28 research outputs found

    Genetic variations in A20 DUB domain provide a genetic link to citrullination and neutrophil extracellular traps in systemic lupus erythematosus

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    Objectives: Genetic variations in TNFAIP3 (A20) de-ubiquitinase (DUB) domain increase the risk of systemic lupus erythematosus (SLE) and rheumatoid arthritis. A20 is a negative regulator of NF-κB but the role of its DUB domain and related genetic variants remain unclear. We aimed to study the functional effects of A20 DUB-domain alterations in immune cells and understand its link to SLE pathogenesis. Methods: CRISPR/Cas9 was used to generate human U937 monocytes with A20 DUB-inactivating C103A knock-in (KI) mutation. Whole genome RNA-sequencing was used to identify differentially expressed genes between WT and C103A KI cells. Functional studies were performed in A20 C103A U937 cells and in immune cells from A20 C103A mice and genotyped healthy individuals with A20 DUB polymorphism rs2230926. Neutrophil extracellular trap (NET) formation was addressed ex vivo in neutrophils from A20 C103A mice and SLE-patients with rs2230926. Results: Genetic disruption of A20 DUB domain in human and murine myeloid cells did not give rise to enhanced NF-κB signalling. Instead, cells with C103A mutation or rs2230926 polymorphism presented an upregulated expression of PADI4, an enzyme regulating protein citrullination and NET formation, two key mechanisms in autoimmune pathology. A20 C103A cells exhibited enhanced protein citrullination and extracellular trap formation, which could be suppressed by selective PAD4 inhibition. Moreover, SLE-patients with rs2230926 showed increased NETs and increased frequency of autoantibodies to citrullinated epitopes. Conclusions: We propose that genetic alterations disrupting the A20 DUB domain mediate increased susceptibility to SLE through the upregulation of PADI4 with resultant protein citrullination and extracellular trap formation

    Unsupervised hidden Markov model for automatic analysis of expressed sequence tags

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    This thesis provides an in-depth analyze of expressed sequence tags (EST) that represent pieces of eukaryotic mRNA by using unsupervised hidden Markov model (HMM). ESTs are short nucleotide sequences that are used primarily for rapid identificationof new genes with potential coding regions (CDS). ESTs are made by sequencing on double-stranded cDNA and the synthesizedESTs are stored in digital form, usually in FASTA format. Since sequencing is often randomized and that parts of mRNA contain non-coding regions, some ESTs will not represent CDS.It is desired to remove these unwanted ESTs if the purpose is to identifygenes associated with CDS. Application of stochastic HMM allow identification of region contents in a EST. Softwares like ESTScanuse HMM in which a training of the HMM is done by supervised learning with annotated data. However, because there are not always annotated data at hand this thesis focus on the ability to train an HMM with unsupervised learning on data containing ESTs, both with and without CDS. But the data used for training is not annotated, i.e. the regions that an EST consists of are unknown. In this thesis a new HMM is introduced where the parameters of the HMM are in focus so that they are reasonablyconsistent with biologically important regionsof an mRNA such as the Kozak sequence, poly(A)-signals and poly(A)-tails to guide the training and decoding correctly with ESTs to proper statesin the HMM. Transition probabilities in the HMMhas been adapted so that it represents the mean length and distribution of the different regions in mRNA. Testing of the HMM's specificity and sensitivityhave been performed via BLAST by blasting each EST and compare the BLAST results with the HMM prediction results.A regression analysis test shows that the length of ESTs used when training the HMM is significantly important, the longer the better. The final resultsshows that it is possible to train an HMM with unsupervised machine learning but to be comparable to supervised machine learning as ESTScan, further expansion of the HMM is necessary such as frame-shift correction of ESTs byimproving the HMM's ability to choose correctly positioned start codons or nucleotides. Usually the false positive results are because of incorrectly positioned start codons leadingto too short CDS lengths. Since no frame-shift correction is implemented, short predicted CDS lengths are not acceptable and is hence not counted as coding regionsduring prediction. However, when there is a lack of supervised models then unsupervised HMM is a potential replacement with stable performance and able to be adapted forany eukaryotic organism

    Plasmacytoid dendritic cells and RNA-containing immune complexes drive expansion of peripheral B cell subsets with an SLE-like phenotype

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    Background Hyperactive B cells and a continuous interferon (IFN)-alpha production by plasmacytoid dendritic cells (pDCs) play a key role in systemic lupus erythematosus (SLE). We asked whether the interaction between B cells and pDCs stimulated with RNA-containing immune complexes affects peripheral B cell subsets. Methods B cells and pDCs were isolated from blood of healthy individuals and stimulated with immune complexes consisting of SLE-IgG and U1snRNP (RNA-IC). Expression of cell surface molecules as well as IL-6 and IL-10 production were determined by flow cytometry and immunoassays. Gene expression profiles were determined by a NanoString nCounter expression array. Results We found a remarkable increase of double negative CD27-IgD-B cells, from 7% within fresh CD19+B cells to 37% in the RNA-IC-stimulated co-cultures of B cells and pDCs, comparable to the frequency of double negative B cells in SLE patients. Gene expression analysis of the double negative CD27-IgD -and the CD27 + IgD-memory B cells revealed that twenty-one genes were differentially expressed between the two B cell subsets (>= 2-fold, p< 0.001). The, IL21R, IL4R, CCL4, CCL3, CD83 and the IKAROS Family Zinc Finger 2 (IKZ2) showed higher expression in the double negative CD27-IgD-B cells. Conclusion The interactions between B cells and pDCs together with RNA-containing IC led to an expansion of B cells with similar phenotype as seen in SLE, suggesting that the pDC-B cell crosstalk contributes to the autoimmune feed-forward loop in SLE

    Plasmacytoid dendritic cells and RNA-containing immune complexes increase the frequency of CD27<sup>-</sup>IgD<sup>-</sup> B cells.

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    <p>Flow cytometric analysis of peripheral blood B cells isolated from SLE patients (SLE) or from healthy individuals (HC). The CD19<sup>+</sup> B cells were stained without culturing (fresh) or either cultured alone or together with plasmacytoid dendritic cells (pDC) for 6 days, in absence or presence of RNA-containing immune complexes (RNA-IC). All cultures were supplemented with IL-3 and GM-CSF (Cyt). (A) Representative plots and gating strategy of B cells stained for the cell surface expression of CD27 and IgD, and categorized as: CD27<sup>+</sup>IgD<sup>-</sup> switched memory cells (SM), CD27<sup>hi</sup>IgD<sup>-</sup> plasmablasts (PC), CD27<sup>+</sup>IgD<sup>+</sup> non-switched memory cells (NSM), CD27<sup>-</sup>IgD<sup>+</sup> naive cells (N) and CD27<sup>-</sup>IgD<sup>-</sup> double negative B cells (DN). The plots show B cells fom healthy individuals stained without culturing (left plot), cultured in presence of IL-3/GM-CSF (middle plot), or stimulated with RNA-IC+ IL-3/GM-CSF (right plot). (B) The frequency of CD27<sup>-</sup>IgD<sup>-</sup> B cells in the total CD19<sup>+</sup> B cell population. (C) The frequency of CD27<sup>-</sup>IgD<sup>-</sup>CD95<sup>+</sup> B cells in the total CD19<sup>+</sup> B cell population. (B, C) Individual values (dots) and the mean (horizontal bars) are shown. * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001. Statistical analyses were performed by Mann Whitney test or Wilcoxon signed rank test.</p

    CD86 expression by B cells cultured with plasmacytoid dendritic cells and RNA-containing immune complexes.

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    <p>Flow cytometric analysis of CD86 expression by (A) CD19+ B cells and (B) subsets of B cells. A) The B cells were analyzed without culturing (fresh) or after culturing for 6 days with IL-3/GM-CSF (Cyt), in absence or presence of RNA-containing immune complexes (RNA-IC) and plasmacytoid dendritic cells (pDCs). (B) The CD86 expression was further analyzed on B cell subsets characterized as switched memory cells (CD27<sup>+</sup>IgD<sup>-</sup>), non-switched memory (CD27<sup>+</sup>IgD<sup>+</sup>), double negative (CD27<sup>-</sup>IgD<sup>-</sup>) or naïve (CD27<sup>-</sup>IgD<sup>+</sup>) B cells. The median fluorescence intensity (MFI) of CD86 expression is based on (A) 9 and (B) 4–6 individual donors, respectively. * = p<0.05, ** = p<0.01. Statistical analyses were performed by Wilcoxon signed rank test.</p

    Frequency of switched memory and naïve B cells in presence of plasmacytoid dendritic cells and RNA-containing immune complexes.

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    <p>Flow cytometric analysis of B cells, without culturing (fresh) or stimulated for 6 days with IL-3/GM-CSF (Cyt) in absence or presence of RNA-containing immune complexes (RNA-IC). The CD19<sup>+</sup> B cells were either stimulated alone or in co-cultures with plasmacytoid dendritic cells (pDC). (A) The percentage of CD27<sup>+</sup>IgD<sup>-</sup> isotype switched memory (SM) B cells. (B) The percentage of CD27<sup>-</sup>IgD<sup>+</sup> naïve B cells. Individual values (dots) and mean values (horizontal bars) are shown * = p<0.05, ** = p<0.01. Statistical analyses were performed by Friedman test.</p

    PD-L1 and IDO1 are potential targets for treatment in patients with primary diffuse large B-cell lymphoma of the CNS

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    Background Programmed cell death 1 (PD-1) and its ligands PD-L1 and PD-L2, as well as Indoleamine 2,3-deoxygenase (IDO1) can be expressed both by tumor and microenvironmental cells and are crucial for tumor immune escape. We aimed to evaluate the role of PD-1, its ligands and IDO1 in a cohort of patients with primary diffuse large B-cell lymphoma of the CNS (PCNSL). Material and methods Tissue microarrays (TMAs) were constructed in 45 PCNSL cases. RNA extraction from whole tissue sections and RNA sequencing were successfully performed in 33 cases. Immunohistochemical stainings for PD-1, PD-L1/paired box protein 5 (PAX-5), PD-L2/PAX-5 and IDO1, and Epstein-Barr virus encoding RNA (EBER) in situ hybridization were analyzed. Results High proportions of PD-L1 and PD-L2 positive tumor cells were observed in 11% and 9% of cases, respectively. High proportions of PD-L1 and PD-L2 positive leukocytes were observed in 55% and 51% of cases, respectively. RNA sequencing revealed that gene expression of IDO1 was high in patients with high proportion of PD-L1 positive leukocytes (p = .01). Protein expression of IDO1 in leukocytes was detected in 14/45 cases, in 79% of these cases a high proportion of PD-L1 positive leukocytes was observed. Gene expression of IDO1 was high in EBER-positive cases (p = .0009) and protein expression of IDO1 was detected in five of six EBER-positive cases. Conclusion Our study shows a significant association between gene and protein expression of IDO1 and protein expression of PD-L1 in the tumor microenvironment of PCNSL, possibly of importance for prediction of response to immunotherapies.Title in Thesis: PD-L1 and IDO1 are important immunosuppressive molecules in primary diffuse large B-cell lymphoma of the CNS</p

    Twenty one genes differentially expressed by double negative CD27<sup>-</sup>IgD<sup>-</sup> B cells and switched memory CD27<sup>+</sup>IgD<sup>-</sup> B cells.

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    <p>Heat map of gene expression levels by double negative CD27<sup>-</sup>IgD<sup>-</sup> B cells (CD27<sup>neg</sup>IgD<sup>neg</sup>) and switched memory B cells (CD27<sup>pos</sup>IgD<sup>neg</sup>) (genes with >2-fold difference, p≤0.001). Plasmacytoid dendritic cells and CD19+ B cells were isolated from peripheral blood of healthy individuals (n = 10) and co-cultivated in the presence of RNA-containing immune complexes (RNA-IC) for four days. The CD27<sup>neg</sup>IgD<sup>neg</sup> and CD27<sup>pos</sup>IgD<sup>neg</sup> B cell subsets were isolated by flow cytometric cell sorting after staining with antibodies to CD123, CD19, CD27 and IgD. The mRNA expression of totally 614 genes were analyzed by nCounter gene expression platform and nSolverAnalysis Software 3.0. Each column represents a sample and rows represent differentially expressed genes. Median differences between the groups were analyzed by using Wilcoxon signed rank test.</p

    DNA methylation mapping identifies gene regulatory effects in patients with systemic lupus erythematosus

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    Objectives: Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with heterogeneous presentation and complex aetiology where DNA methylation changes are emerging as a contributing factor. In order to discover novel epigenetic associations and investigate their relationship to genetic risk for SLE, we analysed DNA methylation profiles in a large collection of patients with SLE and healthy individuals. Methods: DNA extracted from blood from 548 patients with SLE and 587 healthy controls were analysed on the Illumina HumanMethylation 450 k BeadChip, which targets 485 000 CpG sites across the genome. Single nucleotide polymorphism (SNP) genotype data for 196 524 SNPs on the Illumina ImmunoChip from the same individuals were utilised for methylation quantitative trait loci (cis-meQTLs) analyses. Results: We identified and replicated differentially methylated CpGs (DMCs) in SLE at 7245 CpG sites in the genome. The largest methylation differences were observed at type I interferon-regulated genes which exhibited decreased methylation in SLE. We mapped cis-meQTLs and identified genetic regulation of methylation levels at 466 of the DMCs in SLE. The meQTLs for DMCs in SLE were enriched for genetic association to SLE, and included seven SLE genome-wide association study (GWAS) loci: PTPRC (CD45), MHC-class III, UHRF1BP1, IRF5, IRF7, IKZF3 and UBE2L3. In addition, we observed association between genotype and variance of methylation at 20 DMCs in SLE, including at the HLA-DQB2 locus. Conclusions: Our results suggest that several of the genetic risk variants for SLE may exert their influence on the phenotype through alteration of DNA methylation levels at regulatory regions of target genes
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