB7 conditional knockout (cKO) mice had a severe reduction in B220+ CD19+ bone marrow B cells (Figure 1A, left-hand plots). A majority of the ABCB7-deficient B cells in Mb1-cre ABCB7 cKO mice were B220+ CD19+ CD43+ pro-B cells (Figure 1A, middle-left plots). Analysis of Hardy fractions (Hardy et al., 1991) in Mb1-cre ABCB7 cKO mice revealed a significant decrease in the proportion of developing B cells starting at fraction (Fr.) B, which had a threefold reduction when quantified as a percentage of live bone marrow cells (Figure 1C). There was also a nearly threefold reduction in the proportion of Fr. C cells in these mice (Figure 1C). A 13-fold decrease in the proportion of Fr. C’ cells and a nearly 70-fold decrease in the proportion of Fr. D cells in Mb1-cre ABCB7 cKO mice indicated a severe block during pro-B development (Figure 1A, middle-right plots, Figure 1C). Quantification of the absolute cell numbers for each Hardy fraction confirmed a striking block during pro-B development and loss of later B cell development stages in Mb1-cre ABCB7 cKO mice (Figure 1—figure supplement 1A). The ratio of the number of Fr. C cells over the number of Fr. C’ cells also supported a pro-B cell development block as Mb1-cre ABCB7 cKO mice had more Fr. C (pro-B) cells than Fr. C’ (large pre-B) cells (Figure 1—figure supplement 1B). Very few IgM+ cells were observed in Mb1-cre ABCB7 cKO mice, with dramatic reductions in the proportions and numbers of naïve Fr. E cells and recirculating Fr. F cells (Figure 1A, right-hand plots, Figure 1C, Figure 1—figure supplement 1A). These data indicate ABCB7-deficient cells were blocked at the pro-B cell stage and failed to continue development into pre-B cells. Because of this, there was a significant decrease in the number of peripheral CD19+ B cells in the spleen of Mb1-cre ABCB7 cKO mice (Figure 1B, left-hand plots, Figure 1D).

Figure 1 with 2 supplements see all
ABCB7 is required for pro-B cell development but not peripheral B cell homeostasis.

(A) Flow cytometry analysis of B cell development in bone marrow from wild-type (WT), Mb1-cre ABCB7 conditional knockout (cKO), and CD23-cre ABCB7 cKO mice. Pro-B cells were divided into Hardy fractions as follows: Fr. B (B220+ CD19+ CD43+ BP-1-), Fr. C (B220+ CD19+ CD43+ CD24lo BP-1+), and Fr. C’ (B220+ CD19+ CD43+ CD24hi BP-1+), Fr. D (B220+ CD19+ CD43-/low sIgM-), Fr. E (B220+ CD19+ CD43-/low sIgM+), and Fr. F (B220hi CD19+ CD43-/low sIgM+). Contour plots are representative of six independent experiments (total of 6–11 mice/group). (B) Flow cytometry analysis of splenic B cell populations in WT, Mb1-cre ABCB7 cKO, and CD23-cre ABCB7 cKO mice. Populations were identified by gating on CD19+ splenocytes: transitional type 1 (T1; AA4.1+CD21/35- IgM+ CD23-), transitional type 2 (T2; AA4.1+ CD21/35- IgM+ CD23+), transitional type 3 (T3; AA4.1+CD21/35+IgM+), follicular (FO; AA4.1- CD21/35+ IgM+), and marginal zone (MZ; AA4.1- CD21/35hi IgMhi). Contour plots are representative of seven independent experiments (total of 7–12 mice/group). (C) Graph showing the percentage of total live bone marrow cells for each Hardy fraction in (A). (D) Graph showing absolute cell numbers of splenic B cell populations in (B). (C, D) Lines represent the mean ± SEM. Statistics were obtained by using a one-way ANOVA with Dunnett’s test for multiple comparisons.

To determine if ABCB7 was required for establishment or maintenance of peripheral B cells, ABCB7 was conditionally deleted by crossing ABCB7 floxed mice with CD23-cre transgenic mice (Kwon et al., 2008), in which cre expression is under the control of the B cell-specific Cd23 promoter that is induced during the progression from transitional T1 to T2 B cell development (Kondo et al., 1994). These mice also express a human CD5 (huCD5) reporter linked to cre expression via an IRES (Kwon et al., 2008). CD23-cre ABCB7 cKO mice had normal proportions of each Hardy fraction in the bone marrow (Figure 1A and C), although the number of Fr. B and Fr. C’ cells was slightly reduced in these mice (Figure 1—figure supplement 1A). Expression of the huCD5 reporter was only observed in mature, recirculating cells (Fr. F) and was absent from any pro- or pre-B cell (Fr. B-D; Figure 1—figure supplement 2A), which was expected as CD23-cre is expressed in the periphery and Fr. F cells are recirculating. No differences were observed in the proportion or absolute number of CD19+ B cells in the spleen of CD23-cre ABCB7 cKO mice (Figure 1B, left-hand plots, Figure 1D), further suggesting that bone marrow B cell development is normal in these mice. There were no differences observed in the numbers of T1, T2, T3, follicular (FO), or marginal zone (MZ) B cells in CD23-cre ABCB7 cKO mice (Figure 1B and D), implying that peripheral B cell homeostasis in these mice was also unaffected by the absence of ABCB7. Expression of the huCD5 reporter was largely absent on the majority of T1 cells, while expressed on T2, a large majority of T3, FO, and MZ B cells (Figure 1—figure supplement 2B), confirming that the B cell-specific Cd23 promoter turns on at the transition from T1 to T2 B cells. Thus, using CD23-cre, the role of ABCB7 at the T1 stage cannot be analyzed. Additionally, quantitative PCR (qPCR) analysis confirmed the deletion of ABCB7 in sorted FO and MZ B cells from CD23-cre ABCB7 cKO mice (Figure 1—figure supplement 2C). These data demonstrate that ABCB7 is required for B cell development in the bone marrow, particularly in pro-B cells, but is dispensable for peripheral B cell homeostasis in the spleen.

Gene expression changes confirm absence of pre-B cells in Mb1-cre ABCB7 cKO mice

B cell development is dependent on the concerted activity of several critical transcription factors that activate the early B cell developmental program, inducing B cell specification and commitment, including Early B-Cell Factor 1 (EBF1) (Medina et al., 2004; O’Riordan and Grosschedl, 1999), E2A (E47; Tcf3) (Bain et al., 1994; Kwon et al., 2008; O’Riordan and Grosschedl, 1999), Forkhead Box O1 (FOXO1) (Dengler et al., 2008), and PAX5 (Fuxa et al., 2004; Nutt et al., 1997; Souabni et al., 2002). Additionally, combined heterozygous loss of EBF1 and E2A, PAX5, and/or FOXO1 can also disrupt B cell commitment and development (Lin et al., 2010; Ungerbäck et al., 2015). Therefore, protein expression of EBF1, E47, FOXO1, and PAX5 in ABCB7-deficient pro-B cells was analyzed by flow cytometry. No significant decrease in expression of any of these critical transcription factors was observed in Fr. C cells (B220+ CD19+ CD43+ BP-1+) in Mb1-cre ABCB7 cKO mice (Figure 2A–D). EBF1 expression had a slight, but significant, increase in expression in ABCB7-deficient Fr. C cells (Figure 2A). These results suggest that the B cell transcriptional program is intact in ABCB7-deficient pro-B cells.

Figure 2 with 1 supplement see all
Gene expression changes confirm absence of pre-B cells in Mb1-cre ABCB7 conditional knockout (cKO) mice.

Analysis of critical transcription factors in wild-type (WT) and Mb1-cre ABCB7 cKO Fr. C cells (B220+CD19+CD43+BP-1+). (A–G) Intracellular flow cytometry analysis of EBF1 (A), E47 (E2A) (B), FOXO1 (C), PAX5 (D), IKAROS (E), AIOLOS (F), and IRF4 (G) expression. Quantification of MdFI is shown on the right of each plot. Isotype controls are shown in gray. Offset histograms are representative of at least three independent experiments (total of 6–10 mice/group). (H, I) Flow cytometry analysis of CD2 (H) and CD25 (I) expression. Indicated values are the proportion of Fr. C cells positive for either marker, and quantifications are shown on the right of each plot. Offset histograms are representative of three independent experiments (total of five mice/group). (J) Intracellular flow cytometry analysis of TdT expression in Fr. B and Fr. C cells. Indicated values are the proportion of cells positive for TdT expression, and quantifications are shown on the right. Offset histograms are representative of three independent experiments (total of 5–7 mice/group). (K) Quantitative real-time PCR analysis of Rag1 and Rag2 expression in sorted Fr. B and Fr. C cells. 18S rRNA was used as an endogenous control, and relative expression values were normalized to expression in WT Fr. B cells. Results were obtained from three independent experiments (total of 3–4 mice/group). (A–K) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using an unpaired Student’s t-test.

As cells progress from the pro- to pre-B cell stage, signaling from the pre-B cell receptor (pre-BCR) induces upregulation of several transcription factors and surface markers, including IKAROS (Ikzf1) (Ferreirós-Vidal et al., 2013), AIOLOS (Ikzf3) (Thompson et al., 2007), interferon regulatory factor 4 (IRF4) (Lu, 2003), CD2, and CD25 (IL-2Rα) (Rolink et al., 1994; Yagita et al., 1989). Flow cytometric analysis of IKAROS, AIOLOS, and IRF4 revealed that developing B cells in Mb1-cre ABCB7 cKO mice failed to upregulate these transcription factors (Figure 2E–G). Additionally, the proportions of developing B cells with surface expression of CD2 and CD25, hallmarks of transition to the pre-B cell stage, were markedly decreased in Mb1-cre ABCB7 cKO mice (Figure 2H and I). Upon successful rearrangement of the immunoglobulin μ heavy chain (μHC), signals from the pre-BCR induce downregulation of recombination machinery components at the transcriptional and protein level: terminal deoxynucleotidyl transferase (TdT), Rag1, and Rag2 (Galler et al., 2004; Grawunder et al., 1995; Li et al., 1996). TdT expression was examined via flow cytometry and was found to be maintained in ABCB7-deficient Fr. C cells (Figure 2J, right plot). Interestingly, TdT was also highly expressed in Fr. B cells in Mb1-cre ABCB7 cKO mice as compared to WT mice (Figure 2J, left plot). There was not a statistically significant difference in Rag1 and Rag2 transcripts in sorted Fr. B and Fr. C cells (Figure 2K). Additionally, bone marrow from Mb1-cre ABCB7 cKO mice failed to yield pre-B cell colonies after 8 days in an IL-7-dependent colony-forming unit (CFU-pre-B) assay (Figure 2—figure supplement 1), confirming the absence of pre-B cells. Collectively, these experimental findings show that transcription factors required for B cell specification and commitment are normally expressed in ABCB7-deficient B cells, but changes in gene expression concomitant with successful traversal of the pro- to pre-B cell transition are altered.

ABCB7-deficient pro-B cells have increased intracellular iron, but lack evidence of iron-related cellular stress

Excessive labile iron (nonprotein bound) and heme can potently induce the generation of ROS or lead to ferroptosis, an iron-dependent form of cell death (Lawen and Lane, 2013; Li et al., 2020). Because it has been demonstrated that HeLa cells accumulate mitochondrial iron in the absence of ABCB7 (Cavadini et al., 2007), iron levels and the mitochondria were examined in ABCB7-deficient pro-B cells. We used Phen Green SK diacetate (Phen Green), which emits green fluorescence that is quenched in the presence of heavy metal ions such as iron (Petrat et al., 1999), to quantify intracellular iron levels. Mb1-cre ABCB7 cKO pro-B cells (B220+ CD19+ CD43+) had a fourfold increase in the proportion of cells with quenched Phen Green fluorescence compared to WT (Figure 3A), consistent with elevated cellular iron levels. A compensatory mechanism for export of Fe-S-glutathione intermediates in the absence of ABCB7 has been hypothesized as ABCB7-deficient liver and endothelial cells are viable and do not have increased iron accumulation (Pondarré et al., 2006). Intriguingly, peripheral CD19+ cells from CD23-cre ABCB7 cKO mice did not have iron accumulation as detected by Phen Green quenching (Figure 3—figure supplement 1), indicating that they possess a compensatory export mechanism for removing mitochondrial Fe-S-glutathione intermediates in the absence of ABCB7.

Figure 3 with 1 supplement see all
Iron accumulation in ABCB7-deficient pro-B cells. Analysis of mitochondria, iron accumulation, and reactive oxygen species (ROS) in wild-type (WT) and Mb1-cre ABCB7 conditional knockout (cKO) pro-B cells (B220+ CD19+ CD43+).

(A) Flow cytometry analysis of Phen Green SK fluorescence quenching by heavy metal atoms. Indicated values are the proportion of cells with quenched fluorescence, and quantification is shown on the right. Overlaid histogram is representative of five independent experiments (total of 10–11 mice/group). (B) Intracellular flow cytometry analysis of HO-1 expression. Quantification of HO-1 MdFI is shown on the right. Offset histogram is representative of three independent experiments (total of 5–7 mice/group). (C) Flow cytometry analysis of CD71 expression. Quantification of CD71 gMFI is shown on the right. Overlaid histogram is representative of three independent experiments (total of seven mice/group). (D) Flow cytometry analysis of mitochondria abundance (MitoTracker Green) and membrane potential (tetramethylrhodamine methyl ester [TMRM]). Quantification of MitoTracker Green volume-adjusted MdFI and TMRM volume-adjusted MdFI is shown on the right. Contour plots are representative of four independent experiments (total of seven mice/group). (E) Intracellular flow cytometry analysis of VDAC1 expression in Fr. C cells (B220+ CD19+ CD43+ BP-1+). Quantification of VDAC1 MdFI is shown on the right. Offset histogram is representative of three independent experiments (total of 5–7 mice/group). (F) Flow cytometry analysis of CellROX Green ROS detection probe. Quantification of CellROX Green volume-adjusted MdFI is shown on the right. Offset histogram is representative of five independent experiments (total of 7–8 mice/group). (G) Flow cytometry analysis of MitoSOX Red mitochondrial ROS detection probe. Indicated values are the proportion of cells positive for MitoSOX Red dye, and quantification is shown on the right. Offset histogram is representative of four independent experiments (total of 7–9 mice/group). (H) Flow cytometry analysis of Bodipy C-11 lipid peroxidation probe. Indicated values are the proportion of cells positive for oxidized Bodipy C-11, and quantification is shown on the right. Offset histogram is representative of three independent experiments (total of six mice/group). (I) Flow cytometry analysis of ThiolTracker Violet glutathione detection agent. Quantification of ThiolTracker Violet volume-adjusted MdFI is shown on the right. Overlaid histogram is representative of four independent experiments (total of 7–8 mice/group). (A–H) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using an unpaired Student’s t-test.

In addition to Fe-S clusters, iron-containing heme is synthesized in the mitochondria and has been shown to regulate differentiation and class switching in peripheral B cells (Lane et al., 2015; Lawen and Lane, 2013). Because heme synthesis occurs in the mitochondria, mitochondrial iron could be shunted into the heme synthesis pathway for export in the absence of ABCB7. The expression of heme oxygenase-1 (HO-1) was examined as a surrogate marker for heme levels as HO-1 expression is upregulated when intracellular heme levels increase (Watanabe-Matsui et al., 2011). Expression of HO-1 in Mb1-cre ABCB7 cKO pro-B cells was not significantly different compared to WT pro-B cells (Figure 3B). Therefore, there does not appear to be an increase in intracellular heme in ABCB7-deficient pro-B cells. ABCB7-deficient HeLa cell cultures displayed a cytoplasmic iron starvation phenotype, characterized by upregulated CD71 expression, despite accumulating mitochondrial iron (Cavadini et al., 2007). CD71 expression was not increased in ABCB7-deficient pro-B cells (Figure 3C), suggesting that they did not possess a similar cytoplasmic iron starvation phenotype. To assess mitochondria in ABCB7-deficient pro-B cells, MitoTracker Green and tetramethylrhodamine methyl ester (TMRM) labeling was analyzed by flow cytometry. MitoTracker Green measures mitochondrial abundance while TMRM labels active mitochondria with intact membrane potential (Floryk and Houstĕk, 1999). An alteration in mitochondrial abundance or active mitochondria was not observed in Mb1-cre ABCB7 cKO pro-B cells as MitoTracker Green and TMRM labeling were comparable to WT (Figure 3D). Additionally, Mb1-cre-ABCB7 cKO Fr. C cells did not have a difference in expression of VDAC1 (Figure 3E), an abundant anion channel in the outer mitochondrial membrane (Colombini, 2004). Because iron accumulation was occurring upon conditional deletion of ABCB7 in pro-B cells, elevated ROS may cause the observed block in B cell development. The dyes CellROX Green and MitoSOX Red were utilized to probe total cellular and mitochondrial ROS levels, respectively. Neither total cellular nor mitochondrial ROS were statistically different (Figure 3F and G), suggesting that there was not elevated ROS occurring in ABCB7-deficient pro-B cells.

To rule out ferroptosis, an iron-mediated form of regulated cell death, ABCB7-deficient pro-B cells were examined for the presence of lipid peroxides, a hallmark of ferroptotic cells (Li et al., 2020). To do so, Bodipy 581/591C-11 (Bodipy C-11) oxidation was assessed by flow cytometry. Bodipy C-11 emits red fluorescence until it is oxidized by lipid peroxides, after which the emission shifts to green fluorescence and can be used to quantify cells with increased lipid peroxides. There were no changes in oxidized Bodipy C-11 levels in Mb1-cre ABCB7 cKO pro-B cells (Figure 3H), indicating that there was not an increase of lipid peroxides characteristically found in ferroptotic cells. Additionally, intracellular glutathione (GSH) levels were analyzed utilizing the dye ThiolTracker Violet. GSH is an antioxidant utilized by cells to scavenge free radicals and other ROS, particularly lipid peroxides (Haenen and Bast, 2014; Li et al., 2020). Interestingly, a small but significant decrease in GSH levels was observed in ABCB7-deficient pro-B cells (Figure 3I). Together, these data demonstrate ABCB7-deficient pro-B cells accumulate iron, but this accumulation does not disrupt mitochondrial abundance or membrane potential, induce ferroptosis, or cause increased ROS formation.

ABCB7-deficient pro-B cells are not undergoing elevated apoptosis

As there was no evidence of ferroptosis in ABCB7-deficient pro-B cells (Figure 3G), apoptosis was examined. Expression of the pro-survival factors Bcl-xL and Mcl-1 is critical for B cell development (Grillot et al., 1996; Vikström et al., 2016) and is not decreased in ABCB7-deficient Fr. C cells compared to WT Fr. C cells (Figure 4A and B). Bcl2 protein expression could not be detected in WT or ABCB7-deficient Fr. C cells (Figure 4—figure supplement 1; WT Fr. A cells were used as a positive control), a finding in line with previous literature (Patton et al., 2014; Immgen: Heng et al., 2008). There was a small but significant increase in dead (Annexin V+ FVD+) ABCB7-deficient Fr. C’ cells, but there was not a significant increase in dead pro-B cells (Fr. B-C) from Mb1-cre ABCB7 cKO mice (Figure 4C). There was also no difference in the proportion of apoptotic (Annexin V+ FVD-) pro-B cells from Mb1-cre ABCB7 cKO mice (Figure 4D). Apoptotic and dead pro-B cells are rapidly eliminated in the bone marrow (Osmond et al., 1994). To overcome this and determine if ABCB7-deficient pro-B cells were less viable, WT and Mb1-cre ABCB7 cKO bone marrow were placed in overnight cultures (16 hr) and Annexin V binding was assessed the next day. There was no difference in apoptosis of cultured pro-B cells from Mb1-cre ABCB7 cKO compared to WT (Figure 4E). This indicates that elevated apoptosis was not responsible for the reduced pro-B cell numbers observed in Mb1-cre ABCB7 cKO (Figure 1C), and together these data suggest that elevated apoptosis was not responsible for the block in B cell development in the bone marrow of Mb1-cre ABCB7 cKO mice.

Figure 4 with 1 supplement see all
Analysis of apoptosis in ABCB7-deficient pro-B cells.

(A, B) Intracellular flow cytometry analysis of Bcl-xL (B) and Mcl-1 (C) expression in Fr. C cells (B220+ CD19+ CD43+ BP-1+) from wild-type (WT) and Mb1-cre ABCB7 conditional knockout (cKO) mice. Quantification of MdFI is shown on the right of each plot. Isotype controls are shown in gray. Offset histograms are representative of at least three independent experiments (total of 4–10 mice/group). (C) Flow cytometry analysis of Annexin V binding and fixable viability dye (FVD) labeling of Fr. B-C’ cells from WT and Mb1-cre ABCB7 cKO mice. Quantification of the proportion of each fraction that are dead (Annexin V+ FVD+) is shown on the right. Contour plots are representative of four independent experiments (total of 8–10 mice/group). (D) Quantification of the proportion of each fraction that are apoptotic (Annexin V+ FVD-) from (C). (E) Quantification of the proportion of each fraction that are apoptotic (Annexin V+ FVD-) after 16 hr in culture. Data represent three independent experiments (total of six mice/group). (A–E) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using an unpaired Student’s t-test.

Reduced heavy chain recombination in ABCB7-deficient pro-B cells

The block in Mb1-cre ABCB7 cKO pro-B cell development (Figure 1A and C) suggested that altered recombination or expression of intracellular μHC was occurring in ABCB7-deficient pro-B cells. Indeed, a large reduction in the proportion of ABCB7-deficient Fr. C cells that expressed intracellular μHC as observed by flow cytometry (Figure 5A). To determine if this was due to a defect in recombination, a semiquantitative PCR assay was used to analyze recombination of the heavy chain locus in enriched Mb1-cre ABCB7 cKO pro-B cells. This PCR assay utilized 5′ primers specific for the VH7183, VH3609, VHVgam3.8, or VHJ558 VH gene families and a 3′ primer specific for the JH3 gene (Angelin-Duclos and Calame, 1998; Li et al., 1993; Pelanda et al., 2002; Schlissel et al., 1991). Three product lengths were expected, depending on whether the VDJ recombination utilized JH1, JH2, or JH3 genes (JH1 products are underrepresented due to product size). WT pro-B cells had observable usage of each tested VH gene family (Figure 5B, left lanes). However, Mb1-cre ABCB7 cKO pro-B cells had a reduction in recombination of each of the VH gene families tested (Figure 5B, middle lanes). Additionally, the VH gene family usage that was observed in the ABCB7-deficient pro-B cells was skewed towards the more proximal VH7183 gene family, while the most distal VHJ558 gene family did not have detectable usage (Figure 5B, middle lanes). These results indicated that heavy chain recombination was largely reduced and skewed towards proximal VH gene families upon conditional deletion of ABCB7 in pro-B cells. Expression of sterile germline transcripts (GLT) of VH genes has been used as a measure of locus accessibility in developing B cells (Chen et al., 1993; Hesslein et al., 2003), although production of GLT is not required for recombination (Angelin-Duclos and Calame, 1998). To determine if there was a difference in VH GLT and therefore locus accessibility, qPCR analysis of VH7183 and VHJ558 GLT expression in sorted Fr. B and Fr. C cells from WT and Mb1-cre ABCB7 cKO mice was performed. Interestingly, expression of GLT for both VH gene families was found to be normal in both Fr. B and Fr. C cells (Figure 5C and D), suggesting that the reduction in heavy chain recombination was independent of locus accessibility. Collectively, these data suggest that heavy chain recombination and expression of μHC is reduced upon conditional loss of ABCB7 in developing pro-B cells and cannot be attributed to reduced locus accessibility.

Reduced heavy chain recombination in ABCB7-deficient pro-B cells.

(A) Intracellular flow cytometry analysis of μ heavy chain (μHC) expression in Fr. C cells (B220+ CD19+ CD43+ BP-1+) from wild-type (WT) and Mb1-cre ABCB7 conditional knockout (cKO) mice. Quantification of the proportion of μHC+ cells is shown on the left graph. Quantification of μHC gMFI is shown on the right graph. Contour plots are representative of six independent experiments (total of 12–15 mice/group). (B) Semiquantitative PCR analysis of heavy chain locus recombination. DNA was purified from magnetically enriched pro-B cells. DNA from magnetically enriched CD19+ WT splenocytes was used as a positive control, while DNA from a Rag2-deficient cell line was used as a negative control. DNA was adjusted to an equivalent concentration and subjected to threefold serial dilutions. Recombined VH gene segments were amplified using the indicated family-specific forward primer and a reverse primer specific to JH3. Three bands corresponding to the usage of JH1, JH2, and JH3 were expected for each VH gene amplified (JH1 band is underrepresented due to product length). Results are ordered from proximal (VH7183) to distal (VHJ558) VH gene families. Actin was used as a loading control. Results are representative of four independent experiments. Image contrast and brightness were adjusted and colors were inverted for the final image. Source images are provided in Figure 5—source data 1. (C, D) Quantitative real-time PCR analysis of sterile VH7183 (C) and VHJ558 (D) germline transcript (GLT) expression in FACS sorted Fr. B (B220+ CD19+ CD43+ BP-1-) and Fr. C cells. Hprt1 was used as an endogenous control, and relative expression values were normalized to expression in WT Fr. B cells. Results were obtained from three independent experiments (total of 4–5 mice/group). (A, C, D) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained using an unpaired Student’s t-test.

Figure 5—source data 1

Source images.

This zip archive contains all raw gel images taken for semiquantitative PCR data shown in Figure 5B. Gels were photographed using an Omega Lum G gel imager, which saved the raw image files provided here. Individual files were named based on the VH gene family that was analyzed, and images were saved as full-resolution, 16-bit grayscale TIFF files. In addition to the unedited gel images, a labeled image is provided (named as ‘labeled’) for each gel.

https://cdn.elifesciences.org/articles/69621/elife-69621-fig5-data1-v1.zip

The MD4 HEL-Ig BCR transgene normalizes bone marrow B cell populations and restores splenic B cells in Mb1-cre ABCB7 cKO mice

Because decreased recombination or failure to express a rearranged heavy chain (Figure 5A and B) would cause the observed block in pro-B cell development (Figure 1A and C), Mb1-cre ABCB7 cKO mice were crossbred with mice bearing a transgenic, fully rearranged BCR specific to hen egg lysozyme (HEL; HEL-Ig; Goodnow et al., 1988; Mason et al., 1992). HEL-Ig WT and HEL-Ig Mb1-cre ABCB7 cKO mice had comparable proportions of CD19+ cells in the bone marrow (Figure 6A). However, absolute numbers of CD19+ cells (Figure 6B, left graph) were still reduced in the bone marrow of HEL-Ig ABCB7-deficient mice, despite normal proportions of CD19+ cells in these mice (Figure 6B, right graph). Importantly, splenic B cell proportions and cell numbers were equivalent between HEL-Ig WT and HEL-Ig Mb1-cre ABCB7 cKO mice (Figure 6C and D), showing that introduction of a fully rearranged BCR is able to restore splenic B cells in Mb1-cre ABCB7 cKO mice. Interestingly, HEL-Ig Mb1-cre ABCB7 cKO B cells from bone marrow (left plot) and spleen (right plot) had elevated intracellular iron as indicated by Phen Green quenching (Figure 6E). This demonstrated that elevated intracellular iron in ABCB7-deficient cells is not overtly toxic to mature B cells. Together, these data demonstrate that peripheral B cell numbers in Mb1-cre ABCB7 cKO mice can be restored upon introduction of a fully rearranged BCR.

MD4 HEL-Ig transgenic B cell receptor (BCR) normalizes bone marrow B cell populations and restores splenic B cells in Mb1-cre ABCB7 conditional knockout (cKO) mice.

(A) Flow cytometry analysis of B cell development in bone marrow from wild-type (WT) and Mb1-cre ABCB7 cKO mice in the presence or absence of a fully rearranged transgenic BCR specific for hen egg lysozyme (HEL-Ig). B cell populations were identified by gating on B220+ CD19+ cells: pro-B cells (CD43+), BP-1+ pro-B cells, and Fr. E/F cells (CD43-/low IgM+). Contour plots are representative of seven independent experiments (total of 9–13 mice/group). (B) Graphs showing CD19+ absolute cell numbers (left) and percentage of live cells (right) in the bone marrow of mice analyzed in (A). (C) Flow cytometry analysis of splenic CD19+ cells in mice from (A). Contour plots are representative of seven independent experiments (total of 9–13 mice/group). (D) Graph showing absolute cell numbers of CD19+ cells in the spleen of mice analyzed in (C). (E) Flow cytometry analysis of Phen Green SK fluorescence quenching by heavy metal ions in bone marrow and splenic CD19+ cells from WT and Mb1-cre ABCB7 cKO mice. Indicated values are the proportion of cells with quenched fluorescence, and quantification is shown on the right. Offset histograms are representative of three independent experiments (total of 3–5 mice/group). (B, D, E) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using an unpaired Student’s t-test.

Reduced proliferation and evidence of DNA damage in ABCB7-deficient pro-B cells

One explanation for the reduction in heavy chain protein in ABCB7-deficient pro-B cells may be inability to repair double-stranded DNA breaks during VDJ recombination. Importantly, the Fe-S-GSH intermediates transported by ABCB7 mature into cofactors used in numerous DNA replication and damage repair enzymes including DNA primase, all replicative DNA polymerases, Dna2, FancJ, XPD, Endo III, and MutY (Baranovskiy et al., 2018; Cunningham et al., 1989; Fuss et al., 2015; Klinge et al., 2007; Mariotti et al., 2020; Netz et al., 2011; Porello et al., 1998; Puig et al., 2017; Rudolf et al., 2006). As recombination only occurs in nonproliferating cells due to Rag2 protein degradation (Lin and Desiderio, 1994), DNA damage was assessed by analyzing pH2A.X (γH2A.X) expression (Smith et al., 2010) in parallel with a 3 hr EdU pulse to identify proliferating pro-B cells. Strikingly, pH2A.X was highly expressed in EdU+ ABCB7-deficient Fr. B (B220+ CD19+ CD43+ BP-1-) and Fr. C (B220+ CD19+ CD43+ BP-1+) cells compared to EdU+ WT cells (Figure 7A and B). EdU- ABCB7-deficient cells did not have elevated expression of pH2A.X (Figure 7A and B), indicating that DNA damage was occurring in proliferating cells and was not due to heavy chain recombination. Analysis of EdU incorporation revealed that ABCB7-deficient Fr. B and Fr. C cells incorporated reduced amounts of EdU over the 3 hr pulse (Figure 7C and D), suggesting that these cells were undergoing slower DNA replication and reduced proliferation, consistent with induction of the S-phase DNA damage checkpoint (Ciardo et al., 2019). Because pH2A.X expression was elevated in ABCB7-deficient pro-B cells, expression of poly (ADP-ribose) polymerase (PARP), an important sensor of DNA damage that recruits repair enzymes (Wang et al., 2019), was analyzed and found to be significantly elevated in ABCB7-deficient Fr. C cells (Figure 7E). These data suggest that DNA damage-sensing pathways were active in ABCB7-deficient pro-B cells.

Reduced proliferation and evidence of DNA damage in ABCB7-deficient pro-B cells.

(A) Intracellular flow cytometry analysis of EdU incorporation and pH2A.X expression in Fr. B (B220+ CD19+ CD43+ BP-1-) and Fr. C (B220+ CD19+ CD43+ BP-1+) cells from wild-type (WT) and Mb1-cre ABCB7 conditional knockout (cKO) mice. Cells were pulsed with EdU for 3 hr in culture. Contour plots are representative of three independent experiments (total of three mice/group). (B) Quantification of the proportion of EdU- cells (left graph) and EdU+ cells (right graph) that were positive for pH2A.X expression. (C) Flow cytometric analysis of the proportion of Fr. B and Fr. C cells (A) that incorporated EdU. Histograms are representative of three independent experiments (total of three mice/group). (D) Quantification of the proportion of cells that incorporated EdU (left plot) and EdU gMFI (right graph) in Fr. B and Fr. C cells from (C). gMFI was normalized to WT Fr. B cells. (E–K) Intracellular flow cytometry analysis of PARP (E), pChk1 (F), total Chk1 (G), p53 (H), pATM (I), and CDK2 (J), and Ki-67 (K) expression in Fr. C cells from WT and Mb1-cre ABCB7 cKO mice. Quantification of the MdFI or percent positive is shown on the right of each plot. Isotype controls are shown in gray. Offset and overlaid histograms are representative of at least three independent experiments (total of 4–10 mice/group). (L) Analysis of cell cycle status using intracellular DAPI staining in Fr. B and Fr. C cells from WT and Mb1-cre ABCB7 cKO mice. Leftmost gate marks cells in G1, middle gate marks cells in S phase, and rightmost gate marks cells in G2/M phases. Values shown above gates were derived from the FlowJo cell cycle analysis modeling tool. Quantification of the proportion of cells in G1, S, and G2/M phases is shown on the right of the plot. Proportions were determined by using the FlowJo cell cycle analysis modeling tool. Offset histograms are representative of six independent experiments (total of 8–10 mice/group). (B, D–L) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using an unpaired Student’s t-test.

To prevent genomic instability during replication, the S-phase checkpoint slows replication in the presence of DNA damage (Ciardo et al., 2019). Checkpoint kinase 1 (Chk1) is an effector kinase that enforces the S-phase checkpoint until DNA damage is resolved and is required for B cell development at the pro- to pre-B cell transition (Boddy et al., 1998; Feijoo et al., 2001; Schuler et al., 2017). Analysis by flow cytometry revealed a slight but significant decrease in the expression of phosphorylated Chk1 (pChk1) in ABCB7-deficient Fr. C cells (Figure 7F). There was not a significant decrease in the expression of total Chk1 in these cells (Figure 7G). Intriguingly, this suggests that the activation of effector kinases during the S-phase checkpoint in ABCB7 deficient pro-B cells is partially diminished. The tumor suppressor p53 is a downstream target of the Chk1 during the S-phase checkpoint and is stabilized to control the cell cycle in the presence of DNA damage (Shieh et al., 2000). p53 expression was analyzed in ABCB7-deficient Fr. C cells and was also found to be significantly decreased in these cells (Figure 7H). To determine if DNA damage-sensing pathways were altered upstream of Chk1 activation, expression of phosphorylated ataxia-telangiectasia mutated (pATM), a checkpoint kinase upstream of Chk1 that is activated in the presence of DNA damage (Smith et al., 2010), was analyzed. Expression of pATM was significantly reduced in ABCB7-deficient Fr. C cells (Figure 7I), suggesting that the DNA damage response was less active in ABCB7-deficient pro-B cells, despite increased pH2A.X expression in proliferating cells.

Cyclin-dependent kinase 2 (CDK2) acts both upstream and downstream of Chk1 in the presence of DNA damage and strengthens the S-phase checkpoint. Loss of CDK2 expression delayed S/G2 progression in the presence of DNA damage and knockdown of CDK2 promoted cell cycle exit, including decreased expression of the proliferation-associated marker Ki-67 as well as Chk1 phosphorylation (Bačević et al., 2017). CDK2 expression was significantly decreased in ABCB7-deficient Fr. C cells (Figure 7J). Ki-67 was also analyzed in Fr. C cells from Mb1-cre ABCB7 cKO mice and was strikingly decreased compared to WT Fr. C cells (Figure 7K), suggesting that ABCB7-deficient cells had lost proliferation potential and were dropping out of the cell cycle in response to DNA damage occurring during DNA replication. Together, the reduction in CDK2 and loss of Ki-67 are consistent with an extended S-phase checkpoint. Cell cycle analysis using DAPI revealed that Fr. B cells from Mb1-cre ABCB7 cKO mice had a larger percentage of cells in G1 and a reduction in the percentage of cells in G2, while the proportion of cells in S phase was unchanged (Figure 7L, left-hand plot and graph). Fr. C cells had evidence that fewer cells were progressing through the cell cycle as more cells were in G1, and fewer cells were in S and G2 phases (Figure 7L, right-hand plots and graph). These data support the loss of proliferation potential in ABCB7-deficient pro-B cells as more cells are in G1 and fewer Fr. C cells are progressing through S and G2/M phases. Thus, ABCB7-deficient pro-B cells show evidence of replication-induced DNA damage, slower replication, and loss of proliferative capacity.

ABCB7 is required for peripheral B cell proliferation and class switching

Cell proliferation has a well-characterized role in efficient CSR, and inhibition of proliferation can result in reduced class switching in activated B cells (Hasbold et al., 1998; Hodgkin et al., 1996; Limon et al., 2014; Rush et al., 2005; Stavnezer et al., 2008). Because evidence of reduced proliferation was observed in ABCB7-deficient pro-B cells, proliferation and CSR were examined in splenic B cells from CD23-cre ABCB7 cKO mice. To do so, enriched B220+ CD19+ B cells were cultured for 4 days with lipopolysaccharide (LPS) and various cytokines and/or anti-IgD dextran to induce proliferation and class switching to IgG1, IgG2a, IgG2b, IgG3, or IgA (see Materials and methods, Guikema et al., 2010). Despite normal proportions and numbers of splenic B cells in CD23-cre ABCB7 cKO mice, cells from these mice had a significant defect in class switching upon stimulation in culture (Figure 8A and B). While IgG2a- and IgA-stimulating cultures did not have a significant difference in the proportion of class-switched cells (Figure 8B, top graph), all conditions had a striking decrease in the number of cells that class switched in these cultures (Figure 8B, bottom graph). Interestingly, IgG1- and IgG2b-stimulating conditions had a more profound defect in class switching, both in proportion and numbers of class-switched cells (Figure 8A and B). This suggests that the severity of the defect in class switching in the absence of ABCB7 was dependent upon stimulation signals. No differences in the proportion of class-switched B cells were observed in the spleens of naïve, unchallenged mice (Figure 8—figure supplement 1).

Figure 8 with 3 supplements see all
ABCB7 is required for peripheral B cell proliferation and class switching.

(A) Flow cytometry analysis of IgG1, IgG2a, IgG2b, IgG3, and IgA expression on enriched B220+ CD19+ B cells from wild-type (WT) and CD23-cre ABCB7 conditional knockout (cKO) mice after 4 days in culture conditions that induce class switching to the indicated isotypes. Pseudocolor dot plots are representative of five independent experiments (total of five mice/group). (B) Quantification of the proportion (top) and number (bottom) of cells from (A) that class switched to the indicated antibody isotypes. The reported cell number was derived from flow cytometry live CD19+ cells during analysis. (C) Flow cytometry analysis of carboxyfluorescein diacetate succinimidyl diester (CFSE) dilution in cells from (A). Offset histograms are representative of five independent experiments (total of five mice/group). (D) FlowJo proliferation modeling tool was used to quantify the proliferation index (left) and percentage of undivided cells (right) in cells from (C). (E) Intracellular flow cytometric analysis of Ki-67 expression in proliferating B220+ CD19+ cells after 4 days in culture conditions that induce class switching. Contour plots are representative of three independent experiments (total of three mice/group). Quantification of the percentage of undivided, Ki-67- cells is shown on the graph on the right. (B, D, E) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using an unpaired Student’s t-test.

Analysis of cell proliferation, which was quantified by CFSE dilution, revealed that B220+ CD19+ cells in each class switch culture condition had significant defects in proliferation in the absence of ABCB7 (Figure 8C and D). ABCB7-deficient B cells stimulated in these cultures underwent fewer cell divisions (quantified as proliferation index, Figure 8D, left graph) and a larger number of cells were undivided (Figure 8D, right graph). As seen with expression of antibody isotypes above, the severity of the defect in proliferation in the absence of ABCB7 was context dependent, with IgG1- and IgG2b-stimulating conditions having a stronger effect on proliferation (Figure 8C and D). Because ABCB7-deficient cells had less robust proliferation, cell viability was observed over time in IgG1-stimulating culture conditions. WT cells had a clear increase in cell viability after 2 days of stimulation, consistent with robust proliferation, which was not evident in CD23-cre ABCB7 cKO IgG1-stimulating cultures (Figure 8—figure supplement 2A, solid lines). Interestingly, unstimulated CD23-cre ABCB7 cKO B cells did not have altered viability compared to unstimulated WT cells (Figure 8—figure supplement 2A, dashed lines), suggesting that this difference in cell viability is only apparent in stimulated cells due to altered proliferation. The dtableecrease in ABCB7-deficient cell viability was observed in each culture condition tested, with ABCB7-deficient cells in IgG1- and IgG2b-stimulating cultures having a more severe decrease (Figure 8—figure supplement 2B) and a corresponding profound decrease in the number of live cells recovered (Figure 8—figure supplement 2C). Thus, peripheral B cells from CD23-cre ABCB7 cKO mice have reduced proliferation and cell viability. As reduced Ki-67 expression was observed in ABCB7-deficient pro-B cells, Ki-67 expression was analyzed in splenic B cells after 4 days in class switch culture conditions. Stimulated splenic B cells from CD23-cre ABCB7 cKO mice had a larger number of undivided, Ki-67- cells compared to WT mice (Figure 8E), consistent with a loss of proliferation potential in the absence of ABCB7. Intriguingly, there were no differences in Ki-67 expression in T1, T2, T3, FO, or MZ B cells from the spleen of CD23-cre ABCB7 cKO mice analyzed ex vivo (Figure 8—figure supplement 3). These data suggest that the reduced cell proliferation after in vitro stimulation affected CSR in ABCB7-deficient peripheral B cells, and the severity of the defect was dependent upon stimulation signals received. Together, these data demonstrate that ABCB7 is essential for splenic B cell proliferation and class switch after activation.

Improved proliferation of B cells from HEL-Ig Mb1-cre ABCB7 cKO mice

ABCB7-deficient pro-B cells had reduced Ki-67 expression, fewer cells progressing through the cell cycle, and reduced EdU incorporation (Figure 7), suggesting that these cells have reduced proliferation potential and slower DNA replication. Additionally, these ABCB7-deficient pro-B cells had evidence of DNA damage in proliferating cells, but not in nonproliferative cells that would be undergoing heavy chain recombination (Figure 7). This indicated that nonproliferating, ABCB7-deficient pro-B cells undergoing heavy chain recombination were not accumulating DNA damage. ABCB7-deficient splenic B cells also had reduced proliferation, class switching, and Ki-67 expression upon stimulation in culture, but the severity of the defect was signal-dependent (Figure 8). Therefore, it was intriguing that peripheral B cell proportions and numbers were restored in the spleens of HEL-Ig Mb1-cre ABCB7 cKO mice (Figure 6). Interestingly, there was no difference in Ki-67 expression between HEL-Ig WT and HEL-Ig Mb1-cre ABCB7 cKO pro-B cells (Figure 9A). Additionally, analysis of cell cycle status using DAPI revealed that ABCB7-deficient HEL-Ig CD127+ pro-B cells had equivalent proportions of cells in G1, S, and G2/M phases (Figure 9B). These data suggest that ABCB7-deficient pro-B cells in HEL-Ig Mb1-cre ABCB7 cKO mice have intact proliferation potential. Confirming this, the proportion of cells with EdU incorporation after a 3 hr pulse was equivalent between HEL-Ig WT and HEL-Ig Mb1-cre ABCB7 cKO pro-B cells (Figure 9D, left graph). Similar to ABCB7-deficient pro-B cells (Figure 7), ABCB7-deficient HEL-Ig pro-B cells had a significant reduction in EdU gMFI (Figure 9D, right graph), indicating that DNA replication in HEL-Ig Mb1-cre ABCB7 cKO pro-B cells was slowed in the absence of ABCB7. In addition, like Mb1-cre ABCB7 cKO pro-B cells, HEL-Ig Mb1-cre ABCB7 cKO pro-B cells had increased expression of pH2A.X in proliferating cells but not in nonproliferating cells (Figure 9E and F). These data demonstrate that in the presence of a fully rearranged BCR, ABCB7-deficient cells have restored proliferation potential and EdU incorporation. HEL-Ig mice bear a fully rearranged BCR, with μ and δ constant regions under endogenous control of the Eμ enhancer, that is expressed early during B cell development and pro-B cell development is altered in these mice (Goodnow et al., 1988). Therefore, the presence of BCR signals received in developing pro-B cells in HEL-Ig mice may rescue proliferation in the absence of ABCB7. Expression of IgM and IgD on WT, Mb1-cre ABCB7 cKO, HEL-Ig WT, and HEL-Ig Mb1-cre ABCB7 cKO B220+CD19+CD43+ cells was analyzed by flow cytometry. As expected, IgM and IgD were not detected on WT and Mb1-cre ABCB7 cKO pro-B cells while their HEL-Ig counterparts had a clear increase in IgM expression and a slight increase in IgD expression (Figure 9G). Interestingly, HEL-Ig Mb1-cre ABCB7 cKO pro-B cells had a reduction in IgM expression and an increase in IgD expression compared to HEL-Ig WT pro-B cells (Figure 9G). Together, these data show that despite slowed proliferation and evidence of DNA damage in proliferating cells, HEL-Ig ABCB7-deficient pro-B cells are able to reconstitute the peripheral B cell compartment, which may be due to signals received at the pro-B cell stage through IgM and IgD.

Improved proliferation of B cells from HEL-Ig Mb1-cre ABCB7 conditional knockout (cKO) mice.

(A) Intracellular flow cytometry analysis of Ki-67 expression in pro-B cells (B220+ CD19+ CD43+ CD127+) from HEL-Ig wild-type (WT) and HEL-Ig Mb1-cre ABCB7 cKO mice. Quantification of the percent of Ki-67+ cells is shown on the right. Offset histogram is representative of three independent experiments (total of three mice/group). (B) Analysis of cell cycle status using intracellular DAPI staining in CD127+ pro-B cells from HEL-Ig WT and HEL-Ig Mb1-cre ABCB7 cKO mice. Leftmost gate marks cells in G1, middle gate marks cells in S phase, and rightmost gate marks cells in G2/M phases. Values shown above gates were derived from the FlowJo cell cycle analysis modeling tool. Quantification of the proportion of cells in G1, S, and G2/M phases is shown on the right of the plot. Proportions were determined by using the FlowJo cell cycle analysis modeling tool. Offset histograms are representative of four independent experiments (total of four mice/group). (C) Intracellular flow cytometric analysis of the proportion of pro-B cells from HEL-Ig WT and Hel-Ig Mb1-cre ABCB7 cKO mice that incorporated EdU. Cells were pulsed with EdU for 3 hr in culture. Histograms are representative of four independent experiments (total of four mice/group). (D) Quantification of the proportion of cells that incorporated EdU (left plot) and EdU gMFI (right plot) in pro-B cells from (C). (E) Intracellular flow cytometry analysis of pH2A.X expression in pro-B cells from (C). Contour plots are representative of four independent experiments (total of four mice/group). (F) Quantification of the proportion of EdU- cells (left graph) and EdU+ cells (right graph) that were positive for pH2A.X expression. (G) Flow cytometry analysis of IgM (left) and IgD (right) expression in pro-B cells from WT, Mb1-cre ABCB7 cKO, HEL-Ig WT, and HEL-Ig Mb1-cre ABCB7 cKO mice. Quantification of the MdFI is shown on the right. Offset histograms are representative of three independent experiments (total of three mice/group). Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using a one-way ANOVA with Tukey’s multiple comparisons test. (A, B, D, F) Error bars represent SEM, and p-values are indicated above the data. Statistics were obtained by using an unpaired Student’s t-test.

Discussion

Here, we demonstrate that ABCB7 is critical for bone marrow pro-B cell development and for proliferation and CSR in splenic B cells, but dispensable for peripheral B cell homeostasis. Mb1-cre ABCB7 cKO mice had notable iron accumulation and a severe block during pro-B cell development (Figures 1 and 3A). Strikingly, ABCB7 was not required for splenic B cell homeostasis as CD23-cre ABCB7 cKO mice had normal populations and numbers of peripheral B cells (Figure 1). Surprisingly, splenic B cells deficient in ABCB7 did not exhibit iron accumulation (Figure 3—figure supplement 1). The block in pro-B cell development was not due to alterations in critical transcription factors, iron-related cellular stress, or elevated apoptosis (Figures 24). ABCB7-deficient cells had significantly reduced expression of intracellular μHC and diminished recombination at the heavy chain locus (Figure 5). These data suggested that ABCB7-deficient pro-B cells either had defective recombination or were halted in pro-B development prior to recombination.

Introduction of a fully rearranged transgenic BCR was able to restore bone marrow B cell proportions and splenic B cell numbers in Mb1-cre ABCB7 cKO, despite these cells still having iron accumulation (Figure 6). Analysis of proliferation using short-term EdU labeling demonstrated that fewer ABCB7-deficient pro-B cells incorporated EdU and that ABCB7-deficient pro-B cells incorporated less EdU compared to WT cells (Figure 7C and D). Interestingly, ABCB7-deficient pro-B cells that incorporated EdU had elevated expression of pH2A.X compared to WT pro-B cells (Figure 7A and B), which suggested an increase in DNA damage in proliferating cells but not cells undergoing heavy chain recombination. Pro-B cells from Mb1-cre ABCB7 cKO mice also had altered DNA damage sensing and a striking loss of proliferation potential as measured by Ki-67 expression (Figure 7K). Interestingly, pro-B cell proliferation was found to be restored in developing B cells bearing a fully rearranged transgenic HEL-Ig receptor, despite these cells still having evidence of elevated DNA damage in the absence of ABCB7 (Figure 9). This indicates that the defect in pro-B cell development in the absence of ABCB7 is likely due to a proliferation defect rather than simply an accumulation of DNA damage. How proliferation is restored in HEL-Ig Mb1-cre ABCB7 cKO pro-B cells remains to be seen. It may be due to the nature of the signal received by HEL-Ig pro-B cells as normally pro-B cells receive signals through the pre-BCR to pass the heavy chain checkpoint, but would receive signals through IgM and IgD in HEL-Ig transgenic mice instead (Figure 9G).

Although there was no defect in peripheral B cell homeostasis, ABCB7-deficient splenic B cells also had a significant defect in proliferation and Ki-67 expression during in vitro CSR assays (Figure 8), demonstrating that ABCB7 is critical for peripheral B cell proliferation as well. Intriguingly, some conditions in the CD23-cre ABCB7 cKO class switching cultures had a more profound defect in B cell proliferation and class switching, suggesting that different signaling pathways influence the severity of the proliferation defect. Together, these data demonstrate that ABCB7 is required for proliferation, pro-B cell development, and CSR.

Previous literature has demonstrated the importance of ABCB7 in iron homeostasis as some cell types accumulate mitochondrial iron, have defects in Fe-S cluster and heme synthesis, and have altered cytoplasmic aconitase activity upon the loss of ABCB7 (Cavadini et al., 2007; Pondarré et al., 2006). In agreement with these findings, we found that conditional deletion of ABCB7 in pro-B cells resulted in iron accumulation as indicated by Phen Green quenching (Figure 3A). Unexpectedly, we did not observe iron accumulation occurring in splenic B cells upon conditional deletion of ABCB7 in CD23-cre ABCB7 cKO mice at homeostasis. It has been hypothesized that other transporters may compensate for the loss of ABCB7, at least in some cell types. Liver-specific deletion of ABCB7 did not result in iron overload, which the authors suggested was due to unique iron homeostasis in hepatocytes or the existence of a complementary iron exporter (Pondarré et al., 2006). ABCB7 was also found to be dispensable for endothelial cells, although iron levels were never quantified in these cells (Pondarré et al., 2006). One candidate transporter that may share redundant function with ABCB7 is ABCB8, which is also thought to also transport Fe-S clusters to the cytoplasm. ABCB8-deficient cardiomyocytes displayed elevated levels of mitochondrial iron and ROS (Ichikawa et al., 2012). However, pro-B cells and follicular B cells express ABCB8 at similar levels (Immgen: Heng et al., 2008), suggesting that ABCB8 is likely not compensating for ABCB7-deficiency in CD23-cre ABCB7 cKO peripheral B cells at homeostasis or upon stimulation in class switch cultures. Cardiomyocytes also express ABCB7, which did not appear to compensate for loss of ABCB8 expression (Ichikawa et al., 2012; Kumar et al., 2019), implying that ABCB7 or ABCB8 have different roles in certain cell types. It is possible that a currently unknown iron exporter may compensate for the loss of ABCB7 in splenic B cells at homeostasis. Additionally, peripheral B cells may uniquely handle iron trafficking or storage compared to pro-B cells, which may prevent these cells from accumulating mitochondrial iron in the absence of ABCB7.

Iron overload is potentially toxic to cells as it can potently induce the formation of ROS, which can induce mitochondrial damage, disrupt the electron transport chain, or cause DNA damage (Lawen and Lane, 2013). Despite the extensive iron accumulation occurring in pro-B cells upon conditional deletion of ABCB7, we did not find evidence of elevated cellular or mitochondrial ROS (Figure 3E and F) and mitochondria were normal (Figure 3D). Two explanations for this are that the cells are effectively negating any excess ROS generated or the accumulating iron is being sequestered and stored in mitochondrial ferritin (Lane et al., 2015). We did observe reduced level of GSH (glutathione; Figure 3H), an abundant antioxidant utilized by cells to protect from ROS (Haenen and Bast, 2014), a possible indication that these cells are utilizing GSH to negate any excess ROS. However, ABCB7 transports an Fe-S-GSH intermediate (Li and Cowan, 2015), which may accumulate in the mitochondria and make GSH unavailable for antioxidant activity. It remains to be seen whether the lack of excess ROS and normal mitochondria is due to unique iron handling in pro-B cells and/or an effective antioxidant pathway protecting the cells from iron-derived ROS.

The Fe-S-GSH intermediates transported by ABCB7 mature in the cytoplasm where they are then used as critical cofactors for numerous enzymes involved in DNA replication and damage repair, including DNA primase, all replicative DNA polymerases, the helicases Dna2, FancJ, and XPD, and the glycosylases Endo III and MutY (Baranovskiy et al., 2018; Cunningham et al., 1989; Fuss et al., 2015; Klinge et al., 2007; Mariotti et al., 2020; Netz et al., 2011; Porello et al., 1998; Puig et al., 2017; Rudolf et al., 2006). Because we observed pH2A.X was highly expressed in EdU+ cells, we hypothesize that DNA damage is being induced during replication due to defects caused by aberrant Fe-S cluster transport in the absence of ABCB7. The reduced amount of EdU+ incorporation in Mb1-cre ABCB7 cKO pro-B cells implies that replication is slower in these cells (Figure 7A–D). HEL-Ig ABCB7-deficient pro-B cells also had increased pH2A.X expression in EdU+ cells and had evidence of slower proliferation (Figure 9C–F), but these B cells were able to reconstitute peripheral B cell populations in the absence of ABCB7, implying that the slowed proliferation did not inhibit development of these cells. It is unclear if the slowed replication is due to slower polymerase, inefficient helicase-mediated DNA unwinding, increased DNA damage, or defective DNA damage repair, which are all possible if fewer Fe-S clusters are available for incorporation into critical enzymes. It is unlikely that any DNA damage occurring is due to excess ROS caused by iron accumulation because of the lack of excess ROS discussed above. DNA damage induced during replication may also explain the decrease in absolute numbers of Fr. B and Fr. C cells in Mb1-cre ABCB7 cKO mice as these cells undergo proliferation before undergoing heavy chain recombination (Hardy et al., 1991). Because DNA damage can block heavy chain recombination (Arya and Bassing, 2017; Bahjat and Guikema, 2017; Fisher et al., 2017), replication-induced DNA damage occurring in early B cell progenitors may also account for the reduction in heavy chain recombination and expression of μHC we observed in ABCB7-deficient pro-B cells (Figure 5).

The modest decrease in pATM, pChk1, and p53 (Figure 7) expression suggests that ABCB7-deficient cells also have a partially defective DNA damage response. The reduced expression of Chk1 and pChk1 (Figure 7F and G) is of particular interest because Chk1-deficient B cells have a block in B cell development at the pro-B cell stage. Chk1-haploinsufficient B cells had elevated DNA damage and underwent cell cycle arrest (Schuler et al., 2017), similar to what we observed in ABCB7-deficient pro-B cells. In addition to decreased Chk1 and pChk1 expression in ABCB7-deficient pro-B cells, we observed decreased CDK2 and Ki-67 expression. CDK2 has non-redundant functions during the S-phase checkpoint that promotes activation of DNA damage response and phosphorylation of Chk1. Elimination of CDK expression delays S/G2 progression after DNA damage. Additionally, knockdown of CDK2 promotes a cell cycle exit program, as marked by reduction in Ki-67 and decreased phosphorylation of Chk1 (Bačević et al., 2017), which is in agreement with our data (Figure 7F and K). These data suggest that ABCB7-deficient pro-B cells encounter DNA damage during replication and either drop out of the cell cycle or are spending extended periods of time at the S-phase checkpoint. Stimulated ABCB7-deficient peripheral B cells also had a decrease in the proportion of cells expressing Ki-67 (Figure 8E), further suggesting a link between ABCB7 activity and proliferation potential as measured by Ki-67 expression. Interestingly, pro-B cells from HEL-Ig Mb1-cre ABCB7 cKO mice had restored Ki-67 expression, cell cycle status, and EdU incorporation, despite evidence of elevated DNA damage in proliferating ABCB7-deficient cells (Figure 9A). This suggests that different stimuli can influence proliferation in ABCB7-deficient cells, which is supported by the variable effect of ABCB7-deficiency on proliferation in different class switch conditions (Figure 8).

Currently, it is not clear why DNA damage is occurring in proliferating pro-B cells in the absence of ABCB7. It is also unclear why the DNA damage response is partially diminished in these cells. As mentioned, Fe-S clusters exported by ABCB7 are critical cofactors in numerous enzymes involved in DNA replication and damage repair. Whether Fe-S cluster incorporation into these enzymes is defective in the absence of ABCB7 remains to be seen. Additionally, it will be interesting to see if the activity of these DNA enzymes is diminished in ABCB7-deficient pro-B cells. Are pro-B cells more sensitive to alterations in the activities of these enzymes compared to peripheral B cells at steady state? And finally, is T cell development or homeostasis affected by the absence of ABCB7 or are pro-B cells uniquely sensitive to the loss of ABCB7? Future work exploring the role of ABCB7 in the development and homeostasis of lymphocytes will provide insight into how these cells regulate Fe-S export, iron homeostasis, proliferation, and DNA damage repair.

Materials and methods

Mice

The Institutional Animal Care and Use Committee at Mayo Clinic approved all animal studies performed in this article. Abcb7fl/Abcb7fl/fl(Clarke et al., 2006). Mb1-cre (Hobeika et al., 2006), MD4 HEL-Ig transgenic (Goodnow et al., 1988; Mason et al., 1992), and CD23-cre (Kwon et al., 2008) mice were all purchased from The Jackson Laboratory. Mb1-cre has Cre knocked into the Cd79a locus, replacing exons 2 and 3 (Hobeika et al., 2006). CD23-cre was generated by insertion of Cre, linked to a truncated human Cd5 gene using an IRES, into exon 2 in a BAC clone containing the Fcer2a (Cd23) locus (Kwon et al., 2008). Abcb7fl and/or Abcb7fl/fl mice were interbred with Mb1-cre or CD23-cre mice to generate Mb1-cre ABCB7 cKO and CD23-cre ABCB7 cKO mice, respectively. HEL-Ig mice were crossbred with WT and Mb1-cre ABCB7 cKO mice to generate HEL-Ig WT and HEL-Ig Mb1-cre ABCB7 cKO mice, respectively. No differences were observed between male and female mice. All mice were housed in a barrier facility and were analyzed between the ages of 4–8 weeks for bone marrow experiments and between 8–12 weeks of age for splenic B cell experiments. For every experiment, age-matched littermate controls consisting of either ABCB7 floxed-only mice (with no Cre expression), Mb1-cre mice (with no floxed alleles), or wild-type (Abcb7 WT) C57BL/6 mice were utilized, and for convenience these mice are referred to as simply ‘WT littermate’ in this article. Genotypes of all mice were confirmed by PCR after use.

Cell lines

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DNA from a RAG2-/- pro-B cell line was used as a negative control in the heavy chain semiquantitative PCR assay. The RAG2-/- pro-B cell line was previously established, maintained, and phenotyped by Dr. Medina (Bertolino et al., 2005; Gwin et al., 2010; Pongubala et al., 2008). These cells were validated as Rag2-deficient using qPCR. A mycoplasma test kit was used to confirm the absence of mycoplasma from the RAG2-/- pro-B cell line (ATCC, Manassas, VA).

Preparation of single-cell suspensions

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Single-cell suspensions of bone marrow were generated as previously described (Amend et al., 2016). Briefly, both femurs and tibias were dissected, cleaned of muscle tissue, and one end of each bone was snipped longitudinally about 2 mm using dissecting scissors to crack the bones open. Bones were placed, cracked side down, into a 500 μL Eppendorf tube with a hole punched in the bottom using an 18G needle and then the smaller tube subsequently placed into a 1 mL Eppendorf tube. Bone marrow was spun out of the bones at 1500 rpm for 1 min and collected in the larger Eppendorf tube. Red blood cells were lysed with 1 mL of ACK Lysing Buffer (#118-156-101; Quality Biological, Gaithersburg, MD), subsequently diluted in 9 mL of PBS (#21-040-CMR; Corning, Corning, NY), and then filtered through an 80 μm Nylon mesh. Cells were centrifuged at 1500 rpm and washed twice with 10 mL of PBS. For preparation of single-cell suspension of splenocytes, spleens were dissected and homogenized between two frosted slides in 5 mL of PBS. After washing twice with 10 mL of PBS, red blood cells were lysed, and suspensions were diluted, filtered, and washed as above.

Flow cytometry

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All antibody dilutions, clones, and sources are provided in the Key resources table. Single-cell suspensions (5 × 106 cells) from bone marrow or spleen were incubated (4°C, 10 min) with 5% mouse/rat serum (1:1) to block Fc receptors. For flow cytometric analysis of surface antigens, cells were incubated (4 °C, 30 min) with antibodies and fixable viability dye (FVD; Tonbo Biosciences, San Diego, CA). For analysis of bone marrow B cell populations, the following surface antibodies were used: anti-mouse B220 (clone RA3-6B2), anti-mouse BP-1 (Ly-51; clone BP-1 or 6C3), anti-mouse CD19 (clone 6D5), anti-mouse CD24 (clone 30-F1 or M1/69), anti-mouse CD43 (clone 1B11), and anti-mouse IgM (clone RMM-1). For analysis of splenic B cell populations, the following antibodies were used: anti-mouse AA4.1 (CD93; clone AA4.1), anti-mouse CD19 (clone 1D3), anti-mouse CD21/35 (clone 7E9), anti-mouse CD23 (clone B3B4), and anti-mouse IgM (clone RMM-1). The following surface antibodies were also used: CD2 (clone RM2-5), anti-human CD5 (CD23-cre reporter; clone L17F12), CD25 (clone PC61.5), anti-mouse CD71 (clone RI7217), anti-mouse CD127 (IL-7Rα; clone A7R34), and anti-mouse IgD (clone 11–26c.2a). The following antibodies were used for analysis of class switch isotypes: goat F(ab’)2 anti-mouse IgG1 (#1072-09), goat F(ab’)2 anti-mouse IgG2a (#1082-09), goat F(ab’)2 anti-mouse IgG2b (#1092-09), goat F(ab’)2 anti-mouse IgG3 (#1102-09), and goat anti-mouse IgA (#1040-09). For analysis of intracellular antigens, surface-stained cells were fixed and permeabilized using the FoxP3/Transcription Factor Staining Buffer Set (Tonbo Biosciences). Cells were incubated (4°C, 30 min) with 1× fixative, washed, and intracellular antibodies were incubated (4°C, 30 min) in 1× permeabilization buffer. The following intracellular antibodies were used: anti-Bcl-xL (#2767S), anti-mouse Bcl2 (#633508), anti-CDK2 (#14174), anti-Chk1 (ab32531), anti-mouse E47/E2A (#552510), anti-mouse EBF1 (ABE1294), anti-FOXO1 (#14262S), anti-mouse HO-1 (#ab69545), anti-mouse IgM (μHC; clone RMM-1; #406506), anti-mouse IKAROS (#89389S), anti-mouse IRF4 (#12-9858-82), anti-mouse Ki-67 (#652404 or #652411), anti-mouse Mcl-1 (#65617S), anti-p53 (#2015S), anti-PARP (#9532S), anti-mouse pATM (#651204), anti-mouse PAX5 (#17-9918-80), anti-mouse pChk1 (#13959S), anti-pH2A.X Ser139 (γH2A.X; #9720S), anti-mouse TdT (#12-5846-82), and anti-mouse VDAC1 (Porin; #55259-1-AP). Isotype control antibodies were included in experiments utilizing intracellular antibodies. All antibodies were purchased from Abcam (Cambridge, UK), BD Biosciences (Franklin Lakes, NJ), BioLegend (San Diego, CA), Cell Signaling Technology (Danvers, MA), eBioscience (Thermo Fisher; Waltham, MA), Millipore Sigma (Burlington, MA), ProteinTech (Rosemont, IL), SouthernBiotech (Birmingham, AL), or Tonbo Biosciences. Unless otherwise noted in figure legends, pro-B cells were defined as B220+ CD19+ CD43+. Hardy fractions were defined as follows: Fr. B (B220+ CD19+ CD43+ sIgM- BP-1-), Fr. C (B220+ CD19+ CD43+ sIgM- CD24lo BP-1+ or B220+CD19+CD43+sIgM-BP-1+, as denoted in figure legends), Fr. C’ (B220+ CD19+ CD43+ sIgM- CD24hi BP-1+), Fr. D (B220+ CD19+ CD43+/lo sIgM-), Fr. E (B220+ CD19+ CD43+/lo sIgM+), and Fr. F (B220hi CD19+ CD43+/lo sIgM+). Peripheral B cells were defined as follows: T1 (CD19+ AA4.1+ CD21/35- IgM+ CD23-), T2 (CD19+ AA4.1+ CD21/35- IgM+ CD23+), T3 (CD19+ AA4.1+ CD21/35+ IgM+), FO (CD19+ AA4.1- CD21/35+ IgM+), and MZ (CD19+ AA4.1- CD21/35hi IgMhi). Data were collected with an Attune NxT flow cytometer (Thermo Fisher), and all experiments were analyzed using FlowJo software (v10.5.3 or v10.8.0). Unless otherwise noted, all analyses utilized doublet exclusion (forward scatter [FSC] height/FSC area), size exclusion (side scatter [SSC] area/FSC area), and dead cell exclusion (FVD+). Quantitative expression data are presented as median fluorescence intensity (MdFI), unless expression is not normally distributed in which case data are presented as the geometric mean of the fluorescence intensity (gMFI) (Cossarizza et al., 2017).

FACS sorting

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Cell sorting for qPCR was performed on a BD FACSMelody Cell Sorter (BD Biosciences). For pro-B cell populations, single-cell bone marrow suspensions were stained with anti-mouse B220 BV510 (1:200), anti-mouse BP-1 PE (1:50), anti-mouse CD19 PE-Cy7 (1:500), anti-mouse CD24 FITC (1:1000; clone 30-F1), anti-mouse CD43 PerCP (1:100), FVD Ghost Violet 450 (1:1000), and anti-mouse IgM PE-CF594 (1:100). Fr. B cells were gated as FVD- B220+ CD19+ CD43+ IgM- BP-1-. Fr. C cells were gated as FVD- B220+ CD19+ CD43+ IgM- CD24+ BP-1+. For splenic B cell populations, single-cell splenocyte suspensions were stained with anti-mouse CD1d (1:100), anti-mouse CD19 eFluor 450 (1:500), anti-mouse CD21/35 PerCP-Cy5.5 (1:100), anti-mouse CD93 PE-Cy7 (1:100), FVD Ghost Violet 510 (1:1000), and anti-mouse IgM PE (1:100). FO B cells were gated as FVD- CD19+ AA4.1- CD21/35+ IgM+. MZ B cells were gated as FVD- CD19+ AA4.1- CD21/35hi IgMhi CD1d+. Cells were sorted at 4°C into PBS and immediately used for RNA extraction.

RNA purification, cDNA synthesis, and quantitative PCR

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B cells were FACS sorted as described above. Sorted cells were lysed using QIAshredder spin-columns (Qiagen, Hilden, Germany) and total RNA was then extracted and purified using a RNeasy Mini Kit (Qiagen), both according to the manufacturer’s instructions. Purified RNA was eluted from the columns using RNase-free water. cDNA was synthesized from purified RNA with random hexamers using a SuperScript IV First-Strand Synthesis System kit (Thermo Fisher) according to the manufacturer’s instructions. After cDNA synthesis, RNA was removed using RNase H, as described in the SuperScript IV protocol. For analysis of Rag1 and Rag2 expression in Fr. B and Fr. C cells, cDNA was subjected to qPCR analysis using TaqMan probes specific for Rag1 and Rag2. For analysis of Abcb7 in FO and MZ B cells, cDNA was subjected to qPCR analysis using a TaqMan probe specific for Abcb7. Expression was normalized to that of an 18S rRNA TaqMan probe, and then normalized to expression in WT Fr. B cells or WT FO B cells. For analysis of VH7183 and VHJ558 GLT expression in pro-B cells, cDNA was subjected to qPCR analysis using SYBR Green and forward and reverse primers specific for a non-coding region of either VH7183 or VHJ558, as previously described (Fuxa et al., 2004). Primers used are listed in Appendix 2. Expression of each GLT was normalized to the expression of the housekeeping gene Hprt and then normalized to expression in WT Fr. B cells. The Hprt primers are listed in Appendix 2. For qPCR assays, every sample was plated in triplicate as a technical replicate. All qPCR assays were performed on a StepOne Real-Time PCR System (Thermo Fisher) and analyzed using the delta-delta Ct (ΔΔCt) method. All primers were ordered from Integrated DNA Technologies (Coralville, IA), and all qPCR probes were ordered from Thermo Fisher.

Pre-B CFU assay

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To enumerate IL-7-dependent pre-B CFU in bone marrow cells, MethoCult M3630 media (STEMCELL Technologies, Vancouver, Canada) was utilized according to the manufacturer’s instructions. Briefly, single-cell suspensions of total bone marrow were prepared as described above and diluted in IMDM media containing 2% FBS to a concentration of 1 × 106 and 2 × 106 cells/mL. Two plating concentrations were used to account for seeding variability, as recommended by the manufacturer. To prepare the final culture concentrations, 400 μL of the cell suspensions were then added to 4 mL of the M3630 media to create a final cell concentration of 1 × 105 and 2 × 105 cells/mL. Samples were vigorously pulsed on a vortex. Bubbles were allowed to float for 5 min before 1.1 mL of the sample was drawn with a 16G needle syringe and distributed to the center of 35 mm dishes, in triplicate for each sample. Each sample dish was placed in a square 100 mm dish (with lid) along with one unlidded 35 mm dish containing water to maintain humidity. Cells were incubated for 8 days at 37°C in a 5% CO2 incubator. After 8 days, pre-B cell colonies were counted as described in the manufacturer’s protocol, and colony numbers were averaged across the triplicate plates. Colony numbers from the 2 × 105 cell concentration are reported in the article.

Analysis of mitochondria, iron accumulation, ROS, GSH, and lipid peroxides

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For flow cytometric analysis of mitochondria, single-cell suspensions (5 × 106 cells) from bone marrow were incubated with 100 nM MitoTracker Green FM (#M7514) and 100 nM TMRM (#T668). Intracellular iron was quantified by incubating bone marrow cells with 5 μM Phen Green SK (#P14313) diacetate. Intracellular and mitochondrial ROS were detected by incubating bone marrow cells with 5 μM CellROX (#C10444) and 5 μM MitoSOX (#M36008) dyes, respectively. To detect the presence of lipid peroxides, bone marrow cells were incubated with 2 μM Bodipy 581/591C-11 (#D3861), which is specifically oxidized by lipid peroxides. GSH levels were quantified by incubating bone marrow cells with 4 μM ThiolTracker Violet (#T10095), which detects GSH. All dyes were purchased from Thermo Fisher. For labeling with each dye, dyes were diluted in PBS and incubated with cells for 30 min in a 37°C 5% CO2 incubator. After incubation, cells were washed with PBS. Fc receptors were blocked with mouse/rat serum, and surface antigens and FVD were stained as described above. Because of fluorescence spillover from these dyes, a limited surface marker panel was utilized in these experiments: anti-mouse B220, anti-mouse CD19, anti-mouse CD43, and anti-mouse IgM. Pro-B cells were defined as B220+ CD19+ CD43+ sIgM-. Data were collected with an Attune NxT flow cytometer (Thermo Fisher).

Annexin V binding

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Single-cell suspensions (5 × 106 cells) were blocked, and surface antigens and FVD were labeled as described above. Cells were then washed with 1× Annexin V binding buffer (BD Biosciences) before being incubated (4°C, 15 min) with Annexin V-FITC conjugate (1:500; BD Biosciences) diluted in 1× binding buffer. After incubation, cells were washed with and resuspended in 1× binding buffer for immediate analysis on an Attune NxT flow cytometer (Thermo Fisher). Cell populations were gated without live/dead exclusion in order to visualize Annexin V+ FVD+ cells as presented in the article.

Overnight pro-B cell culture

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Single-cell suspensions of total bone marrow were prepared as described above. Cells were resuspended in culture media (IMDM, 10% FBS, 1% glutamine, 1% Pen/Strep, and 0.1% 2-mercaptoethanol [2-ME]) and 5 × 106 cells were placed in 6-well plates and incubated overnight for 16 hr at 37°C in a 5% CO2 incubator. The next day, cells were washed with PBS and Annexin V binding was analyzed as described above.

DNA purification from magnetically enriched pro-B cells

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Single-cell suspensions of total bone marrow were prepared as described above. Pro-B cells were then enriched using an EasySep Mouse Streptavidin RapidSpheres Isolation Kit (STEMCELL Technologies) according to the manufacturer’s instructions. The following biotinylated antibodies were used for negative selection of unwanted cells: CD11b (1:100), CD11c (1:100), CD4 (1:100), CD8 (1:500), GR-1 (1:100), IgM (1:100), NK1.1 (1:100), TCRγδ (1:100), and TCRβ (1:100). Note that IgM was included to eliminate IgM+ pre-B, naïve, and recirculating B cells. For a positive control, splenic B cells were harvested from a WT mouse and subjected to a similar RapidSphere negative selection that did not include IgM antibodies. Purity was checked by using flow cytometry after magnetic separation. Genomic DNA was then isolated from enriched pro-B cells and splenic B cells using a DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s instructions. The concentration of DNA was determined using a Nanodrop spectrophotometer (Thermo Fisher) and adjusted so that all samples would have equivalent concentrations.

Semiquantitative PCR analysis of heavy chain recombination

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Analysis of heavy chain recombination was performed as previously described (Angelin-Duclos and Calame, 1998; Li et al., 1993; Pelanda et al., 2002; Schlissel et al., 1991). Briefly, genomic DNA from magnetically enriched WT and Mb1-cre ABCB7 cKO pro-B cells (described above) was adjusted to equivalent concentrations and then underwent threefold serial dilutions. Serially diluted DNA was then subjected to a PCR assay that detects recombination between specified VH gene families and the JH3 gene. This assay utilizes forward primers specific to either VH7183, VH3609, VHVgam3.8, or VHJ558 gene families and a reverse primer specific to the JH3 gene (Angelin-Duclos and Calame, 1998; Li et al., 1993; Pelanda et al., 2002; Schlissel et al., 1991). Three PCR products were expected depending on whether the recombination utilized the JH1, JH2, or JH3 genes; however, the largest product length is underrepresented due to product length. The primers used are listed in Appendix 2. Primers specific to actin were used as a loading control for each serial dilution and are listed in Appendix 2. DNA isolated from splenic B cells was used as a positive control for recombination events. DNA from a Rag2-/- pro-B cell line was utilized as a negative control. PCR was performed using OneTaq DNA polymerase and buffers for GC-rich DNA (#M0480L; New England Biolabs, Ipswich, MA). Each PCR reaction additionally included 200 μM dNTPs, and forward and reverse primers at 0.5 μM (actin) or 1 μM (VH and JH3 genes). For detection of actin, the reactions were incubated as follows: 95°C for 3 min, then 30 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 60 s, and a final incubation at 72°C for 3 min. For detection of VH7183 and VHJ558 recombination, the reactions were incubated as follows: 95°C for 3 min, then 45 cycles of 95°C for 45 s, 63°C for 45 s, and 72°C for 60 s, and a final incubation at 72°C for 15 min. For detection of VH3609 and VHVgam3.8 recombination, the reactions were incubated as follows: 95°C for 3 min, then 45 cycles of 95°C for 45 s, 60°C for 60 s, and 72°C for 60 s, and a final incubation at 72°C for 10 min. PCR products were run on 1.25% agarose gels containing ethidium bromide and photographed using an Omega Lum G gel imager (Gel Company, Ramsey, MN). All PCR primers were ordered from Integrated DNA Technologies.

EdU assay

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EdU incorporation was analyzed using a Click-iT Plus EdU Alexa Fluor 488 Flow Cytometry Assay Kit (Thermo Fisher). Briefly, single-cell suspensions of bone marrow cells were prepared as described above. 1 × 107 cells were resuspended in complete media (RPMI, 10% FBS, 1% glutamine, 1% HEPES, 1% non-essential amino acids, 1% Pen/Strep, and 0.1% 2-ME) and plated in a 6-well plate. EdU was added to each well at a final concentration of 10 μM. A control well without EdU addition was also plated. Cells were incubated at 37°C for 3 hr in a 5% CO2 incubator. After incubation, 3 × 106 cells were blocked (4°C, 10 min) with mouse/rat serum and surface antigens and FVD were labeled (4°C, 30 min) in PBS containing 1% BSA, as recommended by the manufacturer’s instructions. Cells were washed and then incubated (room temperature, 15 min) with EdU kit fixative. Cells were then washed with PBS containing 1% BSA and then permeabilized by incubating (room temperature, 15 min) with 1× EdU kit permeabilization buffer. Click-iT reaction cocktails were then prepared according to the manufacturer’s instruction. 500 μL of reaction cocktail was then added to each sample and incubated at room temperature for 30 min. After the Click-iT reaction, cells were then washed with 1× permeabilization buffer and incubated (4°C, 30 min) with intracellular antibodies specific for pH2A.X in 1× permeabilization buffer. Samples were washed with 1× permeabilization buffer and immediately analyzed on an Attune NxT flow cytometer (Thermo Fisher).

DAPI staining

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For analysis of cell cycle status, single-cell suspensions (5 × 106 cells) from bone marrow were stained with surface antibodies and FVD and were then fixed and permeabilized using FoxP3/Transcription Factor Staining Buffer Set, as described above. Cells were stained with DAPI (1:4000) by incubating (4°C, 30 min) with DAPI in 1× permeabilization buffer. Samples were immediately analyzed on an Attune NxT flow cytometer (Thermo Fisher). The FlowJo cell cycle analysis tool was used to quantify the proportion of cells in each cell phase.

Class switch culture

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Single-cell suspensions of splenocytes were prepared as described above. Splenic B cells were then enriched using an EasySep Mouse Streptavidin RapidSpheres Isolation Kit (STEMCELL Technologies) according to the manufacturer’s instructions. The following biotinylated antibodies were used for negative selection of unwanted cells: CD11b (1:100), CD11c (1:100), CD4 (1:100), CD8 (1:500), GR-1 (1:100), NK1.1 (1:100), TCRγδ (1:100), and TCRβ (1:100). After selection, B cells were labeled with 2.5 μM carboxyfluorescein diacetate succinimidyl diester (CFSE). Cells were cultured at 2.5 × 105/mL in a 24-well plate and stimulated to class switch to different antibody isotypes as described previously (Guikema et al., 2010). In detail, cells were cultured in complete media (RPMI, 10% stem cell-grade FBS, 1% glutamine, 1% HEPES, 1% non-essential amino acids, 1% Pen/Strep, and 0.1% 2-ME). Note that stem cell-grade FBS of the same lot (#10437028; Thermo Fisher) was utilized in each culture experiment as traditional FBS was found to inhibit class switching (a finding that was previously reported [Zaheen and Martin, 2010]). All culture conditions contained LPS (25 μg/mL; Millipore Sigma) and recombinant human BAFF (100 ng/mL; PeproTech, Cranbury, NJ). For their respective wells, the following were added to induce switching to different isotypes: for IgG1 switching, recombinant mouse IL-4 (20 ng/mL; PeproTech) was added; for IgG2a switching, IFNγ (25 ng/mL; PeproTech) was added; for IgG2b switching, TGF-β (2 ng/mL; PeproTech) was added; for IgG3 switching, anti-δ-dextran (3 ng/mL; Fina Biosciences, Rockville, MD) was added; for IgA switching, recombinant mouse IL-4 (20 ng/mL), TGF-β (2 ng/mL), anti-δ-dextran (3 ng/mL), and IL-5 (2 ng/mL; PeproTech) were added. Cells were cultured for 4 days at 37°C in a 5% CO2 incubator. After culture, surface antigens and FVD were labeled for flow cytometry as described above. CFSE dilution and class switching were also analyzed using flow cytometry. Intracellular flow cytometry was utilized to analyze Ki-67 expression. For the assay analyzing cell death over time, aliquots of cells from an IgG1-stimulating culture were harvested each day and analyzed for FVD binding. The FlowJo proliferating modeling tool was used to quantify proliferation index and percent undivided after culture.

Statistical analysis

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Statistical methods used are listed in each figure legend. Each data point of a bar graph represents a single mouse. Unpaired Student’s t-tests were used to compare quantifications of MdFI, gMFI, proportions, expression, relative expression, and CFU colony counts between WT and Mb1-cre ABCB7 cKO or CD23-cre ABCB7 cKO mice, unless otherwise noted in figure legends. For comparison between WT, Mb1-cre ABCB7 cKO, and CD23-cre ABCB7 absolute cell numbers, a one-way ANOVA with Dunnett’s test for multiple comparisons was utilized. A repeated measures two-way ANOVA with Geisser–Greenhouse correction and Holm–Šídák’s multiple comparisons test were used for comparison between WT and CD23-cre ABCB7 cKO cell viability over time. A one-way ANOVA with Tukey’s multiple comparisons test was used for comparison of IgM and IgD expression between WT, Mb1-cre ABCB7 cKO, HEL-Ig WT, and HEL-Ig Mb1-cre ABCB7 cKO pro-B cells. All error bars represent the mean ± SEM. p-Values are indicated on each figure and/or figure legend. Statistical analysis was performed using GraphPad Prism software.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Gene (Mus musculus)Abcb7GenBankGeneID:11306
Strain, strain background (M. musculus)B6.129S4-Abcb7tm1Mdf/J (ABCB7fl/ABCB7fl/fl)Ordered from The Jackson Laboratory, described in PMID:16424901Cat#:006490;MGI: 3628655; RRID:IMSR_JAX:006490ABCB7 floxed mice
Strain, strain background (M. musculus)B6.C(Cg)-Cd79atm1(cre)Reth/EhobJ (Mb1-cre)Ordered from The Jackson Laboratory, described in PMID:16940357Cat#:020505;MGI: 3687451; RRID:IMSR_JAX:020505Bone marrow B cell-specific cre
Strain, strain background (M. musculus)B6.Cg-Tg(Fcer2a-cre)5Mbu/J (CD23-cre)Ordered from The Jackson Laboratory, described in PMID:18538592Cat#:028197;MGI: 3803652; RRID:IMSR_JAX:028197Peripheral B cell-specific cre, uses B cell-specific Cd23 promoter
Strain, strain background (M. musculus)C57BL/6-Tg(IghelMD4)4Ccg/J (HEL-Ig)Ordered from The Jackson Laboratory, described in PMID:7926785Cat#:002595;MGI:2384162; RRID:IMSR_JAX:002595Transgenic mice with fully rearranged BCR
Cell line (M. musculus)RAG2KO Pro-B cellsKay Medina LabRRID:CVCL_B3QDCell line maintained by Medina labSee Materials and methods
AntibodyAnti-B220 BV785 (Rat monoclonal, RA3-6B2)BioLegendCat#:103246; RRID:AB_2563256FC (1:200)
AntibodyAnti-B220 BV510 (Rat monoclonal, RA3-6B2)BioLegendCat#:103248; RRID:AB_2650679FACS (1:200)
AntibodyAnti-Bcl2 PE (Mouse monoclonal, BCL/10C4)BioLegendCat#:633508; RRID:AB_2290367FC (1:100)
AntibodyAnti-Bcl-xL AF488 (Rabbit monoclonal, 54H6)Cell Signaling TechnologyCat#:2767S; RRID:AB_2274763FC (1:100)
AntibodyAnti-BP-1 biotin (Rat monoclonal, 6C3)eBioscienceCat#:12-5891-82; RRID:AB_466015FC (1:50)
AntibodyAnti-BP-1 BV605 (Mus spretus monoclonal, BP-1)BD BiosciencesCat#:745238; RRID:AB_2742824FC (1:50)
AntibodyAnti-BP-1 PE (Rat monoclonal, BP-1)eBioscienceCat#:12-5891-83; RRID:AB_466016FACS, FC (1:50)
AntibodyAnti-CD1d FITC (Rat monoclonal, 1B1)BioLegendCat#:123508; RRID:AB_1236549FACS (1:100)
AntibodyAnti-CD2 FITC (Rat monoclonal, RM2-5)eBioscienceCat#:11-0021-81; RRID:AB_464872FC (1:100)
AntibodyAnti-CD4 biotin (Rat monoclonal, RM4-5)BioLegendCat#:100508; RRID:AB_312711Negative selection (1:100)
AntibodyAnti-human CD5 APC (Mouse monoclonal, L17F12)Tonbo BiosciencesCat#:20-0058; RRID:AB_2621548FC [Cre reporter] (1:200)
AntibodyAnti-CD8α biotin (Rat monoclonal, 53-6.7)BioLegendCat#:100704; RRID:AB_312743Negative selection (1:500)
AntibodyAnti-CD11b biotin (Rat monoclonal, M1/70)BioLegendCat#:101204; RRID:AB_312787Negative selection (1:100)
AntibodyAnti-CD11c biotin (Armenian Hamster monoclonal, N418)BioLegendCat#:117304; RRID:AB_313773Negative selection (1:100)
AntibodyAnti-CD19 BV510 (Rat monoclonal, 1D3)BD BiosciencesCat#:562956; RRID:AB_2737915FC (1:200)
AntibodyAnti-CD19 eFluor 450 (Rat monoclonal, eBio1D3)eBioscienceCat#:48-0193-82; RRID:AB_2734905FACS, FC (1:500)
AntibodyAnti-CD19 PE-Cy7 (Rat monoclonal, 6D5)BioLegendCat#:115520; RRID:AB_313655FACS, FC (1:500)
AntibodyAnti-CD21/35 PerCP-Cy5.5 (Rat monoclonal, 7E9)BioLegendCat#:123416; RRID:AB_1595490FACS (1:100)
AntibodyAnti-CD21/35 APC (Rat monoclonal, 7E9)BioLegendCat#:123412; RRID:AB_2085160FC (1:200)
AntibodyAnti-CD23 APC (Rat monoclonal, B3B4)BioLegendCat#:101614; RRID:AB_2103036FC (1:200)
AntibodyAnti-CD24 APC (Rat monoclonal, 30-F1)BioLegendCat#:138506; RRID:AB_2565651FC (1:1000)
AntibodyAnti-CD24 FITC (Rat monoclonal, M1/69)BioLegendCat#:101806; RRID:AB_312839FACS, FC (1:1000)
AntibodyAnti-CD25 PE (Rat monoclonal, PC61.5)Tonbo BiosciencesCat#:50-0251; RRID:AB_2621757FC (1:200)
AntibodyAnti-CD43 APC (Rat monoclonal, 1B11)BioLegendCat#:121214; RRID:AB_528807FC (1:200)
AntibodyAnti-CD43 PerCP (Rat monoclonal, 1B11)BioLegendCat#:121222; RRID:AB_893333FACS, FC (1:200)
AntibodyAnti-CD71 PE (Rat monoclonal, RI7217)BioLegendCat#:113808; RRID:AB_313569FC (1:1000)
AntibodyAnti-CD93 PE (Rat monoclonal, AA4.1)eBioscienceCat#:12-5892-83; RRID:AB_466019FC (1:200)
AntibodyAnti-CD93 PE-Cy7 (Rat monoclonal, AA4.1)BioLegendCat#:136506; RRID:AB_2044012FACS (1:100)
AntibodyAnti-CD127 PE-Cy7 (Rat monoclonal, A7R34)BioLegendCat#:135014; RRID:AB_1937265FC (1:200)
AntibodyAnti-CDK2 PE (Rabbit monoclonal, 78B2)Cell Signaling TechnologyCat#:14174; RRID:AB_2798413FC (1:50)
AntibodyAnti-Chk1 (Rabbit monoclonal, E250)AbcamCat#:ab32531; RRID:AB_726821FC (1:200)
AntibodyAnti-E47/E2A FITC (Mouse monoclonal, G127-32)BD BiosciencesCat#:552510; RRID:AB_394408FC (1:500)
AntibodyAnti-EBF1 (Rabbit polyclonal)Millipore SigmaCat#:ABE1294; RRID:AB_2893472FC (1:1000)
AntibodyAnti-FOXO1 PE (Rabbit monoclonal, C29H4)Cell Signaling TechnologyCat#:14262S; RRID:AB_2798437FC (1:50)
AntibodyAnti-GR-1 biotin (Rat monoclonal, RB6-8C5)BioLegendCat#:108404; RRID:AB_313369Negative selection (1:100)
AntibodyAnti-HO-1 FITC (Mouse monoclonal, HO-1–2)AbcamCat#:ab69545; RRID:AB_2118659FC (1:50)
AntibodyAnti-IgA PE (Goat polyclonal IgG)SouthernBiotechCat#:1040-09; RRID:AB_2794375FC (1:300)
AntibodyAnti-IgD PE (Rat monoclonal, 11–26c.2a)BioLegendCat#:405706; RRID:AB_315028FC (1:200)
AntibodyAnti-IgG1 PE (Goat polyclonal F(ab')2 IgG)SouthernBiotechCat#:1072-09; RRID:AB_2794434FC (1:1000)
AntibodyAnti-IgG2a PE (Goat polyclonal F(ab')2 IgG)SouthernBiotechCat#:1082-09; RRID:AB_2794502FC (1:300)
AntibodyAnti-IgG2b PE (Goat polyclonal F(ab')2 IgG)SouthernBiotechCat#:1092-09; RRID:AB_2794553FC (1:300)
AntibodyAnti-IgG3 PE (Goat polyclonal F(ab')2 IgG)SouthernBiotechCat#:1102-09; RRID:AB_2784525FC (1:300)
AntibodyAnti-IgM APC-Cy7 (Rat monoclonal, RMM-1)BioLegendCat#:406516; RRID:AB_10660305FC (1:100)
AntibodyAnti-IgM biotin (Rat monoclonal, RMM-1)BioLegendCat#:406504; RRID:AB_315054Negative selection (1:100)
AntibodyAnti-IgM BV510 (Rat monoclonal, RMM-1)BioLegendCat#:406531; RRID:AB_2650758FC (1:100)
AntibodyAnti-IgM FITC (Rat monoclonal, RMM-1)BioLegendCat#:406506; RRID:AB_315056FC (1:100)
AntibodyAnti-IgM PE (Rat monoclonal, RMM-1)BioLegendCat#:406508; RRID:AB_315058FACS (1:100)
AntibodyAnti-IgM PE-CF594 (Rat monoclonal, R6-60.2)BD BiosciencesCat#:562565; RRID:AB_2737658FACS (1:100)
AntibodyAnti-IKAROS AF488 (Rabbit monoclonal, D6N9Y)Cell Signaling TechnologyCat#:89389S; RRID:AB_2800139FC (1:100)
AntibodyAnti-IRF4 PE (Rat monoclonal, 3E4)eBioscienceCat#:12-9858-82; RRID:AB_10852721FC (1:100)
AntibodyAnti-Ki-67 BV421 (Rat monoclonal, 16A8)BioLegendCat#:652411; RRID:AB_2562663FC (1:200)
AntibodyAnti-Ki-67 PE (Rat monoclonal, 16A8)BioLegendCat#:652404; RRID:AB_2561525FC (1:200)
AntibodyAnti-Mcl-1 PE (Rabbit monoclonal, D2W9E)Cell Signaling TechnologyCat#:65617S; RRID:AB_2799688FC (1:50)
AntibodyMouse IgG1 kappa Isotype FITC (Mouse monoclonal, P3.6.2.8.1)eBioscienceCat#:11-4714-81; RRID:AB_470021FC (concentration matched antibodies of interest)
AntibodyMouse IgG1 kappa Isotype PE (Mouse monoclonal, MOPC-21)BioLegendCat#:400111; RRID:AB_2847829FC (concentration matched antibodies of interest)
AntibodyMouse IgG2b kappa Isotype FITC (Mouse monoclonal, 27-35)BD BiosciencesCat#:555742; RRID:AB_396085FC (concentration matched antibodies of interest)
AntibodyAnti-p53 AF488 (Mouse monoclonal, 1C12)Cell Signaling TechnologyCat#:2015S; RRID:AB_2206297FC (1:50)
AntibodyAnti-PARP (Rabbit monoclonal, 46D11)Cell Signaling TechnologyCat#:9532S; RRID:AB_659884FC (1:100)
AntibodyAnti-pATM (Ser1981) PE (Mouse monoclonal, 10H11.E12)BioLegendCat#:651204; RRID:AB_2562655FC (1:500)
AntibodyAnti-PAX5 APC (Rat monoclonal, 1H9)eBioscienceCat#:17-9918-80; RRID:AB_10734230FC (1:200)
AntibodyAnti-pChk1 (Ser317) PE (Rabbit monoclonal, D12H3)Cell Signaling TechnologyCat#:13959; RRID:AB_2893473FC (1:50)
AntibodyAnti-pH2A.X (Ser139) AF647 (Rabbit monoclonal, 20E3)Cell Signaling TechnologyCat#:9720S; RRID:AB_10692910FC (1:100)
AntibodyRabbit IgG Isotype Control AF488 (Rabbit monoclonal)Cell Signaling TechnologyCat#:4340; RRID:AB_561545FC (concentration matched antibodies of interest)
AntibodyRabbit IgG Isotype Control AF647 (Rabbit monoclonal)Cell Signaling TechnologyCat#:3452; RRID:AB_10695811FC (concentration matched antibodies of interest)
AntibodyRabbit IgG Isotype Control PE (Rabbit monoclonal, DA1E)Cell Signaling TechnologyCat#:5742; RRID:AB_10694219FC (concentration matched antibodies of interest)
AntibodyRabbit IgG Monoclonal Isotype Control (Rabbit monoclonal, EPR25A)AbcamCat#:ab172730; RRID:AB_2687931FC (concentration matched antibodies of interest)
AntibodyRabbit IgG Polyclonal Isotype Control (Rabbit monoclonal)AbcamCat#:ab171870; RRID:AB_2687657FC (concentration matched antibodies of interest)
AntibodyRat IgG2a kappa Isotype APC (Rat monoclonal, RTK2758)BioLegendCat#:400512; RRID:AB_2814702FC (concentration matched antibodies of interest)
AntibodyRat IgG2a kappa Isotype PE (Rat monoclonal, RTK2758)BioLegendCat#:400508; RRID:AB_326530FC (concentration matched antibodies of interest)
AntibodyRat IgG2b kappa Isotype PE (Rat monoclonal, eB149/10H5)eBioscienceCat#:12-4031-82; RRID:AB_470042FC (concentration matched antibodies of interest)
AntibodyAnti-NK1.1 biotin (Mouse monoclonal, PK136)BioLegendCat#:108704; RRID:AB_313391Negative selection (1:100)
AntibodyAnti-Ter119 biotin (Rat monoclonal, TER-119)BioLegendCat#:116204; RRID:AB_313705Negative selection (1:100)
AntibodyAnti-TCRβ biotin (Armenian Hamster monoclonal, H57-597)BioLegendCat#:109204; RRID:AB_313427Negative selection (1:100)
AntibodyAnti-TCRγδ biotin (Armenian Hamster monoclonal, eBioGL3)eBioscienceCat#:13-5711-82; RRID:AB_466668Negative selection (1:100)
AntibodyAnti-TdT PE (Mouse monoclonal, 19–3)eBioscienceCat#:12-5846-82; RRID:AB_1963620FC (1:200)
AntibodyAnti-VDAC1 (Rabbit polyclonal)ProteinTechCat#:55259-1-AP; RRID:AB_10837225FC (1:100)
Sequence-based reagentqPCR: ABCB7 probeThermo FisherAssayId:Mm01235269_m1FAM-MGB
Sequence-based reagentqPCR: Eukaryotic 18S rRNA Endogenous ControlThermo FisherCat#:4352930FAM-MGB
Sequence-based reagentqPCR: Rag1 probeThermo FisherAssayId:Mm01270936_m1FAM-MGB
Sequence-based reagentqPCR: Rag2 probeThermo FisherAssayId:Mm00501300_m1FAM-MGB
Peptide, recombinant proteinAnnexin V-FITC conjugateBD BiosciencesCat#:556420; AB_2665412FC (1:500)
Peptide, recombinant proteinStreptavidin BV510BioLegendCat#:405234FC (1:200)
Peptide, recombinant proteinRecombinant human BAFFPeproTechCat#:310-13Class switch cultures
Peptide, recombinant proteinRecombinant murine IFN-γPeproTechCat#:315-05Class switch cultures
Peptide, recombinant proteinRecombinant murine IL-4PeproTechCat#:214-14Class switch cultures
Peptide, recombinant proteinRecombinant murine IL-5PeproTechCat#:215-15Class switch cultures
Peptide, recombinant proteinAnti-δ dextran (mouse)Fina BiosolutionsCat#:FINABIO0001Class switch cultures
Peptide, recombinant proteinRecombinant human TGF-β1PeproTechCat#:100-21Class switch cultures
Commercial assay or kitClick-iT Plus EdU AF488 Flow Cytometry Assay KitThermo FisherCat#:C10632EdU Assay
Commercial assay or kitDNeasy Blood and Tissue KitQiagenCat#:69504DNA purification
Commercial assay or kitEasySep Mouse Streptavidin RapidSpheres Isolation KitSTEMCELL TechnologiesCat#:19860ANegative selection
Commercial assay or kitMethoCult M3630 pre-B CFU kitSTEMCELL TechnologiesCat#:03630Pre-B CFU assay
Commercial assay or kitOneTaq DNA polymeraseNew England BiolabsCat#:M0480LSemiquantitative PCR
Commercial assay or kitRNeasy Mini KitQiagenCat#:74104RNA isolation
Commercial assay or kitSuperScript IV First-Strand Synthesis SystemThermo FisherCat#:18091050cDNA synthesis
Chemical compound, drugCFSESigmaCat#:21888Class switch cultures (2.5 μM)
Chemical compound, drugFBS (qualified)Thermo FisherCat#:10437-028Class switch cultures
Chemical compound, drugDAPIBioLegendCat#:422801FC, cell cycle analysis (1:4000)
Chemical compound, drugLPSSigmaCat#:L-2630Class switch cultures
Software, algorithmIllustrator CC 2019AdobeRRID:SCR_010279Figure and image panel preparation
Software, algorithmFlowJo v10.8BD BiosciencesRRID:SCR_008520FC analysis
Software, algorithmGraphPad Prism v9GraphPadRRID:SCR_002798Graphs and statistics
OtherBODIPY 581/591C11C11Thermo FisherCat#:D3861FC (2 μM)
OtherCellROX GreenThermo FisherCat#:C10444FC (5 μM)
OtherGhost Dye Violet 450 (viability dye)Tonbo BiosciencesCat#:13-0863FACS, FC (1:1000)
OtherGhost Dye Violet 510 (viability dye)Tonbo BiosciencesCat#:13-0870FACS, FC (1:1000)
OtherGhost Dye Red 780 (viability dye)Tonbo BiosciencesCat#:13-0865FC (1:1000)
OtherMitoSOX RedThermo FisherCat#:M36008FC (5 μM)
OtherMitoTracker Green FMThermo FisherCat#:M7514FC (100 nM)
OtherPhen Green SK, diacetateThermo FisherCat#:P14313FC (5 μM)
OtherTetramethylrhodamine methyl ester perchlorate (TMRM)SigmaCat#:T5428FC (100 nM)
OtherThiolTracker VioletThermo FisherCat#:T10095FC (4 μM)

Appendix 2

PCR primers
Semiquantitative PCR primers
DesignationSequence
JH3 primer5′- GTCTAGATTCTCACAAGAGTCCGATAGACCCTGG
VH7183 primer5′-GCAGCTGGTGGAGTCTGG
VH3609CDR2 primer5′-CAGGGCTGTGTTATA
VHVGAM3.8CDR2 primer5′-CAAACCGTCCCTTGAA
VHJ558 primer5′-CAGGTCCAACTGCAGCAG
Actin_F primer5′-GGTGTCATGGTAGGTATGGGT
Actin_R primer5′-CGCACAATCTCACGTTCAG
qPCR primers
DesignationSequence
VH7183GLT_F primer5′-CGGTACCAAGAASAMCCTGTWCCTGCAAATGASC
VH7183GLT_R primer5′-GTCTCTCCGCGCCCCCTGCTGGTCC
VHJ558GLT_F primer5′-ACCATGGGATGGAGATGGATCTTTC
VHJ558GLT_F primer5′-CTCAGGATGTGGTTACAACACTGTG
qPCR: HPRT_F primer5′-AGGTTGCAAGCTTGCTGGT
HPRT_R primer5′-TGAAGTACTCATTATAGTCAAGGGCA

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. There are no large datasets included in this manuscript. Source data files have been provided for Figure 5.

References

    1. Cossarizza A
    2. Chang HD
    3. Radbruch A
    4. Akdis M
    5. Andrä I
    6. Annunziato F
    7. Bacher P
    8. Barnaba V
    9. Battistini L
    10. Bauer WM
    11. Baumgart S
    12. Becher B
    13. Beisker W
    14. Berek C
    15. Blanco A
    16. Borsellino G
    17. Boulais PE
    18. Brinkman RR
    19. Büscher M
    20. Busch DH
    21. Bushnell TP
    22. Cao X
    23. Cavani A
    24. Chattopadhyay PK
    25. Cheng Q
    26. Chow S
    27. Clerici M
    28. Cooke A
    29. Cosma A
    30. Cosmi L
    31. Cumano A
    32. Dang VD
    33. Davies D
    34. De Biasi S
    35. Del Zotto G
    36. Della Bella S
    37. Dellabona P
    38. Deniz G
    39. Dessing M
    40. Diefenbach A
    41. Di Santo J
    42. Dieli F
    43. Dolf A
    44. Donnenberg VS
    45. Dörner T
    46. Ehrhardt GRA
    47. Endl E
    48. Engel P
    49. Engelhardt B
    50. Esser C
    51. Everts B
    52. Dreher A
    53. Falk CS
    54. Fehniger TA
    55. Filby A
    56. Fillatreau S
    57. Follo M
    58. Förster I
    59. Foster J
    60. Foulds GA
    61. Frenette PS
    62. Galbraith D
    63. Garbi N
    64. García-Godoy MD
    65. Geginat J
    66. Ghoreschi K
    67. Gibellini L
    68. Goettlinger C
    69. Goodyear CS
    70. Gori A
    71. Grogan J
    72. Gross M
    73. Grützkau A
    74. Grummitt D
    75. Hahn J
    76. Hammer Q
    77. Hauser AE
    78. Haviland DL
    79. Hedley D
    80. Herrera G
    81. Herrmann M
    82. Hiepe F
    83. Holland T
    84. Hombrink P
    85. Houston JP
    86. Hoyer BF
    87. Huang B
    88. Hunter CA
    89. Iannone A
    90. Jäck HM
    91. Jávega B
    92. Jonjic S
    93. Juelke K
    94. Jung S
    95. Kaiser T
    96. Kalina T
    97. Keller B
    98. Khan S
    99. Kienhöfer D
    100. Kroneis T
    101. Kunkel D
    102. Kurts C
    103. Kvistborg P
    104. Lannigan J
    105. Lantz O
    106. Larbi A
    107. LeibundGut-Landmann S
    108. Leipold MD
    109. Levings MK
    110. Litwin V
    111. Liu Y
    112. Lohoff M
    113. Lombardi G
    114. Lopez L
    115. Lovett-Racke A
    116. Lubberts E
    117. Ludewig B
    118. Lugli E
    119. Maecker HT
    120. Martrus G
    121. Matarese G
    122. Maueröder C
    123. McGrath M
    124. McInnes I
    125. Mei HE
    126. Melchers F
    127. Melzer S
    128. Mielenz D
    129. Mills K
    130. Mirrer D
    131. Mjösberg J
    132. Moore J
    133. Moran B
    134. Moretta A
    135. Moretta L
    136. Mosmann TR
    137. Müller S
    138. Müller W
    139. Münz C
    140. Multhoff G
    141. Munoz LE
    142. Murphy KM
    143. Nakayama T
    144. Nasi M
    145. Neudörfl C
    146. Nolan J
    147. Nourshargh S
    148. O’Connor J
    149. Ouyang W
    150. Oxenius A
    151. Palankar R
    152. Panse I
    153. Peterson P
    154. Peth C
    155. Petriz J
    156. Philips D
    157. Pickl W
    158. Piconese S
    159. Pinti M
    160. Pockley AG
    161. Podolska MJ
    162. Pucillo C
    163. Quataert SA
    164. Radstake T
    165. Rajwa B
    166. Rebhahn JA
    167. Recktenwald D
    168. Remmerswaal EBM
    169. Rezvani K
    170. Rico LG
    171. Robinson JP
    172. Romagnani C
    173. Rubartelli A
    174. Ruckert B
    175. Ruland J
    176. Sakaguchi S
    177. Sala-de-Oyanguren F
    178. Samstag Y
    179. Sanderson S
    180. Sawitzki B
    181. Scheffold A
    182. Schiemann M
    183. Schildberg F
    184. Schimisky E
    185. Schmid SA
    186. Schmitt S
    187. Schober K
    188. Schüler T
    189. Schulz AR
    190. Schumacher T
    191. Scotta C
    192. Shankey TV
    193. Shemer A
    194. Simon AK
    195. Spidlen J
    196. Stall AM
    197. Stark R
    198. Stehle C
    199. Stein M
    200. Steinmetz T
    201. Stockinger H
    202. Takahama Y
    203. Tarnok A
    204. Tian Z
    205. Toldi G
    206. Tornack J
    207. Traggiai E
    208. Trotter J
    209. Ulrich H
    210. van der Braber M
    211. van Lier RAW
    212. Veldhoen M
    213. Vento-Asturias S
    214. Vieira P
    215. Voehringer D
    216. Volk HD
    217. von Volkmann K
    218. Waisman A
    219. Walker R
    220. Ward MD
    221. Warnatz K
    222. Warth S
    223. Watson JV
    224. Watzl C
    225. Wegener L
    226. Wiedemann A
    227. Wienands J
    228. Willimsky G
    229. Wing J
    230. Wurst P
    231. Yu L
    232. Yue A
    233. Zhang Q
    234. Zhao Y
    235. Ziegler S
    236. Zimmermann J
    (2017) Guidelines for the use of flow cytometry and cell sorting in immunological studies
    European Journal of Immunology 47:1584–1797.
    https://doi.org/10.1002/eji.201646632
    1. Ohnishi T
    (1998) Iron–sulfur clusters/semiquinones in Complex I
    Biochimica et Biophysica Acta (BBA) - Bioenergetics 1364:186–206.
    https://doi.org/10.1016/S0005-2728(98)00027-9

Decision letter

  1. Gail Bishop
    Reviewing Editor; University of Iowa
  2. Betty Diamond
    Senior Editor; The Feinstein Institute for Medical Research, United States
  3. John Colgan
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This work focuses upon an important feature of biology, the interaction between basic cellular processes and cell differentiation. The study addresses how the ABCB7 transporter of iron impacts the differentiation of B lymphocytes.

Decision letter after peer review:

Thank you for submitting your article "ABCB7 is required for B cell development but not peripheral B cell homeostasis" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Betty Diamond as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: John Colgan (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Overall, the paper would be improved by greater information about the mechanisms by which ABCB7 impacts the processes examined.

Essential revisions:

1) Address question of how ABCB7 status impacts Ig H chain rearrangement, as some of the data presented support the role of ABCB7 in this process, while other data do not. The link between ABCB7 function and DNA damage/repair processes is not sufficiently strong. Additional experimentation, as suggested, could provide valuable insight.

2) Provide additional experimental support for the conclusion that ABCB7 is of lower importance in mature B cells compared to developing B cells. This is implied by data in CD23-Cre system, but not firmly established. Reviewers make several comments and suggestions that should be considered.

3) The conclusion that the major impact of ABCB7 occurs on the population C to C' developmental transition is not sufficiently convincing, based upon the results shown. Further clarification in data presentation and discussion would be very helpful to address this area of confusion.

4) There are quite a few technical issues raised by the reviewers. These need to be addressed. In some cases, this may just require clearer description and explanation; in others, additional experimentation will be needed.

Reviewer #1:

The manuscript by Lehrke et al., investigates the importance of the mitochondrial iron transporter ABCB7 for B cell development and mature B cell homeostasis. The principal methods used for this study are conditional deletion of the gene encoding ABCB7 in developing and mature B cells coupled to fairly sophisticated analysis of the effects of this using flow cytometry. The authors report that deletion of the gene for ABCB7 in early B cell progenitors results in a dramatic block in B cell development at a stage where antibody heavy chain gene rearrangement and cell proliferation must occur in order for B cell development to progress. This block occurs despite normal expression of transcription factors that are required for early B cell development. However, antibody heavy chain gene rearrangement is strongly impaired in the absence of ABCB7, suggesting that iron has a crucial role in this process, which requires DNA recombination and repair. Consistent with these results, transgenic expression of a rearranged heavy chain gene at least partially restores B cell development from ABCB7-deficient B cell progenitors. This result argues that an inability to successfully rearrange the antibody heavy chain gene and generate heavy chain protein is a main cause of the block in B cell development resulting from loss of ABCB7 expression. Some evidence is also provided to support the conclusion that DNA replication and thus cell proliferation is compromised in the absence of ABCB7 due to an inability to repair replication-induced DNA damage. In contrast to the dramatic effects on B cell progenitor development, deletion of the gene for ABCB7 in mature B cells has little effect, suggesting that ABCB7 is not essential for B cell homeostasis.

This manuscript is well organized and well written. The quality of the data shown is excellent, and most of the conclusions made are strong supported by the results presented. These are considered as strengths of the manuscript. Another strength is the characterization of how the loss of ABCB7 affects key events during B cell development; this thorough and methodical and reveals some important insights regarding the processes affected. A weakness of the manuscript is that the effects of ABCB7 deletion on mature B cell homeostasis is less well characterized; a few more experiments could extend this data in meaningful ways. Another weakness is that the effects on antibody heavy chain gene rearrangement and DNA replication were not teased apart.

The work presented by Lehrke et al., has impact on our understanding of the role of iron availability for progression of B cell development. It would appear that the appropriate handling of iron in B cell progenitors is critical for it to potentially serve as a cofactor for proteins that mediate antibody gene rearrangement and DNA replication and repair.

A key question that arises from this study is whether DNA replication in addition to immunoglobulin heavy chain gene rearrangement are affected by the absence of ABCB7. The authors provide evidence that both processes are affected; yet if DNA replication was strongly impaired, why would transgenic expression of a rearranged heavy chain gene largely suppress the effects of ABCB deficiency? Part of the answer could be that the lesion in DNA replication remains and may slow cell proliferation without halting development. To address this, the authors could look at EdU incorporation by pro-B cells and pre-B cells in WT and ABCB cKO mice that both carry the HEL-Ig transgene. If lesions in DNA replication persist, it would further support conclusion that DNA damage during replication is in part responsible for the phenotype.

The authors used CD23-cre to delete the gene encoding ABCB7 in mature B cells and didn't see any strong phenotype, suggesting ABCB7 is less important in these cells relative to developing B cells. Given a largely negative result was obtained, it would seem important to use PCR or Western blot analysis to confirm that the gene encoding ABCB7 is efficiently deleted in mature B cells by CD23-cre. Going further, if the authors wish to solidify the conclusion that ABCB7 is less important in mature B cells, they could stimulate WT and cKO cells in culture with LPS without or with the appropriate cytokines and measure proliferation and class-switching. Negative results here would support the idea that ABCB7 is largely dispensable in mature B cells; defects would support the conclusion that ABCB7 is required for DNA replication/proliferation and/or class switching by mature B cells.

Reviewer #2:

The report by Lehrke et al., describes a phenotypic analysis of B-lymphocyte development in mice with conditional deletion of the ABCB7 gene. Deletion in early B-cell progenitors using a mb1-cre driver result in a dramatic block in B-cell development. In contrast, deletion of the gene in mature cells, using a CD23-cre mouse strain, do not appear to cause observe any dramatic effects on either peripheral cell numbers or iron content in the cells. The developmental block induced by deletion of the ABCB7 gene in early progenitors could be overcome by expression of a BCR transgene, indicating a recombination deficiency as underlying cause of the developmental disruption. In consistency with this, the progenitor B-cells displayed an increased level of pH2gX. However, this was most prominent in proliferating cells that normally do not undergo VDJ recombination. Therefore, the authors suggest that the developmental block is a result of increased general DNA damage.

While the experimental designs in many ways are elegant, there are certain aspect of the analysis that complicates the interpretation of the data. Despite that the authors claim that the major block in differentiation occurs in the fraction C to C', a major part of the downstream analysis is made on a combined C/C' population. Hence, it is difficult to determine if the phenotypes observed is a consequence of a shift in populations or effects directly related to the function of ABCB7. It is also hard to understand how the expression of a functional B-cell receptor should be sufficient to overcome a general increase in DNA damage. Neither do the authors provide any direct link between DNA repair and ABCB7 function.

Hence, while the report clearly shows that ABCB7 is essential for normal early B-cell development, the mechanism by which mitochondrial iron-glutathione transport is linked to B-cell differentiation remains largely elusive.

1: The authors might want to investigate the deletion efficiencies in the more mature cell populations. As deletion of an essential gene cause a dramatic selection pressure this might result in that mature cells represent progenitors that has retained one or two functional alleles. This can complicate the interpretation of the data. Could this possibly be a reason for the apparent heterogeneity in the cKo cells in Figure 2J and 3A? As no phenotype is observed in Fig3S1, the interpretation of the data in Figure 3S1 would also be easier upon verification of functional ABCB7 deletion.

2: The authors could consider to be more stringent with their interpretations of their data. For instance, the reduced fraction B numbers in mice carrying a CD23 driver is considered as "largely unaffected" despite a p-value of 0.006. In contrast, the authors state " There was a trending, but not significant, decrease in HO-1 expression in Mb1-cre ABCB7 218 cKO pro-B cells (Figure 3B), confirming the previous findings that ABCB7 affects heme biosynthesis". indicating that a trend confirms a finding.

3: Much of the data analysis is obscured by the use of the combined C/C´ population. The authors claim that the C to C´ transition is targeted, hence in order to generate conclusive data, these populations should be analyzed separately. The data in figure 4S1B suffer from the same problem as this, as far as I can understand, is done on unsorted BM. In order to be informative sorted fraction B cells should have been seeded. The Y axis would benefit from inclusion of the number of seeded cells to be more informative.

4: I can be discussed if the term Wt can be used for a mix of different genotypes as done on this paper according to the MandM. This might explain the rather strange finding that the CD23 driver cause a significant reduction in fraction B cell numbers. This mixture of genotypes also generates rather strange statements such as the data are generated from 11 independent experiments (Figure legend 1C) despite that only 8 mice from each Ko is analyzed. It is also unclear what the data in the contour plots indicate, I guess one "representative" experiment. It is more stringent to use the average values with a std.

Reviewer #3:

In this manuscript by Lehrke et al., authors set out to describe the role of a poorly characterized cassette protein that regulates iron transport from mitochondria to cytosol. The lack of in depth scientific knowledge on how this molecule works makes it challenging for authors to develop their hypothesis. Nevertheless, authors elegantly show the developmental stage specific role of ABCB7 molecule in B cells. For the most part, experiments were designed and executed properly and controls are adequate. Authors show both positive and negative observations openly and their conclusion makes sense. However, the paper fails to explain how a redundant molecule that does not induce any pathological effects once knocked out (no apoptosis, no ferroptosis, no mitochondrial dysfunction, no ROS accumulation, no iron starvation) still manages to somehow affect a handful of genes only in a select subset of B cell precursors and create the demonstrated effects. It is not even clear whether the activities or biosynthesis of these genes/proteins that are differentially affected between knock out and wt are even influenced by iron homeostasis. Unbiased screening assays would have strengthened the manuscript by pointing out potential novel mechanisms or pathways.

Specific points:

General: Authors continuously use the word "trending" for statistically nonsignificant changes between groups. This is very confusing. If something is not significant, I do not see the point of highlighting that.

Figure 1: According to figure legend, number of repeats and number of mice used for each repeat is abnormally high. (eleven experiments with 8-17 mice per condition.) This makes hundreds of mice used. I do not see the rationale behind this many repeats considering that only handful of data points are actually shown in the figure and the findings are clear.

Figure 1 B: Showing absolute numbers is an ideal approach to support a hypothesis however, this strategy is impossible for bone marrow since unlike spleen which is a capsulated organ that can be meshed and counted as a whole bone marrow is not. So, flushing yields in bone marrow will depend on the amount of cutting on each end of the bone and the ability to capture the marrow without loss through needle flushing. While the data shown by authors is pretty significant and convincing, authors should also discuss that this method has limitations. Alternatively, authors can try to normalize by graphing each population as percentage of all viable isolated cells in their suspension.

Figure 1A-B: Authors build their manuscript on the observation of decreased Pro to Pre B (Fr C to C') cell in bone marrows of cKO mice. The contour plots on Figure 1A shows a slight change in percentages of Fraction C and C' however authors fail to highlight that point in the absolute cell number graph as both Fr. C and Fr. C' goes down in cell numbers. A blockage at a developmental stage is expected to increase the population before the blockage in which case this should be Fr.C. If authors see this as the selling point of the manuscript then they should show it more clearly. I suggest getting ratio of Fr. C over Fr. C' would help overcome the problems related to general hypocellularity in the bone marrow of cKO animals.

Figure 1 C: Gating of splenic B cell fractions should involve CD93 in order to discriminate the transitional cell pool which then can be further divided into T1-2-3 subpopulations using IgM and CD23. The gating strategy authors used is unconventional. If possible, it would be nice to see CD93 staining as well. Furthermore, using CD23 cre mouse will not be able to reveal the effects of ABCB7 KO in marginal zone and T1 cells which express little or no CD23. That limitation needs to be discussed in the text.

Figure 2 demonstrates convincing evidence on the effect of conditional KO in regulation of a select group of critical transcriptional mediators of pro to pre-B cell transition. While not necessary, an unbiased RNA seq experiment would have provided a far better portrait of the alterations in transcriptome level than qPCR experiments focusing on a handful of molecules.

Figure 3: The use of mitotracker green for general mitochondrial mass evaluation is not ideal. It has been shown multiple times that various factors including pH and mitochondrial dysfunction can affect the fluorescence intensity of mitotracker green. Authors should use fluorescent antibodies against specific mitochondrial markers such as VDAC-1, TOM-20 and COX-IV to comment on mitochondrial mass. Furthermore, TMRM staining alone does not give any idea about mitochondrial health. Changes in mitochondrial number between groups can easily cause shifts in TMRM intensity. Therefore, it needs to be normalized to exclude mitochondrial number in order to be used as a parameter that can detect mitochondrial performance. To do so, each sample needs to be divided into three groups. Groups need to be treated with either FCCP (basal staining indicator when mitochondria are depolarized) or oligomycin (maximum staining indicator when mitochondria are hyperpolarized) or nothing (unaltered current level) and TMRM values should be measured for each. Then using the formula 100x [ (MFI (untreated)- MFI(FCCP) )/ (MFI oligomycin)-MFI (FCCP)] percentage of maximum mitochondrial membrane potential used by each group can be found and this values are independent from the differences in mitochondrial numbers. I believe this may change the interpretation of results.

Figure 3: Authors should show under microscopy that Phen Green SK colocalizes with TMRM (or any other mitochondrial stain) in order to confirm the staining differences in Figure 1A are located to changes in mitochondrial levels. Occasionally, these dyes have nonspecific binding issues. This experiment will rule out that possibility.

Figure 3F: Mitochondrial ROS is not detected is a misleading claim (Page 13 lane268) I agree that the MitoSOX levels are identical between cKO and the control however, flow cytometry plots are relative for these dyes. Any mitochondria healthy or unhealthy produces some level of ROS and this is even thought to act as a part of normal physiology if the levels are within limits. So authors should amend this part.

Figure 4: It is a good idea to culture cells and measure Annexin levels which would rule out the possibility that apoptotic cells get cleared fast in bone marrow before they are detected. However, I think Annexin measurement at 16 h is not enough to make a statement. Authors should monitor viability using Live-Dead reagents at early and late time points (such as 3-6-9-12-16-24-48h or something like that) and graph changes. Additionally, commercially available apoptosis, necrosis kits that can detect early and late phases of apoptosis are available and can be beneficial to strengthen the statement.

Page 18, lane 377 onwards: It was not very clear how authors introduced the transgene to the mice for the reader. I figured out by looking at the methods section that this is actually crossbreeding the mice with MD4. However, it sounds from the text as if it was done through transfection. Authors should clearly state that they did cross breeding in this section to avoid any confusion. Also, the use of MD4 mice dates back to 1980s, I think the 1994 paper authors cited is not the original one. It should be a Goodnow paper as far as I remember, please double check.

Figure 7: Similar to Figure 2, authors hand pick a few markers to prove their point. While the selection is elegant and relevant, an unbiased approach would have been far more convincing. Furthermore, the list of markers tested are hard to follow and I did not understand why or how they get affected with slight change of iron levels? Are these molecules regulated by iron levels? How does iron relate to these changes? There is a gap in the story in here.

https://doi.org/10.7554/eLife.69621.sa1

Author response

Essential revisions:

1) Address question of how ABCB7 status impacts Ig H chain rearrangement, as some of the data presented support the role of ABCB7 in this process, while other data do not. The link between ABCB7 function and DNA damage/repair processes is not sufficiently strong. Additional experimentation, as suggested, could provide valuable insight.

We have performed additional experiments as requested by the reviewers. Please see our specific answers in the point-by-point response below. In brief, ABCB7-deficient pro-B cells have decreased heavy chain rearrangement, μHC expression, decreased proliferation, and increased DNA damage in proliferating cells. B cell development is rescued in Mb1-cre ABCB7 cKO mice when interbred with the MD4 HEL-Ig transgene. We found that proliferation was similar in B220+CD19+CD43+CD127+ pro-B cells from Hel-Ig transgenic and HEL-Ig transgenic Mb1-cre ABCB7 cKO mice, although increased DNA damage persisted in the absence of ABCB7 (new Figure 9). Thus, the Hel-Ig transgene restores both Ig expression and proliferation. We believe this may be due to the differences in signals received by B220+CD19+CD43+ pro-B cells in these strains of mice. While normally B220+CD19+CD43+ pro-B cells receive signals through the pre-BCR, in HEL-Ig transgenic mice B220+CD19+CD43+ pro-B cells express high levels of IgM and IgD on the cell surface. Analysis of proliferation of ABCB7-deficient splenic B cells under class switching conditions demonstrated that the severity of the defect depended upon the stimuli utilized (new Figure 8).

As to the cause for increased DNA damage in the absence of ABCB7, there is a direct link between ABCB7 and DNA repair, as the iron-sulfur clusters exported from the mitochondria by ABCB7 are cofactors in numerous DNA repair enzymes including DNA primase, all replicative DNA polymerases, the helicases Dna2, FancJ, and XPD, and the glycosylases Endo III and MutY 1–9. Therefore, a disruption in iron-sulfur cluster levels may impair the function of one or numerous DNA repair enzymes in the absence of ABCB7.

2) Provide additional experimental support for the conclusion that ABCB7 is of lower importance in mature B cells compared to developing B cells. This is implied by data in CD23-Cre system, but not firmly established. Reviewers make several comments and suggestions that should be considered (e.g. Rev 1, ¶2; Rev 2, Points 1 and 2; Rev 3, points re Figure 1).

We thank the reviewers for this important suggestion. We have added qPCR data demonstrating efficient deletion of ABCB7 in follicular and marginal zone B cells from CD23-cre ABCB7 cKO mice. In new Figure 8, we now show that ABCB7 is important for splenic B cell proliferation and class switching after stimulation. These data demonstrate that ABCB7 is not required for peripheral B cell homeostasis, as the numbers and proportions of peripheral B cells were unchanged in CD23-cre ABCB7 cKO mice (Figure 1), but is important for proliferation and class switching.

3) The conclusion that the major impact of ABCB7 occurs on the population C to C' developmental transition is not sufficiently convincing, based upon the results shown. Further clarification in data presentation and discussion would be very helpful to address this area of confusion.

We have adjusted the text to clarify that we believe the block in B cell development in the bone marrow is occurring in pro-B cells leading to a severe defect in B cell development at subsequent stages, rather than specifically at the Fr. C to Fr. C’ transition. Please see specific responses to the reviewer comments below.

4) There are quite a few technical issues raised by the reviewers. These need to be addressed. In some cases, this may just require clearer description and explanation; in others, additional experimentation will be needed.

These have been addressed in our point-by-point responses to each reviewer below.

Reviewer #1:

A key question that arises from this study is whether DNA replication in addition to immunoglobulin heavy chain gene rearrangement are affected by the absence of ABCB7. The authors provide evidence that both processes are affected; yet if DNA replication was strongly impaired, why would transgenic expression of a rearranged heavy chain gene largely suppress the effects of ABCB deficiency? Part of the answer could be that the lesion in DNA replication remains and may slow cell proliferation without halting development. To address this, the authors could look at EdU incorporation by pro-B cells and pre-B cells in WT and ABCB cKO mice that both carry the HEL-Ig transgene. If lesions in DNA replication persist, it would further support conclusion that DNA damage during replication is in part responsible for the phenotype.

We thank the reviewer for these suggestions and have added these results in new Figure 9. Pro-B cells (defined by B220+CD19+CD43+CD127+) from Hel-Ig Mb1-cre ABCB7 cKO and HEL-Ig WT mice had equivalent proportions of EdU incorporation and similar cell cycle status as analyzed by DAPI. This is in contrast to Mb1-cre ABCB7 pro-B cells which had a reduced proportion of cells that incorporated EdU (Figure 7). Interestingly, similar to Mb1-cre ABCB7 cKO pro-B cells, HEL-Ig Mb1-cre ABCB7 cKO pro-B cells had greater pH2A.X expression in proliferating cells and reduced EdU gMFI, suggesting a slower rate of DNA replication and increased DNA damage as compared to Hel-Ig WT pro-B cells. Unlike ABCB7-deficient pro-B cells, HEL-Ig Mb1-cre ABCB7 cKO pro-B cells had normal Ki-67 expression compared to HEL-Ig WT pro-B cells. This suggests that the presence of the fully rearranged MD4 HEL-Ig transgenic BCR is able to rescue proliferation in the absence of ABCB7, despite the presence of DNA damage. We believe this may be due to differences in the signals received by pro-B cells in these different models. In HEL-Ig transgenic mice, fully rearranged IgM and IgD receptors are expressed on B220+CD19+CD43+ pro-B cells at high levels; while in non-transgenic mice, pro-B cells signal through the pre-BCR, which is expressed at low levels. Therefore, we believe differences in BCR vs pre-BCR signaling may be responsible for the restored proliferation in HEL-Ig Mb1-cre ABCB7 cKO pro-B cells. In support of this, we observed that the extent of proliferation of ABCB7-deficient splenic B cells was dependent upon the stimulation conditions (new Figure 8).

The authors used CD23-cre to delete the gene encoding ABCB7 in mature B cells and didn't see any strong phenotype, suggesting ABCB7 is less important in these cells relative to developing B cells. Given a largely negative result was obtained, it would seem important to use PCR or Western blot analysis to confirm that the gene encoding ABCB7 is efficiently deleted in mature B cells by CD23-cre.

We performed qPCR analysis on FACS sorted follicular (CD19+AA4.1+CD21/35+IgM+) and marginal zone (CD19+AA4.1+CD21/35hiIgMhiCD1d+) B cells from CD23-cre ABCB7 cKO mice. As expected, we found that both populations were deficient for Abcb7 expression (new Figure 1 —figure supplement 2C).

Going further, if the authors wish to solidify the conclusion that ABCB7 is less important in mature B cells, they could stimulate WT and cKO cells in culture with LPS without or with the appropriate cytokines and measure proliferation and class-switching. Negative results here would support the idea that ABCB7 is largely dispensable in mature B cells; defects would support the conclusion that ABCB7 is required for DNA replication/proliferation and/or class switching by mature B cells.

We thank the reviewer for their suggestion to stimulate peripheral B cells from CD23-cre ABCB7 cKO mice. We performed an analysis of proliferation and class switching in splenic B cells from CD23-cre ABCB7 cKO and littermate controls. Enriched CD19+ cells from these mice were stimulated with LPS and various cytokines and/or anti-δ-dextran to induce activation, proliferation, and class switching to IgG1, IgG2a, IgG2b, IgG3, and IgA. Despite normal peripheral B cell homeostasis, ABCB7-deficient peripheral B cells had a striking defect in proliferation and class switching (new Figure 8). Interestingly, the severity of the defect in proliferation was highly dependent upon the stimulation conditions used, demonstrating that the type of stimuli received has differential effects on the extent of proliferation. Furthermore, ABCB7-deficient cells in these cultures had a larger proportion of undivided cells that lacked Ki-67 expression, suggesting these cells have lost proliferation potential (new Figure 8 —figure supplement 3). We hypothesize that the defect in class switching is largely a result of impaired proliferation, which has been described thoroughly in the literature 10–14. No differences were observed in the expression of class switched antibody isotypes in the splenic B cells from CD23-cre ABCB7 cKO mice and littermate controls (new Figure 8 —figure supplement 1). Thus, while ABCB7 deficiency is dispensable for maintenance of splenic B cell numbers at homeostasis, ABCB7 is required for splenic B cell proliferation.

Reviewer #2:

The report by Lehrke et al., describes a phenotypic analysis of B-lymphocyte development in mice with conditional deletion of the ABCB7 gene. Deletion in early B-cell progenitors using a mb1-cre driver result in a dramatic block in B-cell development. In contrast, deletion of the gene in mature cells, using a CD23-cre mouse strain, do not appear to cause observe any dramatic effects on either peripheral cell numbers or iron content in the cells. The developmental block induced by deletion of the ABCB7 gene in early progenitors could be overcome by expression of a BCR transgene, indicating a recombination deficiency as underlying cause of the developmental disruption. In consistency with this, the progenitor B-cells displayed an increased level of pH2gX. However, this was most prominent in proliferating cells that normally do not undergo VDJ recombination. Therefore, the authors suggest that the developmental block is a result of increased general DNA damage.

While the experimental designs in many ways are elegant, there are certain aspect of the analysis that complicates the interpretation of the data. Despite that the authors claim that the major block in differentiation occurs in the fraction C to C', a major part of the downstream analysis is made on a combined C/C' population. Hence, it is difficult to determine if the phenotypes observed is a consequence of a shift in populations or effects directly related to the function of ABCB7. It is also hard to understand how the expression of a functional B-cell receptor should be sufficient to overcome a general increase in DNA damage. Neither do the authors provide any direct link between DNA repair and ABCB7 function.

Hence, while the report clearly shows that ABCB7 is essential for normal early B-cell development, the mechanism by which mitochondrial iron-glutathione transport is linked to B-cell differentiation remains largely elusive.

1: The authors might want to investigate the deletion efficiencies in the more mature cell populations. As deletion of an essential gene cause a dramatic selection pressure this might result in that mature cells represent progenitors that has retained one or two functional alleles. This can complicate the interpretation of the data. Could this possibly be a reason for the apparent heterogeneity in the cKo cells in Figure 2J and 3A? As no phenotype is observed in Fig3S1, the interpretation of the data in Figure 3S1 would also be easier upon verification of functional ABCB7 deletion.

As stated above, qPCR was performed and ABCB7 is efficiently deleted in follicular and marginal zone B cells from CD23-cre ABCB7 cKO mice. Thus, the lack of Phen Green quenching, indicative of iron overload, in splenic B cells in Figure 3 —figure supplement 1 is not due to lack of ABCB7 deletion in that model. While there is heterogeneity observed with Phen Green quenching in ABCB7-deficient pro-B cells, we believe that this simply indicates that time is needed to accumulate iron.

2: The authors could consider to be more stringent with their interpretations of their data. For instance, the reduced fraction B numbers in mice carrying a CD23 driver is considered as "largely unaffected" despite a p-value of 0.006. In contrast, the authors state " There was a trending, but not significant, decrease in HO-1 expression in Mb1-cre ABCB7

cKO pro-B cells (Figure 3B), confirming the previous findings that ABCB7 affects heme biosynthesis". indicating that a trend confirms a finding.

We have eliminated any discussion of trends and limited interpretation to observed changes with statistical significance of p < 0.05.

As stated by Reviewer 3, it is impossible to completely isolate all the cells from the bone marrow. Based on their suggestions, we have analyzed the frequency of each developing B cell population in Figure 1. Based on frequency, there is no statistical difference in the frequency of Fr. B cells from Cd23-cre ABCB7 cKO mice as compared to controls. The analysis of absolute numbers of bone marrow cells has been moved to Figure 1 —figure supplement 1.

3: Much of the data analysis is obscured by the use of the combined C/C´ population. The authors claim that the C to C´ transition is targeted, hence in order to generate conclusive data, these populations should be analyzed separately. The data in figure 4S1B suffer from the same problem as this, as far as I can understand, is done on unsorted BM. In order to be informative sorted fraction B cells should have been seeded. The Y axis would benefit from inclusion of the number of seeded cells to be more informative.

We agree and have clarified the text and figures to demonstrate that we believe that the better way to describe the B cell phenotype is that there is a defect initiating at the pro-B cell stage, leading to a severe loss of subsequent development stages. Regarding the pre-B CFU assay, we followed the manufacturer’s instructions to seed total bone marrow. This kit is designed to generate pre-B CFU, and sorting Fr. B cells would not have worked in this assay. In addition to IL-7 for robust expansion, Fr. B cells also require stromal cell support as well as Flt3 ligand and SCF signals, which are not provided by the pre-B CFU assay. To clarify the data, we have moved the pre-B CFU data Figure 2 —figure supplement 1 as it better supports failure to generate pre-B cells. We have also added the seeded cell density to the figure and figure legend.

4: I can be discussed if the term Wt can be used for a mix of different genotypes as done on this paper according to the MandM. This might explain the rather strange finding that the CD23 driver cause a significant reduction in fraction B cell numbers. This mixture of genotypes also generates rather strange statements such as the data are generated from 11 independent experiments (Figure legend 1C) despite that only 8 mice from each Ko is analyzed. It is also unclear what the data in the contour plots indicate, I guess one "representative" experiment. It is more stringent to use the average values with a std.

We thank the reviewer for their comments and concern. As stated above, analysis of Hardy fractions as a percentage of live cells revealed that there was not a difference in the proportion of different Hardy fractions in the CD23-cre ABCB7 cKO mice.

We apologize for the confusion regarding the number of independent experiments and analyzed mice figure legends. Figure legends have been updated to clarify that the number of mice reported is the total number of mice analyzed across all independent experiments. Additionally, we have reanalyzed splenic B cell populations in Figure 1 at the suggestion of Reviewer #3. Therefore, the figure legend now reads “…contour plots are representative of seven independent experiments (total of 7-12 mice/group).” This indicates that seven independent experiments were performed, and over the course of these experiments a total of 7-12 mice/group were analyzed (e.g., 12 WT mice, 7 Mb1-cre ABCB7 cKO mice, and 8 CD23-cre ABCB7 cKO mice).

Contour plots are described as, “representative of n independent experiments.” By this we mean that contour plot data shown in a figure is visually equivalent to other independent replicates of that experiment. Throughout our manuscript, we show representative flow cytometry data and then quantify across experiments in a subsequent panel for rigor and reproducibility.

Reviewer #3:

Specific points:

General: Authors continuously use the word "trending" for statistically nonsignificant changes between groups. This is very confusing. If something is not significant, I do not see the point of highlighting that.

We have eliminated any discussion of trends and limited interpretation to observed changes with statistical significance of p < 0.05.

Figure 1: According to figure legend, number of repeats and number of mice used for each repeat is abnormally high. (eleven experiments with 8-17 mice per condition.) This makes hundreds of mice used. I do not see the rationale behind this many repeats considering that only handful of data points are actually shown in the figure and the findings are clear.

We apologize for the confusion regarding the number of independent experiments and analyzed mice figure legends. Figure legends have been updated to clarify that the number of mice reported is the total number of mice analyzed across all independent experiments. For example, a total of 6-11 mice/group were analyzed for Figure 1A (e.g., 11 WT mice, 7 Mb1-cre ABCB7 cKO mice, and 6 CD23-cre ABCB7 cKO mice). Unless otherwise stated in the figure legend, each data point on a bar graph represents one mouse. Please note that splenic B cell populations presented in Figure 1 have been updated to include the use of CD93/AA4.1 and the number of experiments and mice have been updated.

Figure 1 B: Showing absolute numbers is an ideal approach to support a hypothesis however, this strategy is impossible for bone marrow since unlike spleen which is a capsulated organ that can be meshed and counted as a whole bone marrow is not. So, flushing yields in bone marrow will depend on the amount of cutting on each end of the bone and the ability to capture the marrow without loss through needle flushing. While the data shown by authors is pretty significant and convincing, authors should also discuss that this method has limitations. Alternatively, authors can try to normalize by graphing each population as percentage of all viable isolated cells in their suspension.

We agree that any bone marrow harvest procedure has a limitation in that it is impossible to completely flush/remove all bone marrow cells, as cells may become trapped in the bones. To clarify, bone marrow was harvested by centrifugation15 rather than needle flushing. We have found the centrifugation method provides a more thorough bone marrow harvest compared to needle flushing or crushing methods.

We have analyzed the Figure 1 bone marrow data by graphing each Hardy fraction population as a percentage of all viable cells isolated. Using cell frequency, there continues to be a striking difference in B cell development in Mb1-cre ABCB7 cKO mice. This analysis also demonstrated that the proportions of Hardy fractions in CD23-cre ABCB7 cKO mice were not statistically different.

Figure 1A-B: Authors build their manuscript on the observation of decreased Pro to Pre B (Fr C to C') cell in bone marrows of cKO mice. The contour plots on Figure 1A shows a slight change in percentages of Fraction C and C' however authors fail to highlight that point in the absolute cell number graph as both Fr. C and Fr. C' goes down in cell numbers. A blockage at a developmental stage is expected to increase the population before the blockage in which case this should be Fr.C. If authors see this as the selling point of the manuscript then they should show it more clearly. I suggest getting ratio of Fr. C over Fr. C' would help overcome the problems related to general hypocellularity in the bone marrow of cKO animals.

We agree and have altered the manuscript to state that the defect is in pro-B cells leading to a severe loss of pre-B cells, and subsequent stages. Because we observed a statistically significant decrease in Fr. B and Fr. C cell numbers in Mb1-cre ABCB7 cKO mice, we have clarified in the text that we believe the block in B cell development in these mice is generally occurring at the pro-B cell stage. We have also added the ratio of Fr. C and Fr. C’ cells. We found that compared to WT mice, Mb1-cre ABCB7 cKO mice had a significantly higher ratio of Fr. C cells over Fr. C’ cells (new Figure 1 —figure supplement 1B).

Figure 1 C: Gating of splenic B cell fractions should involve CD93 in order to discriminate the transitional cell pool which then can be further divided into T1-2-3 subpopulations using IgM and CD23. The gating strategy authors used is unconventional. If possible, it would be nice to see CD93 staining as well. Furthermore, using CD23 cre mouse will not be able to reveal the effects of ABCB7 KO in marginal zone and T1 cells which express little or no CD23. That limitation needs to be discussed in the text.

We thank the reviewer for their suggestion and comments. We have updated Figure 1 to include CD93 (AA4.1) and a different gating strategy for splenic B cells. We now analyze the following CD19+ populations: T1 (AA4.1+CD21/35-IgM+CD23-), T2 (AA4.1+CD21/35-IgM+CD23+), T3 (AA4.1+CD21/35+IgM+), FO (AA4.1-CD21/35+IgM+), and MZ (AA4.1-CD21/35hiIgMhi).

Regarding CD23-cre expression in T1 B cells, we agree that these mice express little CD23 and therefore would not have robust Cre expression. As described above, we analyzed expression of a human CD5 reporter that is linked to CD23-cre expression via an IRES. We did not observe robust huCD5 reporter expression in T1 B cells, as expected. The reporter was expressed in T2, T3, and FO B cells. As described above, we performed qPCR analysis for Abcb7 expression in FACS sorted follicular and marginal zone B and found that both populations efficiently deleted Abcb7. We have added discussion of the limitation to examining T1 cells as suggested.

Figure 2 demonstrates convincing evidence on the effect of conditional KO in regulation of a select group of critical transcriptional mediators of pro to pre-B cell transition. While not necessary, an unbiased RNA seq experiment would have provided a far better portrait of the alterations in transcriptome level than qPCR experiments focusing on a handful of molecules.

We thank the reviewer for their suggestion, and plan to do this in the future.

Figure 3: The use of mitotracker green for general mitochondrial mass evaluation is not ideal. It has been shown multiple times that various factors including pH and mitochondrial dysfunction can affect the fluorescence intensity of mitotracker green. Authors should use fluorescent antibodies against specific mitochondrial markers such as VDAC-1, TOM-20 and COX-IV to comment on mitochondrial mass. Furthermore, TMRM staining alone does not give any idea about mitochondrial health. Changes in mitochondrial number between groups can easily cause shifts in TMRM intensity. Therefore, it needs to be normalized to exclude mitochondrial number in order to be used as a parameter that can detect mitochondrial performance. To do so, each sample needs to be divided into three groups. Groups need to be treated with either FCCP (basal staining indicator when mitochondria are depolarized) or oligomycin (maximum staining indicator when mitochondria are hyperpolarized) or nothing (unaltered current level) and TMRM values should be measured for each. Then using the formula 100x [ (MFI (untreated)- MFI(FCCP) )/ (MFI oligomycin)-MFI (FCCP)] percentage of maximum mitochondrial membrane potential used by each group can be found and this values are independent from the differences in mitochondrial numbers. I believe this may change the interpretation of results.

We have added flow cytometry analysis of VDAC1 expression and found similar expression in ABCB7-deficient pro-B cells as compared to WT pro-B cells (new panel in Figure 3). Thus, there is similar mitochondrial mass by either MitoTracker Green and VDAC1 expression, providing support that the lack of difference in TMRM staining is not due to alterations in mitochondrial mass.

Figure 3: Authors should show under microscopy that Phen Green SK colocalizes with TMRM (or any other mitochondrial stain) in order to confirm the staining differences in Figure 1A are located to changes in mitochondrial levels. Occasionally, these dyes have nonspecific binding issues. This experiment will rule out that possibility.

Unlike other cellular dyes that analyze fluorescence intensity, Phen Green is read out as fluorescence quenching. This is because Phen Green is a general cell dye that brightly stains the both the cytosol and organelles. This fluorescence is partially quenched in the presence of heavy metal ions, but not completely absent. Therefore, it would be difficult to assess the localization of fluorescence quenching to the mitochondria, especially in developing lymphocytes which do not have an abundance of cytoplasm.

Figure 3F: Mitochondrial ROS is not detected is a misleading claim (Page 13 lane268) I agree that the MitoSOX levels are identical between cKO and the control however, flow cytometry plots are relative for these dyes. Any mitochondria healthy or unhealthy produces some level of ROS and this is even thought to act as a part of normal physiology if the levels are within limits. So authors should amend this part.

We thank the author for the suggestion and have amended the text.

Figure 4: It is a good idea to culture cells and measure Annexin levels which would rule out the possibility that apoptotic cells get cleared fast in bone marrow before they are detected. However, I think Annexin measurement at 16 h is not enough to make a statement. Authors should monitor viability using Live-Dead reagents at early and late time points (such as 3-6-9-12-16-24-48h or something like that) and graph changes. Additionally, commercially available apoptosis, necrosis kits that can detect early and late phases of apoptosis are available and can be beneficial to strengthen the statement.

In Figure 8 —figure supplement 2, we have examined viability over four days in culture of magnetically enriched splenic B cells from WT and CD23-cre ABCB7 cKO mice. No statistical differences were observed in viability of unstimulated cells across the time course.

Page 18, lane 377 onwards: It was not very clear how authors introduced the transgene to the mice for the reader. I figured out by looking at the methods section that this is actually crossbreeding the mice with MD4. However, it sounds from the text as if it was done through transfection. Authors should clearly state that they did cross breeding in this section to avoid any confusion. Also, the use of MD4 mice dates back to 1980s, I think the 1994 paper authors cited is not the original one. It should be a Goodnow paper as far as I remember, please double check.

We apologize for the confusion regarding the use MD4 HEL-Ig mice. We have altered the text to clarify their use. Additionally, the reviewer was correct regarding the 1988 Goodnow paper and the citation has been changed.

Figure 7: Similar to Figure 2, authors hand pick a few markers to prove their point. While the selection is elegant and relevant, an unbiased approach would have been far more convincing. Furthermore, the list of markers tested are hard to follow and I did not understand why or how they get affected with slight change of iron levels? Are these molecules regulated by iron levels? How does iron relate to these changes? There is a gap in the story in here.

We thank the reviewer for their comments. We plan to perform RNA-seq in the future for an unbiased examination of gene expression changes.

https://doi.org/10.7554/eLife.69621.sa2

Article and author information

Author details

  1. Michael Jonathan Lehrke

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Supervision, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2376-9168
  2. Michael Jeremy Shapiro

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Matthew J Rajcula

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Data curation, Formal analysis, Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Madeleine M Kennedy

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Shaylene A McCue

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Kay L Medina

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Virginia Smith Shapiro

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing – review and editing
    For correspondence
    shapiro.virginia1@mayo.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9978-341X

Funding

National Institute of Allergy and Infectious Diseases (1R21 AI157328-01)

  • Virginia Smith Shapiro

National Institute of Allergy and Infectious Diseases (T32AI007425)

  • Michael Jonathan Lehrke

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the members of the VSS, KM, and Hu Zeng (Mayo Clinic) laboratories for their helpful discussions of this work.

Ethics

This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#A3738-18) of the Mayo Clinic.

Senior Editor

  1. Betty Diamond, The Feinstein Institute for Medical Research, United States

Reviewing Editor

  1. Gail Bishop, University of Iowa

Reviewer

  1. John Colgan

Publication history

  1. Received: April 21, 2021
  2. Accepted: October 20, 2021
  3. Version of Record published: November 11, 2021 (version 1)

Copyright

© 2021, Lehrke et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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