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. 2023 Dec 22;216(1):45–54. doi: 10.1093/cei/uxad135

Gene expression analysis revealed downregulation of complement receptor 1 in clonal B cells in cold agglutinin disease

Agnieszka Małecka 1,2,3,✉, Ingunn Østlie 4, Gunhild Trøen 5, Jędrzej Małecki 6, Jan Delabie 7, Anne Tierens 8, Ludvig A Munthe 9,10, Sigbjørn Berentsen 11, Geir E Tjønnfjord 12,13
PMCID: PMC10929701  PMID: 38133636

Abstract

Cold agglutinin disease (CAD) is a rare B-cell lymphoproliferative disorder of the bone marrow, manifested by autoimmune hemolytic anemia caused by binding of monoclonal IgM autoantibodies to the I antigen. Underlying genetic changes have previously been reported, but their impact on gene expression profile has been unknown. Here, we define differentially expressed genes in CAD B cells. To unravel downstream alteration in cellular pathways, gene expression by RNA sequencing was undertaken. Clonal B-cell samples from 12 CAD patients and IgM-expressing memory B cells from 4 healthy individuals were analyzed. Differential expression analysis and filtering resulted in 93 genes with significant differential expression. Top upregulated genes included SLC4A1, SPTA1, YBX3, TESC, HBD, AHSP, TRAF1, HBA2, RHAG, CA1, SPTB, IL10, UBASH3B, ALAS2, HBA1, CRYM, RGCC, KANK2, and IGHV4-34. They were upregulated at least 8-fold, while complement receptor 1 (CR1/CD35) was downregulated 11-fold in clonal CAD B cells compared to control B cells. Flow cytometry analyses further confirmed reduced CR1 (CD35) protein expression by clonal CAD IgM+ B cells compared to IgM+ memory B cells in controls. CR1 (CD35) is an important negative regulator of B-cell activation and differentiation. Therefore, reduced CR1 (CD35) expression may increase activation, proliferation, and antibody production in CAD-associated clonal B cells.

Keywords: cold agglutinin disease, gene expression, complement receptor 1

Differential mRNA expression analysis was performed in clonal B-cells from CAD patients, using IgM-expressing memory B cells from healthy individuals, as controls. While several genes, including SLC4A1, SPTA1, YBX3, TESC, HBD, AHSP, TRAF1, HBA2, RHAG, CA1, SPTB, IL10, UBASH3B, ALAS2, HBA1, CRYM, RGCC, KANK2, and IGHV4-34 were found upregulated, at least 8-fold, the gene for complement receptor 1 (CR1/CD35) was found downregulated 11-fold. Importantly, flow cytometry analyses confirmed that clonal CAD IgM+ B cells had reduced CR1 protein expression, compared to IgM+ memory B cells in controls.

Graphical Abstract

Graphical Abstract.

Graphical Abstract

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Introduction

Cold agglutinin disease (CAD) is a rare B-cell lymphoproliferative disorder of the bone marrow characterized by autoimmune hemolytic anemia (AIHA) mediated by binding of monoclonal IgM autoantibodies to the I antigen [1]. The pathology and genetic features of CAD have been studied in detail during the last decade, leading to the recognition of CAD as a distinct lymphoproliferative disease by the 2022 World Health Organization classification of hematolymphoid tumors and the International Consensus Classification of Mature Lymphoid Neoplasms [2–6]. KMT2D and CARD11 genes are recurrently mutated in CAD B cells. KMT2D has loss-of-function mutations, and CARD11 has gain-of-function mutations [3]. More recently, a more complete mutational landscape of CAD has been described. Of interest, CARD11 and CXCR4 mutations were correlated with hemoglobin levels, suggesting pathogenic involvement [7]. Further, trisomy 3 and 12 or 18 are common in CAD, with the presence of trisomy 12 and 18 correlating with response to treatment [2]. Since CAD is a clonal B-cell disease, B-cell-directed therapy is now the treatment of choice for most patients [8]. Recently, inhibition of complement protein C1s by sutimlimab has been documented as an alternative, highly efficacious approach with selective upstream inhibition of the classic complement pathway and halted hemolysis [9].

Here, we present novel RNA-sequencing (RNA-seq) data from 12 CAD patients. Differential expression analysis revealed that the complement receptor 1 (CR1/CD35) gene was the most significantly downregulated gene in CAD B cells compared with controls. Flow cytometry analysis further confirmed reduced CR1 (CD35) protein expression on clonal CAD B cells. CR1 (CD35) is a membrane-bound complement receptor and an important negative regulator of B-cell differentiation towards immunoglobulin-producing plasma cells. Moreover, it is shown to be downregulated on B cells in some autoimmune diseases, i.e. rheumatoid arthritis and systemic lupus erythematosus [10–16]. Its reduction may, therefore, contribute to CAD pathogenesis.

Materials and methods

Materials and RNA-seq analysis

Peripheral blood and bone marrow samples were obtained from CAD patients enrolled in a prospective trial [8]. Additionally, peripheral blood was obtained from healthy controls. The diagnostic criteria for CAD have been reported previously [8]. Clonal B cells from bone marrow of 12 CAD patients were enriched by fluorescence-activated cell sorting as published before [17] (Supplementary Fig. S1). Immunophenotype of the clonal B cells for each patient is shown in Supplementary Table S1. Normal memory B cells (CD19+/CD27+/IgM+) from four healthy controls were stained for surface antigens with antibodies purchased from BioLegend [anti-CD27 (clone O323) and anti-IgM (clone MHM-88)], Beckman Coulter [anti-CD19 (clone J3-119)], and Becton Dickinson [anti-CD3 (clone SK7) and anti-CD45 (clone 2D1)]. Both CAD and healthy control samples were sorted using a FACS Aria Ilu high-speed sorter (Becton Dickinson, Franklin Lakes, NJ, USA) equipped with 408, 488, and 633 nm lasers. DNA and RNA were extracted simultaneously from the same cells using Qiagen AllPrep DNA/RNA Micro Kit. DNA was analyzed previously and the results have been published [2, 3, 7]. RNA library preparation was performed using Kappa Total RNA prep combined with Twist capturing system Human Core Exome (according to manufacturer recommendations), followed by library QC and sequencing using NextSeq500 HighOutput flow cell with 2 × 75 bp paired-end sequencing that was performed at the Genomics Core Facility at Oslo University Hospital/University of Oslo.

Analysis to assess overall similarity between samples from CAD patients and controls

Two methods were used to check overall similarities of total gene expression between CAD B cells and normal memory B cells. First, sample distances were calculated using the Poisson Distance implemented in the ‘PoiClaClu’ R package (Supplementary Fig. S2A). Then the R function ‘dist’ was used to calculate the Euclidean distance between samples (Supplementary Fig. S2B). Both methods resulted in clustering of controls within patients’ samples, a result expected from appropriate control’s samples. Control samples were very homogeneous, while CAD samples were heterogeneous.

Differential expression analyses

Reads were aligned to hg38 with the use of STAR v2.7.0f. Analyses were performed on Services for Sensitive Data (TSD) server at the University of Oslo. Reads were counted with both STAR and FeatureCounts, and differential expression was analyzed using both DESeq2 and EdgeR. Results from FeatureCounts followed by DESeq2 were used for the main analysis (order of presented genes). Results from STAR followed by EdgeR were then used for validation. Genes were chosen based on results from FeatureCounts followed by DESeq2 with P-adj < 0.001 (P-value adjusted for multiple testing; top 530 genes) and from STAR followed by EdgeR with FDR < 0.05 (false discovery rate; top 550 genes). Only genes that were detected as statistically significant by both methods were used for further analyses. Additionally, immunoglobulin lambda light chain (IGL) genes were removed, since CAD samples were sorted for immunoglobulin kappa light chain (IGK) expressing B cells. T-cell receptor genes were also removed. The final list contained 306 statistically significant genes. In search of biologically relevant changes in gene expression, the list was further scrutinized by keeping only genes with fold change <−8 or >8 and median number of reads, in at least one group, at minimum of 50 reads. The final list contains 93 genes. This stringent cutoff for differential expression analysis was applied to rule out any spurious results.

Gene expression of the final 93 genes was visualized using hierarchical clustering and heatmap generated by Bioconductor package. Normalized number of reads for each sample is presented, to account for differences in library size. In order to present the data, a log2 transformation was used. All raw data presented in Tables and Figures are normalized with the use of DESeq2 to account for differences in library sizes. In addition, Volcano plot (https://huygens.science.uva.nl/VolcaNoseR2/) was used for visualizing top 306 statistically significant genes.

Fusion protein testing

Testing for fusion genes, large indels, and other structural variants was performed with the use of STAR, STAR-fusion, and Manta programs.

Flow cytometry validation of CR1 (CD35) expression

Flow cytometry analysis was performed as described before with the addition of antibody for CR1 (CD35) from BioLegend [PE anti-human CD35 Antibody (clone E11)] [17]. In short, viable cells were gated using the forward scatter versus side scatter dot plot, then CD45 bright, low side scatter events (i.e. lymphocytes) were selected. CD5+ and CD19− events (i.e. T cells) were gated out, leaving only B cells. Finally, monoclonal B cells were separated from the polyclonal B cells using the immunoglobulin light chain gate, taking advantage of the fact that B-cell clones show either kappa or lambda light chain restriction [3, 17]. CR1 (CD35) expression was assessed separately in IGK and IGL expressing B cells [17]. Since our cohort did not contain patients with IGL expressing cold agglutinins, IGL+ B cells were used as in-sample controls. CR1 (CD35) expression in IGK and IGL expressing B cells was analyzed both for CAD patients and for controls.

Results

Gene expression by RNA-seq analysis

The 93 most differentially expressed genes in the samples of CAD patients are listed in Supplementary Table S2. The top 20 differentially expressed genes were: SLC4A1, SPTA1, YBX3, Tescalcin (TESC), HBD, AHSP, TRAF1, HBA2, RHAG, CR1 (CD35), CA1, SPTB, IL10, UBASH3B, ALAS2, HBA1, CRYM, RGCC, KANK2, and IGHV4-34 (Supplementary Table S5; Fig. 1A, B and F). Nineteen of these 20 genes were upregulated at least 8-fold compared to controls. Only one, namely CR1 (CD35), was downregulated about 11-fold. P-value and P-value adjusted for multiple testing were equal or lower than 3.0 × 10−12 and 2.7 × 10−9, respectively, therefore considered highly significant.

Figure 1.

Figure 1.

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Analysis of the most differentially expressed genes in CAD patients with a focus on biologically relevant changes. (A) Heatmap showing 93 of the most differentially expressed genes. Colors correspond to expression levels. Top 20 genes are marked with green boxes, with exception of CR1 (CD35) and IL-10 that are marked with red boxes. (B) Box plots visualizing gene expression levels for top 20 differentially expressed genes. All genes are highly upregulated in comparison to controls, with the exception of CR1 (CD35) gene that is highly downregulated in CAD. Controls are shown in blue and CAD patients in orange. Minimum and maximum values together with median, first and third quartile are shown. There is separate scale for YBX3 and IGHV4-34 due to significant difference in expression. (C–E) Visualization of CR1 (CD35), CR2 (CD21), and IL-10 expression based on RNA-seq data for CAD patients and controls. For CR1 (CD35), CR2 (CD21), and IL-10 the difference between CAD and controls is about 11, 6, and 60 times, respectively. (F) The most differentially expressed genes in CAD. Volcano plot is visualizing top 306 statistically significant genes detected by two methods as explained in materials and methods. Log2 fold change is shown on X-axis and significance (calculated as −Log10 of P-value) on Y-axis. Log2 fold change threshold is set so that values <−3 and >3 are considered significant (it corresponds to fold change <−8 and >8). Top 20 genes are marked with names. Genes marked as green are downregulated while genes marked as red are upregulated. Note: panel A shows Log2 transformed data (transformation is necessary for visualization purposes), other panels however are showing raw data after normalization only, necessary to account for differences in library size. HC: controls, X-axis: patient or control, Y-axis: number of reads.

CR1 (CD35) was downregulated, and its expression level was very similar within each group, but the difference between patients and controls was about 11 times (Fig. 1C). When using STAR followed by EdgeR, CR1 (CD35) was assigned at the top of the most differentially expressed genes. Since CR1 (CD35) was strongly downregulated, we investigated complement receptor 2 (CR2/CD21) in our RNA-seq data and found CR2 (CD21) also to be downregulated in CAD, but not as pronounced as CR1 (CD35). The P-value is 1.3 × 10−6, while P-value adjusted for multiple testing is 1.1 × 10−4, but with a 6-fold change (Fig. 1D). Therefore, CR2 (CD21) was not included in the list of most differentially expressed genes.

Interleukin 10 (IL-10) was highly upregulated (Fig. 1E). Controls had practically no expression of IL-10, while CAD B cells had substantial expression of this cytokine (P-value 7.6 × 10−14 and P-value adjusted for multiple testing 1.2 × 10−10).

The most significantly upregulated pathways, according to Reactome (https://reactome.org/), were connected to O2/CO2 exchange in erythrocytes with 6 genes: HBA1, HBB, HBA2, RHAG, SLC4A1, and CA1 in the top 25 most differentially expressed genes (Fig. 1A; Supplementary Tables S2 and S3). In addition, genes associated with erythrocyte membrane design, SLC4A1, SPTA1, SPTB, and EPB42 were upregulated and were among the top 30 differentially expressed genes (Fig. 1A; Supplementary Table S2).

Validation of CR1 (CD35) expression by flow cytometry

Reduced CR1 (CD35) gene expression was validated by flow cytometry (Fig. 2; Table 1). CR1 (CD35) protein expression was assessed in blood or bone marrow samples obtained from 15 CAD patients (11 frozen and 4 fresh samples; Fig. 2A). Additionally, five non-clonal B-cell samples from CAD patients in complete remission (Fig. 2B) and blood samples from five controls (Fig. 2C) were analyzed. For each sample, CR1 (CD35) expression was compared between IgM+ CD27+ CD19+ memory B cells expressing IGK+ and the CAD clone (if possible to separate), as well as IgM+ memory B cells expressing IGL+, respectively. The latter served as non-clonal patient-specific normal B-cell controls. It was considered important to have patient-specific controls as CR1 (CD35) expression may vary between individuals. In CAD patients, CR1 (CD35) expression by clonal IGK+ B cells was significantly lower than by IGL+ control B cells (Table 1; Fig. 2A and D) as indicated by a median fluorescence intensity that was 11 times lower in clonal B cells than in normal IGL+ B cells. In addition, there were five CAD samples without detectable clonal CAD B cells at the time of testing, and three of those were dominated by immature B cells likely related to regeneration after recent B-cell-directed therapy. In these cases, IGK+ and IGL+ B cells had almost the same level of CR1 (CD35) expression (Fig. 2B and D). B cells from controls showed similar expression of CR1 (CD35) by both IGK+ and IGL+ B cells (Fig. 2C and D). Samples from clonal CAD patients usually showed a bimodal CR1 (CD35) expression by IGK+ B cells, but not by IGL+ B cells (Fig. 2A).

Figure 2.

Figure 2.

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Validation of CR1 (CD35) expression by flow cytometry. (A) Samples from CAD patients with demonstrated CAD clone. (B) Samples from CAD patients in remission. (C) Controls. (D) Median fluorescence intensity (100% stacked column chart; values are shown in columns that are stacked to represent 100%) for IGK+ B cells (containing clonal CAD B cells) and normal IGL+ B cells from the same patient. IGK+ and IGL+ B cells from CAD patients in remission and control samples are also presented. Samples with clonal B-cell population show a significant reduction in CR1 (CD35) expression in clonal CAD B cells compared to normal IGL+ B cells. In contrast, patients in remission at the time of testing and controls (HC) show similar CR1 (CD35) expression between IGK+ and IGL+ B cells. The median fluorescence intensity is used for comparisons, since it is the most informative measure of central tendency for skewed distributions or distributions with outliers. Note: IGK+ B cells: IGM+ memory B cells expressing IGK, IGL+ B cells: IGM+ memory B cells expressing IGL. aSample from a CAD patient with demonstrated CAD clone at the time of testing, bsample from a CAD patient in remission at the time of testing.

Table 1.

CR1 (CD35) expression measured by flow cytometry, mean and median fluorescence intensity for different B-cell populations

Samples CAD clone Material Total B cells CD19+ CD20+ IGK+ B cells CAD clone B cells Normal/IGL+ B cells
Mean FI Median FI Mean FI Median FI Mean FI Median FI Mean FI Median FI
CAD 1.06 CD5+ K+ BM 1004 261 986 255 973 245 3413 2571
CAD 1.24 CD5− K+ BM 847 491 715 415 572 347 2112 1745
CAD 1.25 CD5− K+ PB 1703 885 1222 264 806 177 3138 2656
CAD 1.26 CD5− K+ PB 3937 3001 3486 2527 2613 1665 5429 4548
CAD 1.30 CD5− K+ BM 720 251 585 224 585 224 3148 2520
CAD 1.31 CD5+ K+ PB 736 605 735 605 726 596 4787 4114
CAD 1.32 CD5− K+ BM 1473 731 1181 408 1181 408 2862 2428
CAD 5.01 CD5− K+ BM 1644 344 1212 170 1212 170 3291 2841
CAD 5.02 CD5+ K+ BM 711 88 490 62 208 48 2017 1728
CAD-3 CD5+ K+ BM 2354 1429 1886 773 289 49 4034 3155
CAD-13 CD5− K+ PB 981 486 1002 524 1002 524 3306 2597
CAD 5.03a CD5+ K+ PB(F) 3771 2855 3198 2000 902 497 5437 4347
CAD 5.03a CD5+ K+ BM(F) 504 221 474 215 432 210 1776 938
CAD 5.04 CD5+ K+ BM(F) 2086 1321 2036 1295 1841 1099 4505 3504
CAD 5.05 CD5+ K+ BM(F) 1450 110 1065 75 449 55 4162 3611
CAD 5.06 CD5− K+ BM(F) 814 103 625 94 424 88 3617 2520
CAD 1.23 No clone BM(F) 5973 5261 5733 5054 – – 6275 5560
CAD 1.16 No cloneb BM 567c 295c 1070 731 – – 612 379
CAD 1.21 No cloneb BM 587c 366c 916 677 – – 562 358
CAD 1.25 No clone BM 2246 1669 2898 2422 – – 2922 2392
CAD 1.26 No cloneb BM 587c 377c 814 363 – – 533 352
HC-01 No clone PB(F) 5378 4664 5228 4537 – – 5592 4867
HC-02 No clone PB(F) 5421 4594 5278 4458 – – 5611 4770
HC-03 No clone PB(F) 5135 4293 5126 4250 – – 5124 4325
HC-04 No clone PB(F) 8652 7242 8531 7170 – – 8824 7616
HC-05 No clone PB(F) 7355 5966 7204 5906 – – 7618 6103
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aThe same patient.

bTotal B cells CD19+ (mostly immature B cells in this sample, CD20 weak/neg, IGK/IGL neg).

cCD20 neg B cells included.

Abbreviations: IGK+ B cells, IGM+ memory B cells expressing IGK; IGL+ B cells, IGM+ memory B cells expressing IGL; FI, fluorescence intensity; BM, bone marrow; PB, peripheral blood; (F), fresh material; HC: controls.

Fusion gene testing

Extensive testing for fusion genes, large indels, and other structural variants with the use of three programs (STAR, STAR-fusion, and Manta) was performed. No fusion genes were detected.

Discussion

We report dysregulation of gene expression in B cells derived from CAD patients compared to normal memory B cells. Using RNA-seq analysis, we found 93 genes that were highly differentially expressed in CAD B cells, including CR1 (CD35) that was downregulated, and SLC4A1, SPTA1, YBX3, TESC, HBD, AHSP, TRAF1, HBA2, RHAG, CA1, SPTB, IL10, UBASH3B, ALAS2, HBA1, CRYM, RGCC, KANK2, and IGHV4-34 that were upregulated. Importantly, using flow cytometry analysis, we confirmed reduced membrane expression of CR1 (CD35) in CAD B cells compared to normal memory B cells.

CR1 (CD35) is the receptor for C3b and C4b complement peptides [18]. Its main functions are inhibition of the complement cascade [19] and regulation of B-cell proliferation [20–22]. CR1 (CD35) blocks proliferation induced by B-cell receptor (BCR) activation, and in addition, inhibits differentiation of B cells into plasmablasts and plasma cells, as well as their immunoglobulin production [10]. CR1 (CD35) ligation decreases phosphorylation of key molecules of the BCR-induced signaling cascade [11]. Among other upstream targets in the classical complement pathway, CR1 (CD35) inhibits opsonization with C3b, which is a major hemolytic mechanism in CAD [16]. There are several studies showing an association of CR1 (CD35) with autoimmune diseases, but not much is known about CR1 (CD35) association with other diseases. However, there are some studies on CR1 (CD35) in malaria, HIV, SARS, carcinoma of the gallbladder, and other diseases [20]. CR1 (CD35) downregulation on lymphocytes, erythrocytes, and other types of cells is characteristic of several diseases [20]. Reduced expression of CR1 (CD35) has been observed on B cells in autoimmune conditions, e.g. in rheumatoid arthritis [10, 23] and systemic lupus erythematosus [11].

We found that CR1 (CD35) is highly downregulated in clonal B cells from CAD patients. This finding is of interest because complement, in addition to its critical role for hemolysis in CAD [1, 16], has been shown to control B- and T-cell responses, and thus is a regulator of adaptive immunity [24]. Although we have focused on CR1 (CD35), we also found CR2 (CD21) expression to be downregulated in CAD. CR1 (CD35) has different functions depending on the cell type by which it is expressed. In B cells, the main function of CR1 (CD35), together with CR2 (CD21), is regulation of proliferation [21, 22]. CR1 (CD35) binding to C3b and CR2 (CD21) binding to C3d inhibits BCR-mediated activation, proliferation, and antibody production [21]. Therefore, downregulated expression of CR1 (CD35) and CR2 (CD21) may be part of a mechanism that allows autoreactive CAD B cells to proliferate. Downregulation of CR1 (CD35) and CR2 (CD21) expression may thus result in increased antibody production. CR1-mediated downregulation of B-cell proliferation [10, 11, 15] through autoantigen/suboptimal antigen binding to BCR has previously been demonstrated in autoimmune disease. Both CR1 (CD35) and CR2 (CD21) were reported to be downregulated in several autoimmune diseases, e.g. systemic lupus erythematosus [25]. There is contradictory data on the role of CR2 (CD21) in B-cell proliferation. However, a recent report indicates that co-clustering of CR2 (CD21) with BCR inhibits proliferation and antibody production by B cells, especially at suboptimal levels of BCR stimulation [22]. This would suggest that pronounced downregulation of CR1 (CD35) and CR2 (CD21) enables autoreactive B cells to proliferate and survive.

CR1 (CD35) on erythrocytes functions as an inhibitor of the complement cascade [19]. In this study, gene expression analyses as well as flow cytometry analyses were performed on B cells retrieved from a biobank established for samples from CAD patients enrolled in previous clinical studies. Erythrocytes were not available for flow cytometry analyses and could not be investigated. Downregulation of CR1 (CD35) expression on erythrocytes has previously been reported in other diseases, including autoimmune disease [26–29]. Reduced expression of CR1 (CD35) on erythrocytes in CAD has previously been shown in case series, but systematic studies are lacking [26, 30, 31].

IL-10 gene expression was substantially upregulated in CAD B cells with almost no expression in controls. Primed B cells can secrete IL-10 after CD40 and BCR ligation [32], and activated IL-10 secretors include CD24hiCD27+ and CD27hiCD38hi plasmablast B-cell compartments [33]. IL-10 may have anti-inflammatory feedback and regulatory effects, but its precise role in human B-cell biology is still unclear. High expression in CAD B cells could suggest recent BCR ligation and activation of the signaling cascade together with CD40-pathway activation, such as provided help from T helper (Th) cells. Several studies have shown increased expression of IL-10 and a role of IL-10 in AIHA [34–37], and IL-10 expression in B cells may be positively correlated with disease severity [38]. It has also been suggested that genetic factors influencing IL-10 production may increase the risk of developing AIHA [39]. Neutralization of IL-10 has been considered for treatment also in CAD [36].

In addition, several genes connected to cancer were overexpressed in CAD B cells. These include the cold shock protein YBX3, which regulates mRNA transcription, splicing, and translation, and has a role in modulating stress responses, inflammation, and cancer development [40, 41]. TESC plays a role in hematopoietic stem cell differentiation and growth, and its high expression contributes to invasive and metastatic activity in colorectal cancer [42]. Tumor necrosis factor receptor-associated factor 1 (TRAF1) is involved in the classical NF-kB activation and is overexpressed in many B-cell malignancies, including chronic lymphocytic leukemia, non-Hodgkin lymphoma, and Burkitt lymphoma/leukemia [43–45]. The protein tyrosine phosphatase UBASH3B is upregulated in prostate cancer [46], and its overexpression in breast cancer promotes invasion and metastasis [47].

Other overexpressed genes, such as HBA1, HBB, HBA2, RHAG, SLC4A1, and CA1, code for proteins involved in the uptake and release of oxygen and carbon dioxide in erythrocytes. Moreover, genes connected to hereditary spherocytosis, SLC4A1, SPTA1, SPTB, and EPB42, which is the most common red blood cell membrane disorder [48], were overexpressed in the samples of CAD patients. These genes, which code for erythrocyte membrane proteins, were essentially not expressed in controls, whereas their expression in CAD B cells was substantial. It should be noted that expression of hemoglobin and other erythrocyte-related genes is not limited to erythroid cells only, but is also found in a variety of other cells [49–51]. Also, genes of the SLC4 family are overexpressed in many cancer types, which may have diagnostic value and therapeutic potential in cancer treatment [52–54].

The above results of our RNA-seq analysis suggest that CAD B cells contain substantial amounts of mRNA for genes that are typically expressed in erythrocytes, which is puzzling. Our stringent flow-cytometry sorting criteria rule out the possibility that sorted CAD B cells were simply contaminated with erythroid precursors or mature erythrocytes. However, one possible scenario is the transfer of mRNA from these cells to clonal B cells after phagocytosis. Indeed, erythrocyte phagocytosis by human lymphocytes was shown over 50 years ago [55, 56], and more recent studies confirmed that B cells have phagocytic capabilities [57–60]. Multiple studies have shown that plasma cells can phagocyte erythrocytes, especially in disease states such as multiple myeloma [61–64] and monoclonal gammopathy of undetermined significance [65]. In addition, it was shown that B cells are able to phagocyte 3-μm latex beads coated with anti‐IgM antibodies, and that B-cell phagocytosis is required for potent humoral response [66].

Recent experiments may further explain the mechanism of RNA transfer into CAD clonal B cells. First, immune response to sheep erythrocytes is driven by cytosolic recognition of sheep erythrocyte RNA through the RIG-I-like receptor–mitochondrial anti-viral signaling adaptor pathway in phagocytes [67], suggesting that erythrocyte RNA can be transported into the phagocyte cytoplasm. Second, some malignant IgM+ B cells, such as CLL cells, can phagocytose large (3 μm) particulate antigens and present Ag to T cells [68]. Third, very recent alloimmunization experiments demonstrated that B cells can activate T cells by presenting cytosolic erythrocyte antigens by an unknown mechanism, possibly by trogocytosis [69].

Since BCR on clonal CAD B cells is autoreactive against I antigen present on erythrocytes [1], it is likely that CAD B cells can bind erythrocytes by interacting with I antigen on their surface. Moreover, in light of published literature, it is plausible that after such binding some erythrocytes will be phagocytosed by CAD B cells, and in this way, erythrocyte RNA can be transferred to B cells’ cytoplasm. Although erythrocytes cannot synthetize new RNA molecules, they still contain a large number of RNA transcripts [70, 71]. Alternatively, instead of internalizing erythrocytes, CAD B cells could, in fact, have internalized erythrocyte extracellular vesicles (RBCEV) that are increasingly generated after complement activation and are increased in patients with AIHA and other diseases with complement-mediated lysis [72]. Importantly, RBCEV have been demonstrated to very efficiently transfer mRNA into target cells [73]. Elevated levels of RBCEV have been also found in CAD patients; moreover, it correlated with the severity of anemia and hemolytic features [74].

In addition to BCR, Fc and complement receptors might be taking part in erythrocyte uptake and phagocytosis. Phagocytosis via Fc and complement receptors was shown many years ago [75]. CR1 (CD35) and Fc receptors were shown to cooperate in uptake of particles covered by complement proteins and immunoglobulins [20]. In summary, it is plausible, but unproven that the partial erythrocyte-like gene expression profile detected in CAD B cells was not derived from CAD B cells, but rather from erythrocytes, either whole erythrocytes or RBCEV. This RNA transfer could be an imprint of the B-cell specificity in this disease. Further studies are required to investigate the potential effect of erythrocyte antigens and erythroid RNA on CAD B cells.

One of the genes that was found to be highly upregulated in all CAD samples, except for one, was IGHV4-34. This was expected and underscores that our analysis was performed on CAD B cells since it is well established that the IGHV-4-34 gene is used by the clonal B cells in almost all CAD patients [17].

Conclusion

In conclusion, we demonstrate that CR1 (CD35) expression is significantly downregulated in clonal B cells in CAD, both at the mRNA and the protein level. Since CR1 (CD35) blocks proliferation induced by the BCR, downregulation of CR1 (CD35) in CAD might be responsible for increased proliferation and survival of autoreactive B cells. Deregulation, secondary to CR1 (CD35) downregulation, could lead to enhanced activation, IgM production, and secretion of autoantibodies in CAD B cells. We also found that IL-10 and some other genes were substantially upregulated in CAD B cells, but their role in CAD requires further investigations.

Supplementary Material

uxad135_suppl_Supplementary_Figures_S1-S2_Tables_S1-S5
uxad135_suppl_supplementary_figures_s1-s2_tables_s1-s5.docx (2.7MB, docx)

Acknowledgments

We thank Joanna Bilas for excellent technical support. We are grateful for the sequencing services provided by the Helse Sør-Øst Genomics Core Facility at Oslo University Hospital.

Contributor Information

Agnieszka Małecka, Department of Haematology, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Pathology, Oslo University Hospital, Oslo, Norway.

Ingunn Østlie, Department of Pathology, Oslo University Hospital, Oslo, Norway.

Gunhild Trøen, Department of Pathology, Oslo University Hospital, Oslo, Norway.

Jędrzej Małecki, Department of Biosciences, University of Oslo, Oslo, Norway.

Jan Delabie, Laboratory Medicine Program, University Health Network and University of Toronto, Toronto, ON, Canada.

Anne Tierens, Laboratory Medicine Program, University Health Network and University of Toronto, Toronto, ON, Canada.

Ludvig A Munthe, KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Immunology, Oslo University Hospital, Oslo, Norway.

Sigbjørn Berentsen, Department of Research and Innovation, Haugesund Hospital, Helse Fonna Trust, Haugesund, Norway.

Geir E Tjønnfjord, Department of Haematology, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Funding

This study was funded by the South-Eastern Norway Regional Health Authority (Helse Sør-Øst) grant (2018069), The Norwegian Cancer Society, Fondsstiftelsen, Oslo University Hospital, and the KG Jebsen Foundation (grant 19).

Conflict of Interests

Authors declare no financial conflict of interest with regard to publication of this manuscript.

Author contributions

AM, IØ, GT, JD, AT, SB and GET designed the study. AM and IØ performed the analyses. AM, IØ, GT, JM, JD, LAM and GET discussed the results. GT, JD, AT, SB and GET supervised the study. JD, AT, SB and GET reviewed the diagnostic patient samples and collected the clinical data. AM, JD, JM, LAM and GET prepared the manuscript. All authors have critically read the manuscript.

Ethical approval

The patients included in this study were enrolled in a clinical trial. The study was approved by the Regional Committee for Medical and Health Research Ethics of South-East Norway (2012/131/REK).

Patient consent statement

Written informed consent was procured by using consent forms approved by the Regional Committee for Medical and Health Research Ethics of South-East Norway.

Data availability

In accordance with Norwegian legislation and the ethic approval of the study, all sensitive data are stored in protected databases at Oslo University Hospital. On request, the data will be made available for other institutions. Additional approval might be required before sharing. However, non-sensitive data will be shared upon request. For the original data, please contact the corresponding author.

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Associated Data

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

Supplementary Materials

uxad135_suppl_Supplementary_Figures_S1-S2_Tables_S1-S5
uxad135_suppl_supplementary_figures_s1-s2_tables_s1-s5.docx (2.7MB, docx)

Data Availability Statement

In accordance with Norwegian legislation and the ethic approval of the study, all sensitive data are stored in protected databases at Oslo University Hospital. On request, the data will be made available for other institutions. Additional approval might be required before sharing. However, non-sensitive data will be shared upon request. For the original data, please contact the corresponding author.

Articles from Clinical and Experimental Immunology are provided here courtesy of British Society for Immunology

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