Marc Bonin

Upregulation of Immunoproteasome Subunits in Myositis Indicates Active Inflammation with Involvement of Antigen Presenting Cells, CD8 T-Cells and IFNγ

Objective In idiopathic inflammatory myopathies (IIM) infiltration of immune cells into muscle and upregulation of MHC-I expression implies increased antigen presentation and involvement of the proteasome system. To decipher the role of immunoproteasomes in myositis, we investigated individual cell types and muscle tissues and focused on possible immune triggers. Methods Expression of constitutive (PSMB5, -6, -7) and corresponding immunoproteasomal subunits (PSMB8, -9, -10) was analyzed by real-time RT-PCR in muscle biopsies and sorted peripheral blood cells of patients with IIM, non-inflammatory myopathies (NIM) and healthy donors (HD). Protein analysis in muscle biopsies was performed by western blot. Affymetrix HG-U133 platform derived transcriptome data from biopsies of different muscle diseases and from immune cell types as well as monocyte stimulation experiments were used for validation, coregulation and coexpression analyses. Results Real-time RT-PCR revealed significantly increased expression of immunoproteasomal subunits (PSMB8/-9/-10) in DC, monocytes and CD8+ T-cells in IIM. In muscle biopsies, the immunosubunits were elevated in IIM compared to NIM and exceeded levels of matched blood samples. Proteins of PSMB8 and -9 were found only in IIM but not NIM muscle biopsies. Reanalysis of 78 myositis and 20 healthy muscle transcriptomes confirmed these results and revealed involvement of the antigen processing and presentation pathway. Comparison with reference profiles of sorted immune cells and healthy muscle confirmed upregulation of PSMB8 and -9 in myositis biopsies beyond infiltration related changes. This upregulation correlated highest with STAT1, IRF1 and IFNγ expression. Elevation of T-cell specific transcripts in active IIM muscles was accompanied by increased expression of DC and monocyte marker genes and thus reflects the cell type specific involvement observed in peripheral blood. Conclusions Immunoproteasomes seem to indicate IIM activity and suggest that dominant involvement of antigen processing and presentation may qualify these diseases exemplarily for the evolving therapeutic concepts of immunoproteasome specific inhibition.

Monocyte alterations in rheumatoid arthritis are dominated by preterm release from bone marrow and prominent triggering in the joint

Objective Rheumatoid arthritis (RA) accompanies infiltration and activation of monocytes in inflamed joints. We investigated dominant alterations of RA monocytes in bone marrow (BM), blood and inflamed joints. Methods CD14+ cells from BM and peripheral blood (PB) of patients with RA and osteoarthritis (OA) were profiled with GeneChip microarrays. Detailed functional analysis was performed with reference transcriptomes of BM precursors, monocyte blood subsets, monocyte activation and mobilisation. Cytometric profiling determined monocyte subsets of CD14++CD16−, CD14++CD16+ and CD14+CD16+ cells in BM, PB and synovial fluid (SF) and ELISAs quantified the release of activation markers into SF and serum. Results Investigation of genes differentially expressed between RA and OA monocytes with reference transcriptomes revealed gene patterns of early myeloid precursors in RA-BM and late myeloid precursors along with reduced terminal differentiation to CD14+CD16+monocytes in RA-PB. Patterns associated with tumor necrosis factor/lipopolysaccharide (TNF/LPS) stimulation were weak and more pronounced in RA-PB than RA-BM. Cytometric phenotyping of cells in BM, blood and SF disclosed differences related to monocyte subsets and confirmed the reduced frequency of terminally differentiated CD14+CD16+monocytes in RA-PB. Monocyte activation in SF was characterised by the predominance of CD14++CD16++CD163+HLA-DR+ cells and elevated concentrations of sCD14, sCD163 and S100P. Conclusion Patterns of less mature and less differentiated RA-BM and RA-PB monocytes suggest increased turnover with accelerated monocytopoiesis, BM egress and migration into inflamed joints. Predominant activation in the joint indicates the action of local and primary stimuli, which may also promote adaptive immune triggering through monocytes, potentially leading to new diagnostic and therapeutic strategies.

Genomic stratification by expression of HLA-DRB4 alleles identifies differential innate and adaptive immune transcriptional patterns - A strategy to detect predictors of methotrexate response in early rheumatoid arthritis

Effective drug selection is the current challenge in rheumatoid arthritis (RA). Treatment failure may follow different pathomechanisms and therefore require investigation of molecularly defined subgroups. In this exploratory study, whole blood transcriptomes of 68 treatment-naïve early RA patients were analyzed before initiating MTX. Subgroups were defined by serologic and genetic markers. Response related signatures were interpreted using reference transcriptomes of various cell types, cytokine stimulated conditions and bone marrow precursors. HLA-DRB4-negative patients exhibited most distinctive transcriptional differences. Preponderance of transcripts associated with phagocytes and bone marrow activation indicated response and transcripts of T- and B-lymphocytes non-response. HLA-DRB4-positive patients were more heterogeneous, but also linked failure to increased adaptive immune response. RT-qPCR confirmed reliable candidate selection and independent samples of responders and non-responders the functional patterning. In summary, genomic stratification identified different molecular pathomechanisms in early RA and preponderance of innate but not adaptive immune activation suggested response to MTX therapy.

A6.07 Tissue- and cell-specific transcriptomes indicate systemic nature of ra and revealed combinations of protein biomarkers relevant for disease characterisation in serum

Background and objectives Clinical signs and symptoms, radiographic changes and routine laboratory tests have indispensable roles in diagnosis of rheumatoid arthritis (RA). Nevertheless, a high degree of heterogeneity between RA patients and increasing options of treatment require the identification of objective criteria relevant for diagnosis and therapeutic stratification of patients. This study focused on global approaches in dissecting inflammation in RA including transcriptome analyses of synovial tissue, blood and bone marrow monocytes and proteome analyses of selected molecules in serum from long-lasting and early RA. Materials and methods Gene-expression profiling of synovial tissues, blood and bone marrow monocytes of RA and osteoarthritis (OA) patients were performed by Affymetrix microarrays. Based on transcriptome data, 28 molecules were selected for protein analyses by ELISA and multiplex immunoassays in sera from patients with long-lasting RA (n = 17) and OA (n = 16), early RA (n = 10) and healthy donors (n = 14). Results Transcriptome analyses of synovial tissues from RA and OA patients showed the most prominent differences between these two diseases and identified more than 1000 differentially expressed genes. More subtle differences were disclosed by gene-expression profiling of blood and bone marrow monocytes from RA and OA with 300 and 150 differentially expressed genes, respectively. From RA tissue- and cell-specific transcriptomes 28 genes were selected for protein analyses in serum from RA and OA patients including: chemokines (CXCL13, CCL18), adhesion molecules (VCAM1, ICAM1, E- and P-Selectins), enzymes (MMP3, A1AT), alarmins (S100P and S1008/9) and the soluble form of cell surface molecules (CD14, CD163). Out of 28 markers only 16 reached statistical significance to discriminate long-lasting RA from OA. A combination of 5 markers was able to correctly classify long-lasting RA. However, the same combination of markers identified only one-third of early RA patients. Conclusions Tissue- and cell-specific transcriptomes demonstrated the systemic nature of RA. Proteome analyses of serum from long-lasting RA patients confirmed transcriptome data and showed that molecular patterns determined by the combination of inflammatory and cell-specific markers are required for disease stratification. In early RA transcriptome data outperformed proteome data suggesting that focus on transcriptional alterations is more sensitive approach for disease management of early RA.

Gene expression profiling of human bronchial epithelial cells exposed to fine particulate matter (PM 2.5 ) from biomass combustion

ABSTRACT One‐third of the worlds population relies on solid biomass fuels for domestic energy demands. In contrast to industrial or traffic related emissions, only a limited number of studies focus on the adverse health effects of particulate matter (PM) from biomass combustion. We conducted Affymetrix Human Genome U133 Plus 2.0 arrays, bioinformatic analysis, qRT‐PCR and immunoblotting to determine the molecular impact of fuelwood‐derived PM2.5 on lung epithelial BEAS‐2B cells. In the presence of PM2.5 175 differentially regulated genes were identified. Gene ontology (GO), pathway and functional enrichment analysis allocated these genes to cellular development, metabolism, inflammation, cancer and the immune system. Analysis of enriched transcription factor binding sites extracted 15 PM2.5 responsive transcription factors, including the polycyclic aromatic hydrocarbon (PAH)‐activated aryl hydrocarbon receptor (AhR). Accordingly, a complex mixture of PAHs was detected in the PM2.5 fraction using APLI and AhR‐inhibitors reduced the up‐regulation of CYP1A1, EREG, GREM1, IL1B and IL6, indicating that PAHs are involved in PM2.5 specific gene deregulation. We also provide evidence, that HIF‐1&agr; might be responsive to PM2.5. To analyze the impact of microbial infections, PM2.5 predisposed cells were incubated with LPS or dsRNA. We identified 40 LPS and 380 dsRNA specific genes in PM2.5 predisposed cells. GO allocated these genes with chemokine dependent and inflammatory pathways, viral responses and xenobiotic metabolism. A disease ontology allocated lung and lung associated diseases to PM2.5 exposed cells. In some cases LPS or dsRNA increased significance of probability of diseases. Altogether our studies enhance our knowledge on the mechanism promoting harmful effects of PM. HIGHLIGHTSDifferentially gene expression in BEAS‐2B cells by PM2.5 from biomass combustionInvolvement of genes in metabolism, inflammation, cancer and the immune systemIdentification of PAHs by APLI and involvement of AhR and HIF‐1alphaAllocation of lung and lung associated diseases to PM2.5 by disease ontologyResponses to microbial pathogens ‐ LPS and dsRNA ‐ in PM2.5 pre‐exposed cells

SAT0249 Reduction of monocyte activation by bowel cleanse and one week fasting suggests permanent pathogenetic triggering from the gut in rheumatoid arthritis

Background: Fasting can improve clinical disease activity in rheumatoid arthritis (RA) [1], but mechanism involved are not clear. Recently, we demonstrated that monocytes in RA express transcriptome patterns of increased myelopoiesis, premature egress from bone marrow and reduced circulation time as indicators of permanent activation of the innate immune response [2]. Objectives: We investigated the influence of bowel cleanse and fasting on monocyte subpopulations in the blood to determine the extent of microbiota and gut immunity related triggering of chronic inflammation in RA. Methods: RA patients (n=22) and controls (n=12, metabolic syndrome), who presented for fasting according to the Buchinger procedure (bowel cleanse with colonoscopy fluid), were analyzed for DAS28, CrP, differential blood count and high resolution cytometric phenotyping at baseline, day 3, day 7 (end of fasting) and day 10. ImmunoClust was applied for automated cell clustering [3]. Results: Disease activity was strikingly decreased after fasting in virtually all RA patients (DAS28 from 4.24 to 3.17, p<0.00005) with significant reduction already after 3 days (p<0.01). This was accompanied by a significant decline of CrP and ESR. Differential blood count revealed a slight decrease in total leukocytes and significant reduction of lymphocytes and eosinophils in RA. However, these blood changes were also observed but on a lower level in the metabolic controls. The most dominant and RA specific effect was a significant reduction of total monocytes when compared to RA baseline or to controls at day 10. Deep profiling of the monocyte compartment revealed reduced non-classical (CD14+CD16+) and intermediate (CD14++CD16+) monocytes prior to fasting in RA compared to controls and confirmed previous results [2]. Bowel cleanse and fasting induced a significant increase of these two monocyte subpopulations by absolute counts and even more by percentage of total monocytes. This indicates reduced recruitment to inflamed tissue and prolonged circulation with more cells differentiating from classical to non-classical monocytes in the blood [4]. The decrease of lymphocytes in RA patients after fasting was characterized by a dominant reduction of naive T-, B-cells and CD16- NK-cells along with a relative increase in memory lymphocytes and CD16+ NK-cells. These effects were also observed but less pronounced in controls. Conclusions: Bowel cleanse and fasting in RA induces a reduction of inflammation related to monocyte activation and turnover immediately within few days. Changes in the monocyte compartment were specific for RA compared to controls and dominated the immunological changes, suggesting that innate triggering mechanisms from gut and its microbiota are etiologically relevant in RA. References [1] Kjeldsen-Kragh J, et al. Lancet1991;338(8772):899. [2] Smiljanovic B, et al. Ann Rheum Dis2018;77(2):300. [3] Sörensen T, et al. Cytometry A2015;87(7):603. [4] Tak T, et al. Blood2017;130(12):1474. Acknowledgements: Technical assitance: Silvia Pade, Barbara Walewska Funding: German Federal Ministry of Education and Research grant ArthoMark (01EC1009A), Corona-Stiftung grant BioFast (S199/10063/2016). Disclosure of Interest: None declared

05.08 Increased turnover of monocytes in patients with rheumatoid arthritis identified by transcriptome and cytometric profiling

Background Targeting molecules involved in monocyte activation is an important treatment strategy for RA. In this study we aimed to determine monocyte maturation and activation from bone marrow (BM) via blood into synovial fluid (SF) by investigating monocytes transcriptomes and by cytometric profiling of classical (CD14++CD16-), intermediated (CD14++CD16+) and non-classical (CD14+CD16+) monocytes. Materials and methods CD14+ cells from BM and blood of RA and osteoarthritis (OA) patients were profiled with Affymetrix microarrays. A detailed functional analysis was performed with reference transcriptomes of BM precursors, monocyte blood subsets, monocyte activation and egress from BM induced by G-CSF (granulocyte colony-stimulating factor). Cytometric profiling of CD14, CD16, HLA-DR and CD163 expression were used to determine monocyte subsets and to follow their activation and differentiation in BM, blood and SF. Results Transcriptomes of RA-BM monocytes exhibited i) pronounce gene pattern of early myeloid precursors from BM and ii) weak gene pattern of late myeloid precursors from BM. Transcriptomes of RA blood monocytes demonstrated i) pattern of late myeloid precursors from BM and ii) reduced pattern of terminally differentiated CD14+CD16+ monocytes from blood. Cytometric profiling of BM, blood and SF monocytes in RA and OA showed that all three body compartments have their own distribution of monocyte subsets. BM was characterised with classical and intermediate subsets and both subsets showed decreased CD16 expression in RA when compared to OA. As expected, blood was characterised with three subsets, and RA blood showed decreased CD14 and HLA-DR expression on classical monocytes and reduced frequency of non-classical subset. In RA-SF, classical monocytes were absent, intermediate were most dominant and cell-phenotype with low CD16 expression but similar to non-classical monocytes was related to macrophages. Cell frequency of intermediate subset in SF positively correlated with inflammation (ESR; R>0.85) and showed the highest expression of HLA-DR, CD14, CD163. Conclusions Monocyte turnover is increased in RA and characterised with accelerated monocytopoiesis, faster BM egress and migration into inflamed joints. Permanent monocyte activation in the joint and their role in linking innate and adaptive immunity, which is targeted by biologics, emphasises their high diagnostic value and relevance for therapeutic stratification.

A6.37 The synovial tissue transcriptome reveals combinations of protein biomarkers for unambiguous identification of RA patients from synovial fluid and for quantification of disease activity in serum

Background and objectives A main challenge in disease-management of rheumatoid arthritis (RA) is to establish criteria for molecular disease activity and therapeutic stratification of patients. The commonly used disease activity score 28 (DAS28), autoantibodies, or the joint ultrasound score US7 only insufficiently characterise the diversity of chronic inflammation in RA. To address these challenges we analysed synovial tissue transcriptomes, synovial fluid (SF) and serum proteome from long-lasting RA patients, and serum proteome from early RA patients. Materials and methods Affymetrix HG-U133A transcriptomes were generated from synovial tissue biopsies of long-lasting RA (n = 10) and osteoarthritis (OA) (n = 10) patients. Multiplex-immunoassays and ELISA were used for marker validation at the protein level in SF and matched serum samples from long-lasting RA (n = 17) and OA (n = 16) patients. Prediction analysis for microarrays (PAM) identifies a minimum set of markers able to correctly classify RA. These markers were measured in serum from early RA patients (n = 10) before and after treatment with corticosteroids and methotrexate (MTX). Serum from healthy donors (ND; n = 14) was used as control. Results Comparison between synovial tissue transcriptomes from long-lasting RA and OA patients revealed differential expression of 1200 genes. 28 candidate genes were selected based on high differential expression and extracellular release. When validated at the protein level, 23 markers revealed elevated concentration in SF and 16 markers in serum from long-lasting RA patients. The PAM algorithm identified combination of 5 markers as the minimum set of molecules for correct classification of long-lasting RA compared to OA. An RA-specific molecular score was determined as the sum of the normalised expression values for these 5 markers, which correlated with DAS28 (r = 0.6854). Evaluation of top candidates in early RA patients revealed that only one-third of patients exhibited this molecular pattern. Treatment of these patients with corticosteroids and MTX resulted in changes of DAS28, where 7 patients were considered to be good and 3 moderate responders. Reduced serum concentration of the 5 markers was accompanied with changes of RA-molecular scores, which also correlated with the change of DAS28 (r = 0.582). Conclusion The synovial tissue transcriptome is an exceptional source for biomarker discovery. The tested synovial fluid proteome in long-lasting RA greatly resembles the transcriptome data for the secreted proteins. Serum from long-lasting RA also reflected the disease-specific characteristics of this multi-parameter pattern when compared to OA and ND. However, limited potential was observed when applied in early RA patients, suggesting that more sensitive approaches are needed.

A8.20 Bioconpages - comparison of DNA methylation and gene expression in different immune cells

Background and Objective Site specific methylation of DNA may contribute to the regulation of gene expression. Microarray based analysis of methylation refers to CpG site selected by a biostatistic algorithm without proof for actual involvement. To test for putatively effective CpG sites in immunity, we compared methylation with transcription in parallel in different sorted immune cell types. In order to perform primary analysis and to map corresponding results, software tools and an online database were developed. Materials and Methods Cells from 4 healthy donors were sorted by FACS technology for naive and activated/memory T-cells and B-cells, NK-cells, monocytes, and granulocytes. Genome wide DNA methylation was assessed using the HumanMethylation450 BeadChip platform and Genome Studio (Illumina). Transcriptomes were determined with Affymetrix HG-U133 Plus 2.0 GeneChips. A tool has been implemented in Java and R. In a first step the program checks the quality of each microarray and normalizes the data (Affymetrix & Illimunina). Afterwards the program imports and analyses the transcription and methylation data to determine high and low transcribed genes, match them with the status of DNA methylation and save the results as. txt and. jpg files. The tool will be provided on our homepage http://www.charite-bioinformatik.de. Results As an example, one of the performed analyses compared monocytes and T-cells. We found 4.624 genes, which showed differences in gene expression and 19.261 different DNA methylation sites. Between closer related cells like naive and activated/memory cells of the same lymphocyte subtype (CD4+ T-cells) the number decrease to 638 genes and 9.412 sites. Comparing monocytes against T-cells, corresponding changes of expression and methylation were found in only 629 of 1951 increased and in 279 of 2673 decreased expressed genes. These results and other comparisons will be presented in the BioConpages database. The database can be searched by GeneID and to retrieve information of the corresponding transcription signals and percentage of methylation in the different cell types. In general, when selecting genes differentially expressed in immune cells, only around 10% of all CpG sites annotated to a single gene were compatible with the differential expression pattern in immune cells. Conclusions This type of screening enables to preselect CpG sites putatively involved in differntiation of immune cells. Thus, corresponding information of transcription and methylation is indispensible to infer methylation associated gene regulation. This applies not only for microarray but also for sequencing approaches.

A8.21 Identification of geneexpression networks in different immunological states

Background and Objective Knowledge about gene networks is of great importance for analysis of transcriptome data. However, current tools mainly rely on information about direct molecular interactions between proteins, which is not directly connected to expression levels. These differences between transcriptome based perception of biological information and tools for network analysis are the main reason for difficulties in functional interpretation. Therefore, we started to use transcriptome data of biologically well-defined states to define functional markers and signatures as tools for future analysis. Materials and Methods GeneChip HG-U133 Plus 2.0 transcriptomes from highly purified blood cell types (granulocytes, monocytes, CD4+ and CD8+ T-cell, B-cells, NK-cells) as well as from monocyte stimulation with LPS, TNF and type 1 IFN were selected from the BioRetis database (www.bioretis.de). Correlations of expression between all probesets were calculated to filter for co-regulation Correlation matrices were calculated, clustered and displayed in heat maps The web-platform was constructed based on Ruby on Rails to provide a framework for analysis and storage of data The database and the correlation-algorithm will be provided on our homepage http://www.charite-bioinformatik.de. Results Initially, correlation matrices were determined for each individual stimulation condition and its control. Stepwise combination of the three different conditions for calculation of correlation coefficients revealed a reduction of the correlation network and a reduction of overlap between the networks. This indicates increasing functional specificity of the identified candidates. All of the typical previously published IFN related genes were identified and thus confirmed our strategy. In a similar way, cell type specific co-expression networks were determined. Additional filtering for high signal intensity provides candidates for sensitive detection of the function related patterns even in highly diluted conditions. These marker panels are currently tested for detection and quantification of functional signatures in biopsies of inflamed tissue. Conclusions Correlating transcription between genes in well-defined biological states identifies function-related markers and signatures. Depending on the type of function, appropriate conditions have to be selected.