Sporadic occurrence of non-diagnosed IgG4-related disease in lymphoma patients with a previous Sjögren’s syndrome diagnosis

<p><b>Background:</b> IgG4-related disease (IgG4-RD) is a recently recognized fibro-inflammatory disorder, which may affect many organs, and often comes to clinical attention due to tumor-like organ swelling or is identified incidentally by specific biopsy findings. Typical histopathology of IgG4-RD is lymphoplasmacytic infiltration rich in IgG4 + plasma cells (PCs), storiform fibrosis, and obliterative phlebitis. Patients with sicca symptoms can be misdiagnosed as primary Sjögren’s syndrome (pSS) instead of IgG4-RD because of clinical and histopathological similarities. Moreover, an association with lymphoma development is described in both diseases. This study investigated signs of IgG4-RD in a population-based cohort of patients diagnosed with pSS complicated by lymphoma.</p> <p><b>Methods:</b> Patients with pSS and lymphoma diagnoses and available lymphoma specimens were identified by linkage with the Swedish Patient Register 1964–2007 and the Cancer Register 1990–2007 (n = 79). Clinical data and lymphomas were reviewed and the diagnoses evaluated. All lymphoma tissues and available minor salivary gland biopsies (n = 11) were immunostained for IgG4 + PCs and evaluated for other histopathological signs of IgG4-RD. In a case with specific findings of IgG4-RD, other available tissue specimens of the same patient were investigated for IgG4-RD.</p> <p><b>Results:</b> Only one patient of 79 (1.3%) had >10 IgG4 + PCs/high power field (HPF) in the lymphoma tissue, an unspecified low-grade B-cell lymphoma localized in the submandibular gland. This patient also had other histopathological features of IgG4-RD in the lymphoma and a surgical lung biopsy taken five years before lymphoma diagnosis and, therefore, fulfilled the criteria for IgG4-RD. Occasional IgG4 + PCs (<10/HPF) without signs of IgG4-RD were observed in another six lymphomas. No IgG4 + PCs were identified in the minor salivary gland biopsies.</p> <p><b>Conclusion:</b> Histopathological findings of IgG4-RD may co-exist with low malignant B-cell lymphoma in patients with initially suspected pSS and may be associated with an underlying IgG4-RD.</p

Multi-modal single cell sequencing of B cells in primary Sjögren’s Syndrome (processed VDJ data).

Meta data record for processed VDJ data from the publication "Multi-modal single cell sequencing of B cells in primary Sjögren’s Syndrome". Abstract: Primary Sjögren’s syndrome (pSS) is an autoimmune disease characterized by lymphocytic infiltration in the salivary and lacrimal glands, B cell activation, SSA/SSB autoantibodies and an increased risk of B cell lymphoma. By generating sorted B cell single-cell gene expression and BCR libraries from 24 pSS patients stratified by SSA/SSB antibodies and four healthy controls, we defined 16 B cell subtypes. Interferon response genes were upregulated in pSS across all B cell subtypes, with the highest levels in pSS with both SSAB antibodies. The SSAB group showed a higher proportion of naïve B cells and lower proportion of memory B cells compared with controls. Memory B cells from SSAB patients were not class switched and expressed unmutated VDJ sequences. IGHV1-69 repertoire frequencies were higher in pSS patients than controls and 1287 clonotypes were unique for pSS. The present study describes molecular differences which may enable stratification of pSS patients at improved resolution. Repository content: 10X Genomics 5' VDJ (v1.1) BCR data from B cells from  Primary Sjögren's Syndrome (pSS) patients and healthy controls. Output from cellranger (5.0.1) for all samples where targeted VDJ libraries were successfully created and sequenced (23/24 samples). Files to be used as input to, for instance,  the immcantation workflow or the Bioconductor R package scRepertoire. Data is available upon reasonable request. Processed data files included for each sample: *_filtered_contig_annotations.csv *_filtered_contig.fasta Command used to generate the files: cellranger vdj \   --id="${sample}B" \   --sample=${bcrsamples} \   --fastqs=${fqdir} \   --reference="$CELLRANGER_VDJ_DATA/refdata-cellranger-vdj-GRCh38-alts-ensembl-5.0.0" \   --localcores=16 \   --localmem=112 List of files: C001_B_filtered_contig.fasta 3.1M C001_B_filtered_contig_annotations.csv 1.0M C002_B_filtered_contig.fasta 9.8M C002_B_filtered_contig_annotations.csv 3.2M C003_B_filtered_contig.fasta 9.8M C003_B_filtered_contig_annotations.csv 3.2M C004_B_filtered_contig.fasta 12M C004_B_filtered_contig_annotations.csv 3.8M P001_B_filtered_contig.fasta 7.4M P001_B_filtered_contig_annotations.csv 2.5M P002_B_filtered_contig.fasta 9.1M P002_B_filtered_contig_annotations.csv 3.1M P003_B_filtered_contig.fasta 3.7M P003_B_filtered_contig_annotations.csv 1.2M P004_B_filtered_contig.fasta 11M P004_B_filtered_contig_annotations.csv 3.6M P005_B_filtered_contig.fasta 10M P005_B_filtered_contig_annotations.csv 3.4M P006_B_filtered_contig.fasta 13M P006_B_filtered_contig_annotations.csv 4.4M P007a_B_filtered_contig.fasta 4.9M P007a_B_filtered_contig_annotations.csv 1.6M P007b_B_filtered_contig.fasta 8.0M P007b_B_filtered_contig_annotations.csv 2.7M P008a_B_filtered_contig.fasta 8.8M P008a_B_filtered_contig_annotations.csv 2.8M P008b_B_filtered_contig.fasta 12M P008b_B_filtered_contig_annotations.csv 3.8M P009_B_filtered_contig.fasta 7.5M P009_B_filtered_contig_annotations.csv 2.5M P010_B_filtered_contig.fasta 15M P010_B_filtered_contig_annotations.csv 5.0M P011_B_filtered_contig.fasta 9.7M P011_B_filtered_contig_annotations.csv 3.3M P012_B_filtered_contig.fasta 12M P012_B_filtered_contig_annotations.csv 4.1M P013_B_filtered_contig.fasta 9.1M P013_B_filtered_contig_annotations.csv 3.0M P014_B_filtered_contig.fasta 11M P014_B_filtered_contig_annotations.csv 3.8M P015_B_filtered_contig.fasta 11M P015_B_filtered_contig_annotations.csv 3.7M P016_B_filtered_contig.fasta 13M P016_B_filtered_contig_annotations.csv 4.2M P017_B_filtered_contig.fasta 7.2M P017_B_filtered_contig_annotations.csv 2.4M P018_B_filtered_contig.fasta 15M P018_B_filtered_contig_annotations.csv 4.9M P019_B_filtered_contig.fasta 20M P019_B_filtered_contig_annotations.csv 6.5M P020_B_filtered_contig.fasta 10M P020_B_filtered_contig_annotations.csv 3.5M P021_B_filtered_contig.fasta 7.8M P021_B_filtered_contig_annotations.csv 2.6M P022_B_filtered_contig.fasta 9.4M P022_B_filtered_contig_annotations.csv 3.1M P023_B_filtered_contig.fasta 7.2M P023_B_filtered_contig_annotations.csv 2.4M _CHECKSUMS.txt 3.8K _README.txt 637B</p

Association of 5676 SNPs to lupus nephritis in case-control analysis of 195 patients.

<p>Results from the association analysis of 5676 SNPs in 195 patients with lupus nephritis and 512 healthy controls in cohort I. The negative logarithm of the p-value is plotted against the chromosomal location. The line represent associations with p<0.001 and the nine genes associated with p<0.001 are indicated. The <i>STAT4</i> SNPs rs11889341, rs7574865, rs7568275 and rs7582694 have an r² = 0.98 calculated from the 512 controls.</p

Case-control association analysis in cohort I^a. The best SNPs in genes associated with lupus nephritis with p <0.001 are shown.

<p>The best SNP in each gene is shown and for STAT4, IRF5, TNIP1 and BLK also the SNPs used for meta-analysis, marked in bold; STAT4 rs11889341, rs7582694 r² = 0.98, IRF5 rs2070197, rs10488631 r²≈1.00, TNIP1 rs7708392, rs6889239 r²≈1.00 and BLK rs922483, rs13277113 r² = 0.87 calculated in 512 Swedish controls. OR: odds ratio, CI: confidence interval, NA: not available.</p><p>^a Uppsala, Stockholm and Lund, Sweden, n = 567 SLE cases, n = 512 controls</p><p>^b WHO class III or IV on renal biopsy, according to the 1995 WHO classification system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-Churg1" target="_blank">[2]</a>.</p><p>^c Glomerular filtration rate <30 mL/min/1.73 m²<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-KDOQI1" target="_blank">[22]</a>.</p><p>^d rs3135394 has an r² = 0.87 with the HLA*DR3 (DRB1*0301) allele <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-Gateva1" target="_blank">[6]</a>.</p><p>^e Unadjusted p-value and OR for differences in allele frequencies between patients and controls.</p><p>^f Adjusted p-value and OR from logistic regression analysis including age and gender as covariates. Number of cases and controls; lupus nephritis, n = 194, proliferative nephritis, n = 91, severe renal insufficiency, n = 28, SLE, n = 566, controls, n = 504.</p

Patient basic characteristics.

<p>^a Uppsala, Stockholm and Lund, Sweden</p><p>^b Linköping, Sweden</p><p>^c Comparison between SLE with lupus nephritis and SLE without lupus nephritis. Frequencies compared with Chi square test and continuous variables with Mann-Whitney U-test.</p><p>^d WHO class III or IV on renal biopsy, according to the 1995 WHO classification system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-Churg1" target="_blank">[2]</a>. Biopsy data was not available from all patients.</p><p>^e Glomerular filtration rate <30 mL/min/1.73 m²<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-KDOQI1" target="_blank">[22]</a>.</p

Case-only meta-analysis of cohort I^a and cohort II^b in a total of 712 SLE cases.

<p>^a Uppsala, Stockholm, Lund, n = 567 cases.</p><p>^b Linköping, n = 145 cases.</p><p>^c WHO class III or IV on renal biopsy, according to the 1995 WHO classification system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-Churg1" target="_blank">[2]</a>.</p><p>^d Glomerular filtration rate <30 mL/min/1.73 m²<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-KDOQI1" target="_blank">[22]</a>.</p><p>^e Unadjusted p-value and OR for difference in allele frequencies between patients with (n = 230) and without (n = 482) lupus nephritis.</p><p>^f Unadjusted p-value and OR for difference in allele frequencies between patients with proliferative nephritis (n = 112) and SLE without proliferative nephritis (n =  548; SLE without LN, n = 482 and LN other than proliferative, n = 66).</p><p>^g Unadjusted p-value and OR for difference in allele frequencies between patients with severe renal insufficiency (n = 31) and SLE without severe renal insufficiency (n = 676; SLE without LN, n = 482 and LN without severe renal insufficiency at follow-up, n = 194). P-value remaining significant after Bonferroni correction for 4 tested SNPs is in italic.</p><p>^h Adjusted p-value and OR from meta-analysis of logistic regression results including disease duration and gender as covariates. Number of cases in each analysis; LN/non-LN: 225/481, proliferative nephritis/non-proliferative nephritis: 109/545, severe renal insufficiency/non-severe renal insufficiency: 31/670.</p

Case-control meta-analysis of cohort I^a and cohort II^b in a total of 712 SLE cases and 1131 controls.

<p>^a Uppsala, Stockholm, Lund, n = 567 cases, n = 512 controls.</p><p>^b Linköping, n = 145 cases, n = 619 controls.</p><p>^c WHO class III or IV on renal biopsy, according to the 1995 WHO classification system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-Churg1" target="_blank">[2]</a>. Biopsies available in 178 patients.</p><p>^d Glomerular filtration rate <30 mL/min/1.73 m²<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084450#pone.0084450-KDOQI1" target="_blank">[22]</a>. Data available for 225 patients.</p><p>^e Unadjusted combined p-value and OR calculated using Cochran-Mantel Haenszel chi-square test. P-values remaining significant after Bonferroni correction for 4 tested SNPs are in italic.</p><p>^f Adjusted p-value and OR from meta-analysis of logistic regression results including age and gender as covariates. Number of cases and controls; lupus nephritis, n = 229, proliferative nephritis, n = 111, severe renal insufficiency, n = 31, SLE, n = 711, controls, n = 960.</p

Functional characterizations of <i>OAS1</i> isoforms.

<p>(A) Protein expression of OAS1 isoforms was evaluated in EBV-transformed B cells from SS patients (four independent samples from each genotype group) using anti-OAS1 antibody targeting the shared epitope of all the isoforms. The stimulated cells were treated with universal type I IFN (1500U/ml) for 24hrs. The p44 isoform was not detectable using western-blot due to its low expression. The right panel shows quantified band intensity normalized to the GAPDH in each sample. (B) The transcript levels of each <i>OAS1</i> isoform from the same sets of cells described above were determined using real-time PCR. Consistent with the RNA-seq results, the SS-associated risk allele A of rs10774671 was correlated with decreased levels of p46 and increased expression of the p42, p48, and p44 isoforms (significance levels are shown at the bottom). The transcript levels of all the isoforms significantly increased after IFN stimulation (two-tailed <i>t</i> test); however, only p46 had increased protein production after IFN stimulation. (Significance level: ** <i>P</i><0.01; *** <i>P</i><0.001) (C) Individual isoforms of <i>OAS1</i> tagged with Xpress epitope were cloned and transfected into HEK 293T cells for 48hrs. The p48 and p44 isoforms had impaired protein expression compared to p46 and p42, although their transcript levels were equivalent as determined by real-time PCR (n = 4; normalized to <i>HMBS</i>). (D) The full-length and truncated <i>OAS1</i> p48 and p44 isoforms were cloned into HEK 293T cells. Western-blot indicated the lack of expression of the full-length p48 and p44 isoforms, whereas the truncation of both isoform transcripts (T2 and T4) was able to restore protein expression. (E) The 3' alternatively spliced terminus of different <i>OAS1</i> isoforms were linked to the 3'-end of GFP to observe their influence on GFP protein expression in HEK 293T cells. The 3'-terminus from the p48 and p44 isoforms resulted in decreased expression of GFP.</p

Differentially expressed transcripts between 115 anti-Ro/SSA positive SS cases and 56 controls identified through transcriptome profiling.

<p>(A) We identified 73 genes (represented by 83 probes on the heatmap) differentially expressed between anti-Ro/SSA positive SS cases and healthy controls (absolute FC >2 and <i>q</i><0.05). Among the differentially expressed genes, 57 were type I IFN-regulated genes (black bar on right) and formed an IFN signature where most genes were overexpressed in SS patients (yellow indicates overexpressed genes compared to controls). (B) The 57 differentially expressed type I IFN-regulated genes were re-clustered in anti-Ro/SSA positive SS cases using <i>k</i>-means (<i>k</i> = 3) algorithm and heterogeneity of the IFN signature levels in anti-Ro/SSA positive SS cases was observed.</p

Results of <i>cis</i>-eQTL analysis in <i>OAS1</i> region.

<p>(A) After imputation, 453 variants near <i>OAS1</i> were tested for association with <i>OAS1</i> transcript expression using linear regression. The association of each variant with the transcript level of <i>OAS1</i> (represented by 3 probes on the microarray; see B) are plotted based on the most significant -log₁₀(<i>P</i>_<i>eQTL</i>) values. We identified <i>cis</i>-eQTLs within and near <i>OAS1</i>, with the top association at rs10774671 (<i>P</i>_<i>eQTL</i> = 6.05×10⁻¹⁴). The variant rs10774671 was also the most significant genotyped SS-associated SNP in the genetic association analysis (<i>P</i>_<i>assoc</i> = 8.47×10⁻⁵; The top imputed SS-associated variant rs4767023 is also marked on the plot). The <i>r</i>² coded by colors indicating LD with rs10774671 are given in the figure. Variants above the dashed line were associated with <i>OAS1</i> transcript expression with <i>q</i><0.01. No eQTL was observed for <i>OAS2</i> or <i>OAS3</i>. (B) The genomic structures of the isoforms of <i>OAS1</i> (p46: NM_016816; p42: NM_002534; p48: NM_001032409; and p44, as described previously and identified in our RNA-seq analysis) are shown. The location of rs10774671 and the splicing consensus sequence AG in p46, p48, and p44 are indicated. One probe on the microarray specifically detects the p42 isoform (Probe 3). (C) The <i>cis</i>-eQTL analysis was performed through integration of the microarray expression data of <i>OAS1</i> with the genotype data of rs10774671. The SS-associated risk allele A of rs10774671 was associated with higher expression level of the p42 isoform as determined by Probe 3. The A allele was associated with lower expression of total <i>OAS1</i> as measured by Probe 1 and Probe 2. The <i>cis</i>-eQTL analysis results were determined using both a linear model and ANOVA. The mean value and the standard error of the mean (Mean±SEM) in each group are plotted in red.</p