Dissecting Integrin Expression and Function on Memory B Cells in Mice and Humans in Autoimmunity

Immunological memory ensures life-long protection against previously encountered pathogens, and in mice and humans the spleen is an important reservoir for long-lived memory B cells (MBCs). It is well-established that integrins play several crucial roles in lymphocyte survival and trafficking, but their involvement in the retention of MBCs in secondary lymphoid organs, and differences between B cell subsets in their adhesion capacity to ICAM-1 and/or VCAM-1 have not yet been confirmed. Here, we use an autoimmune mouse model, where MBCs are abundant, to show that the highest levels of LFA-1 and VLA-4 amongst B cells are found on MBCs. In vivo blockade of VLA-4 alone or in combination with LFA-1, but not LFA-1 alone, causes a release of MBCs from the spleen into the blood stream. In humans, we find that in peripheral blood, spleens, and tonsils from healthy donors the highest expression levels of the integrins LFA-1 and VLA-4 are also found on MBCs. Consistent with this, we found MBCs to have a higher capacity to adhere to ICAM-1 and VCAM-1 than naïve B cells. In patients with the autoimmune disease rheumatoid arthritis, it is the MBCs that have the highest levels of LFA-1 and VLA-4; moreover, compared with healthy donors, naïve B and MBCs of patients receiving anti-TNF medication have enhanced levels of the active form of LFA-1. Commensurate levels of the active αL subunit can be induced on B cells from healthy donors by exposure to the integrin ligands. Thus, our findings establish the selective use of the integrins LFA-1 and VLA-4 in the localization and adhesion of MBCs in both mice and humans

Activation, adhesion and motility of B lymphocytes in health and disease

B cells can be activated by T cell-dependent stimuli, such as CD40 ligation and cytokines, which induce extensive proliferation, class switch recombination and somatic hypermutation. Epstein-Barr virus (EBV) can also induce B cell activation by mimicking T cell help through its main oncoprotein, latent membrane protein 1 (LMP-1). It is regulated by another EBV-encoded protein, EBV nuclear antigen 2 (EBNA-2), which is absent in Hodgkin and Burkitt lymphomas. We have studied LMP-1 induction by cytokines in vitro and shown that LMP-1 is induced through the transcription factor signal transducer and activator of transcription (STAT6) and a newly defined high-affinity STAT6-binding site. When IL-4 is added together with lipopolysaccharide (LPS) or α-CD40 to B cells, it induces homotypic round and tight aggregates in vitro, whereas LPS alone does not induce such morphological changes. I describe here attempts to identify the molecules that regulate these responses. I have shown that the Rho GTPase Cdc42 controls the spreading of B cells, whereas two other molecules in the same family, Rac1 and Rac2, control homotypic adhesion. Further, I have shown by conditional deletion of Cdc42 in B cells that it is important in the humoral immune response.  Dock10 is a guanosine nucleotide exchange factor (GEF) for Cdc42. It is expressed through all differentiation stages of B cell development. However, targeted deletion of Dock10 in B cells does not result in an aberrant phenotype. Furthermore, by studying conditional knockout mice for Dock10, Cdc42, Rac1 and Rac2, I have elucidated the mechanism of cytoskeletal changes during B cell activation, leading to adhesion and motility. My results may lead to a better understanding of normal B cell activation and of EBV infection, which is associated with many human tumours and may help to understand cancer development and progression in B cells.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p

Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics

Periodontitis is a highly prevalent chronic inflammatory disease of the periodontium, leading ultimately to tooth loss. In order to characterize the gene expression of periodontitis-affected gingival tissue, we have here simultaneously quantified and localized gene expression in periodontal tissue using spatial transcriptomics, combining RNA sequencing with histological analysis. Our analyses revealed distinct clusters of gene expression, which were identified to correspond to epithelium, inflamed areas of connective tissue, and non-inflamed areas of connective tissue. Moreover, 92 genes were identified as significantly up-regulated in inflamed areas of the gingival connective tissue compared to non-inflamed tissue. Among these, immunoglobulin lambda-like polypeptide 5 (IGLL5), signal sequence receptor subunit 4 (SSR4), marginal zone B and B1 cell specific protein (MZB1), and X-box binding protein 1 (XBP1) were the four most highly up-regulated genes. These genes were also verified as significantly higher expressed in gingival tissue of patients with periodontitis compared to healthy controls, using reverse transcription quantitative polymerase chain reaction. Moreover, the protein expressions of up-regulated genes were verified in gingival biopsies by immunohistochemistry. In summary, in this study, we report distinct gene expression signatures within periodontitis-affected gingival tissue, as well as specific genes that are up-regulated in inflamed areas compared to non-inflamed areas of gingival tissue. The results obtained from this study may add novel information on the genes and cell types contributing to pathogenesis of the chronic inflammatory disease periodontitis

The Expression Pattern of the Pre-B Cell Receptor Components Correlates with Cellular Stage and Clinical Outcome in Acute Lymphoblastic Leukemia

<div><p>Precursor-B cell receptor (pre-BCR) signaling represents a crucial checkpoint at the pre-B cell stage. Aberrant pre-BCR signaling is considered as a key factor for B-cell precursor acute lymphoblastic leukemia (BCP-ALL) development. BCP-ALL are believed to be arrested at the pre-BCR checkpoint independent of pre-BCR expression. However, the cellular stage at which BCP-ALL are arrested and whether this relates to expression of the pre-BCR components (<i>IGHM</i>, <i>IGLL1</i> and <i>VPREB1)</i> is still unclear. Here, we show differential protein expression and copy number variation (CNV) patterns of the pre-BCR components in pediatric BCP-ALL. Moreover, analyzing six BCP-ALL data sets (n = 733), we demonstrate that <i>TCF3-PBX1</i> ALL express high levels of <i>IGHM</i>, <i>IGLL1</i> and <i>VPREB1</i>, and are arrested at the pre-B stage. By contrast, <i>ETV6-RUNX1</i> ALL express low levels of <i>IGHM</i> or <i>VPREB1</i>, and are arrested at the pro-B stage. Irrespective of subtype, ALL with high levels of <i>IGHM</i>, <i>IGLL1</i> and <i>VPREB1</i> are arrested at the pre-B stage and correlate with good prognosis in high-risk pediatric BCP-ALL (n = 207). Our findings suggest that BCP-ALL are arrested at different cellular stages, which relates to the expression pattern of the pre-BCR components that could serve as prognostic markers for high-risk pediatric BCP-ALL patients.</p></div

Increased citrullination and expression of peptidylarginine deiminases independently of P. gingivalis and A. actinomycetemcomitans in gingival tissue of patients with periodontitis

BACKGROUND: A relationship between rheumatoid arthritis (RA) and periodontitis has been suggested from findings that individuals with RA are prone to have advanced periodontitis and vice versa. In search of possible common pathogenetic features of these two diseases, we investigated the presence of citrullinated proteins and expression of endogenous peptidylarginine deiminases (PAD2 and PAD4), in periodontal tissue of individuals with periodontitis and healthy controls, in relation to the periodontal pathogens Porphyromonas gingivalis (P. gingivalis) and Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans), producing leukotoxin as virulence factor. These two oral bacteria have been suggested to be linked to anti-citrullinated protein antibodies in patients with RA. METHODS: Gingival tissue biopsies were obtained from 15 patients with periodontitis and 15 individuals without periodontal disease. Presence of CD3-positive lymphocytes, citrullinated proteins, PAD2, PAD4, P. gingivalis as well as A. actinomycetemcomitans and Mannheimia haemolytica produced leukotoxins were analysed by immunohistochemistry, followed by triple-blind semi-quantitative analysis. Mann-Whitney and Fisher's exact tests were used to analyse differences between groups. PADI2 and PADI4 mRNA levels were assessed by RT-qPCR and analysed using Wilcoxon signed rank test. RESULTS: Increased staining of citrullinated proteins was observed in gingival connective tissue from subjects with periodontitis (80%, 12/15) compared to healthy gingival tissue (27%, 4/15), whereas no differences were observed in gingival epithelium. There was also an increased staining of the citrullinating enzymes PAD2 and PAD4 in gingival connective tissue of patients with periodontitis whereas similar levels of PAD2 and PAD4 were observed in the gingival epithelium of the two groups. Similarly, the mRNA levels of PADI2 and PADI4 were also increased in the gingival tissue of patients with periodontitis compared to healthy controls. Furthermore, presence of P. gingivalis and leukotoxins was comparable in both epithelium and connective tissue, from the different investigated individuals with and without periodontitis, and there were no correlations between the presence of periodontal pathogens and the expression of citrullinated proteins or PAD enzymes. CONCLUSION: Chronic gingival inflammation is associated with increased local citrullination and PAD2 and PAD4 expression in periodontitis. The increased citrullination and PAD2 and PAD4 expression in periodontitis were, however, independent of the presence of periodontal pathogen P. gingivalis and A. actinomycetemcomitans leukotoxin

The pre-BCR components show distinct mRNA expression patterns in BCP-ALL and normal B cells.

<p>(A) Heat map shows the expression patterns of the pre-BCR components in childhood BCP-ALL (GSE12995). (B) The graphs show the expression patterns of <i>IGHM</i>, <i>VPREB1</i> and <i>IGLL1</i> in 733 BCP-ALL patient samples from six cohorts (GSE12995, Blood2003, GSE177031, GSE26281, GSE13425 and GSE47051). All patient samples were evenly divided into quartiles according to the expression level of <i>IGHM</i>, <i>VPREB1</i> or <i>IGLL1</i>. (C) Heat map shows the expression patterns of the pre-BCR components in a normal B-cell data set (GSE45460). HH, High Hyperdiploid; iB, immature B cells.</p

The expression pattern of pre-BCR components associates with the clinical outcomes in high-risk patient group.

<p>(A) The percentage of MRD 29 positive patients was compared among clusters 1–4 using Fisher's exact test. MRD 29: minimal residual disease at day 29. (B-E) The Kaplan-Meier Log rank Survival analysis was performed to compare event free survival (B and C) and overall survival (D and E) of the 207 high-risk patients. Survival probabilities between patients within different clusters (1–4) are shown.</p

GSEA reveals molecular signature similarities between the <i>IGHM</i>⁺<i>IGLL1</i>⁺<i>VPREB1</i>⁺ BCP-ALL and normal pre-B cells.

<p>(A) The BCP-ALL in data set GSE11877, including 207 high-risk patient samples, were classified into four clusters according the expression levels of <i>IGHM</i>, <i>IGLL1</i> and <i>VPREB1</i>: <i>IGHM+IGLL1+VPREB1+</i> (Cluster 1), <i>IGHM+IGLL1+/-VPREB1+/-</i> (not including <i>IGHM+IGLL1+VPREB1+</i>) (Cluster 2), <i>IGHM-IGLL1+/-VPREB1+/-</i> (not including <i>IGHM-IGLL1-VPREB1-</i>) (Cluster 3) and <i>IGHM-IGLL1-VPREB1-</i> (Cluster 4). (B) Genes highly expressed in pre-B cells are enriched in Cluster 1. Left: The pre-B signature was identified using supervised comparison in data set GSE45460. Middle: Heat map of the pre-B signature in Cluster 1 and the remaining ALL. Right: Enrichment plot shows the enrichment of pre-B signature in the Cluster 1. (C) Genes highly expressed in Cluster 1 are enriched in pre-B cells. Left: The top 400 genes highly expressed in Cluster 1 (Cluster 1 signature) were identified using supervised comparison. Middle: Heat map of the Cluster 1 signature in healthy pre-B cells. Right: Enrichment plot shows the enrichment of Cluster 1 signature in healthy pre-B cells. (D) Pie-chart shows the distribution of genetic subtypes in clusters 1–4. (E) Genes highly expressed in pre-B cells are enriched in the <i>TCF3-PBX1</i> ALL from Cluster 1 but not that from other clusters. Left: Heat map of the pre-B signature in the <i>TCF3-PBX1</i> ALL from Cluster1. Right: Enrichment plot shows the enrichment of pre-B signature in the <i>TCF3-PBX1</i> ALL from Cluster 1. (F) Genes highly expressed in pre-B cells are enriched in Cluster 1 without <i>TCF3-PBX1</i>. Left: Heat map of the pre-B signature in the Cluster1 without <i>TCF3-PBX1</i>. Right: Enrichment plot shows the enrichment of pre-B signature in Cluster 1 without the <i>TCF3-PBX1</i> ALL.</p