Ursula Schulte-Wrede

The multifaceted balance of TNF-α and type I/II interferon responses in SLE and RA: how monocytes manage the impact of cytokines

Many cytokines are involved in the pathogenesis of autoimmune diseases and are recognized as relevant therapeutic targets to attenuate inflammation, such as tumor necrosis factor (TNF)-α in rheumatoid arthritis (RA) and interferon (IFN)-α/γ in systemic lupus erythematosus (SLE). To relate the transcriptional imprinting of cytokines in a cell type- and disease-specific manner, we generated gene expression profiles from peripheral monocytes of SLE and RA patients and compared them to in vitro-generated signatures induced by TNF-α, IFN-α2a, and IFN-γ. Monocytes from SLE and RA patients revealed disease-specific gene expression profiles. In vitro-generated signatures induced by IFN-α2a and IFN-γ showed similar profiles that only partially overlapped with those induced by TNF-α. Comparisons between disease-specific and in vitro-generated signatures identified cytokine-regulated genes in SLE and RA with qualitative and quantitative differences. The IFN responses in SLE and RA were found to be regulated in a STAT1-dependent and STAT1-independent manner, respectively. Similarly, genes recognized as TNF-α regulated were clearly distinguishable between RA and SLE patients. While the activity of SLE monocytes was mainly driven by IFN, the activity from RA monocytes showed a dominance of TNF-α that was characterized by STAT1 down-regulation. The responses to specific cytokines were revealed to be disease-dependent and reflected the interplay of cytokines within various inflammatory milieus. This study has demonstrated that monocytes from RA and SLE patients exhibit disease-specific gene expression profiles, which can be molecularly dissected when compared with in vitro-generated cytokine signatures. The results suggest that an assessment of cytokine-response status in monocytes may be helpful for improvement of diagnosis and selection of the best cytokine target for therapeutic intervention.

Cell-Specific Type I IFN Signatures in Autoimmunity and Viral Infection: What Makes the Difference?

Gene expression profiling of peripheral blood mononuclear cells (PBMCs) has revealed a crucial role for type I interferon (IFN) in the pathogenesis of systemic lupus erythematosus (SLE). However, it is unclear how particular leucocyte subsets contribute to the overall type I IFN signature of PBMCs and whole blood samples.Furthermore, a detailed analysis describing the differences in the IFN signature in autoimmune diseases from that observed after viral infection has not been performed to date. Therefore, in this study, the transcriptional responses in peripheral T helper cells (CD4+) and monocyte subsets (CD16− inflammatory and CD16+ resident monocytes) isolated from patients with SLE, healthy donors (ND) immunised with the yellow fever vaccine YFV-17Dand untreated controls were compared by global gene expression profiling.It was striking that all of the transcripts that were regulated in response to viral exposure were also found to be differentially regulated in SLE, albeit with markedly lower fold-change values. In addition to this common IFN signature, a pathogenic IFN-associated gene signature was detected in the CD4+ T cells and monocytes from the lupus patients. IL-10, IL-9 and IL-15-mediated JAK/STAT signalling was shown to be involved in the pathological amplification of IFN responses observed in SLE. Type I IFN signatures identified were successfully applied for the monitoring of interferon responses in PBMCs of an independent cohort of SLE patients and virus-infected individuals. Moreover, these cell-type specific gene signatures allowed a correct classification of PBMCs independent from their heterogenic cellular composition. In conclusion, our data show for the first time that monocytes and CD4 cells are sensitive biosensors to monitor type I interferon response signatures in autoimmunity and viral infection and how these transriptional responses are modulated in a cell- and disease-specific manner.

OMIP-034: Comprehensive immune phenotyping of human peripheral leukocytes by mass cytometry for monitoring immunomodulatory therapies.

Keywords: mass cytometry; panel design; mass-response characteristic; human blood leukocytes; immune phenotyping; immune monitoring; autoimmunity; rheumatism; systemic lupus erythematosus (SLE); biomarker and clinical trial

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.

A6.09 Nk cells as biosensors for responsiveness to etanercept in ankylosing spondylitis (Morbus Bechterew)

Background Therapeutic targeting of TNF is approved to be highly effective in ankylosing spondylitis patients who fail to respond to conventional anti-inflammatory drugs. However, only around two-thirds of anti-TNFa treated AS patients show an adequate response according to the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) independently from the biological used. Therefore, there is an urgent need for biomarkers which would aid in treatment choice and treatment outcome separating responders and non-responders to such expensive therapies and to avoid side effects induced by inefficient drugs. Thus, the aim of this study was to identify cell-based biosensors in peripheral blood by multiparametric flow cytometry that can be used for an early treatment stratification of AS patients. Methods A multiparametric flow cytometric approach, including 50 monoclonal antibodies combined to 10 staining cocktails, was applied to identify useful baseline predictors in 38 AS patients with active disease before treatment with the TNF blocker Adalimumab, Etanercept, Golimumab or Infliximab. BASDAI response criteria were used to determine therapeutic success after 1 to 6 month. Automated clustering of flow data, correlation analysis and receiver operator characteristics were accomplished to appoint auspicious candidate phenotypes. Results Out of multiple potentially significant parameters, which are involved both in acquired and adaptive immunity the NK cell compartment revealed most promising subsets that are predictive for a successful therapy response to Etanercept in AS. Correlation analyses showed an error-free classification of responders and non-responders for Etanercept but not for Adalimumab-treated patients. Conclusions This is the first study demonstrating that the composition of the NK cell compartment has predictive power with respect to classify AS patients whether they will respond or fail to the treatment by Etanercept. These results also shed some new light on the mode of action comparing TNF-alpha neutralising antibodies and soluble TNF alpha-receptors. In conclusion, these data make it reasonable to assume that monitoring of particular NK cell phenotypes can be used in terms of a companion diagnostic to realise the concept of personalised medicine in the field of rheumatology.

An explorative study on deep profiling of peripheral leukocytes to identify predictors for responsiveness to anti-tumour necrosis factor alpha therapies in ankylosing spondylitis: natural killer cells in focus

BackgroundTherapeutic targeting of tumour necrosis factor (TNF)-α is highly effective in ankylosing spondylitis (AS) patients. However, since one-third of anti-TNF-treated AS patients do not show an adequate clinical response there is an urgent need for new biomarkers that would aid clinicians in their decision-making to select appropriate therapeutic options. Thus, the aim of this explorative study was to identify cell-based biomarkers in peripheral blood that could be used for a pre-treatment stratification of AS patients.MethodsA high-dimensional, multi-parametric flow cytometric approach was applied to identify baseline predictors in 31 AS patients before treatment with the TNF blockers adalimumab (TNF-neutralisation) and etanercept (soluble TNF receptor).ResultsAs the major result, the frequencies of natural killer (NK) cells, and in particular CD8-positive (CD8+) NK cell subsets, were most predictive for therapeutic outcome in AS patients. While an inverse correlation between classical CD56+/CD16+ NK cells and reduction of disease activity was observed, the CD8+ NK cell subset behaved in the opposite direction. At baseline, responders showed significantly increased frequencies of CD8+ NK cells compared with non-responders.ConclusionsThis is the first study demonstrating that the composition of the NK cell compartment has predictive power for prediction of therapeutic outcome for anti-TNF-α blockers, and we identified CD8+ NK cells as a potential new player in the TNF-α-driven chronic inflammatory immune response of AS.

A6.11 Immunoclust based analysis of cytometric profiles reveals immunophenotypic changes in synovial fluid compared to peripheral blood cells in rheumatoid arthritis

Background and objectives Flow cytometry offers quantification of multidimensional characteristics at single cell level for millions of cells. Multiplex flow cytometry or mass cytometry enable to screen for dozens of antigens on a single cell. Conventional analysis of such data requires user defined gating and is time consuming. Using the new bioinformatics tools immunoClust for automated and user-independent analysis, we investigated the complexity of phenotypic changes of immune cells upon migration from peripheral blood (PB) to synovial fluid (SF) in rheumatoid arthritis (RA). Materials and methods Seven paired samples of PB and SF from RA patients were stained in 10 different antibody cocktails and data investigated by the newly developed immunoClust pipeline for sample specific populations and differences between PB and SF. Population clustering and comparative meta-clustering assume finite mixture models and use Expectation Maximisation (EM)-iterations with integrated classification likelihood (ICL) criterion to stabilise the number of reasonable clusters. For meta-clustering, a probability measure on Gaussian distributions was invented, which is based on the Bhattacharyya Coefficients. Meta-clusters were manually annotated and classified. The clustering tools of immunoClust are available as open source R-package in Bioconductor. Results Automated clustering with 46 different surface markers detected all major leukocyte subsets and several activation markers in PB and SF samples including neutrophils, eosinophils, T-cells and sub-populations, monocytes, B-cells, NK-cells and dendritic cells. The comparison revealed about 10 highly significant changes per staining cocktail. For example the percentage of monocytes/macrophages was doubled in SF and dominated by CD16+ cells, the frequencies of effector/memory subpopulations of lymphocytes were increased and naïve T-cells and B-cells were almost completely absent. In addition several unexpected populations like CCR7+ monocytes were found in SF only. Conclusion In conclusion, the results give a reasonable starting point to face the next field of research for marker detection and prediction analysis. The data will be further exploited for changes in cell activation and differentiation in SF in order to screen for these populations also in PB. This approach is not only applicable to fluorescence-based flow data but could be also used for multi-parametric data sets generated by mass spectrometry-based cytometry (CyTOF).

A8.16 T helper lymphocytes and monocytes as biosensors of type I interferon responses in viral infection and autoimmunity

Background Immune responses in viral infections and autoimmunity are characterised by type I interferon (IFN) gene signatures detectable in peripheral blood mononuclear cells (PBMC). We have identified cell-specific IFN signatures in CD4 T cells and in monocyte subsets and could use them to discriminate between physiologic and pathologically sustained type I responses in yellow fever vaccinated individuals and in systemic lupus erythematosus patients (SLE). In this study we show that these cell-specific IFN signatures are not only working in isolated cell subsets but are even robust enough to classify PBMC in viral infection and autoimmunity. Materials and Methods We have used whole genome expression arrays to identify cell-specific type I IFN response signatures in isolated CD4 lymphocytes, CD16+ and CD16- monocyte subsets. These signatures were used to classify independent PBMC samples of yellow fever vaccinated individuals (n = 10) and jSLE patients (n = 10). Results 98/165/173 probe sets (CD4+ T cells/CD16- inflammatory Mo/CD16 + resident Mo, respectively, fold change > = 2, < = -2) were detected as a “common” IFN signature observed both in autoimmunity and in immunised ND. 111/164/120 probe sets were detected as an “autoimmune-specific” IFN signature, whereas only 0/8/5 probe sets were detected to be specific for the “virus-induced” IFN signature. Interestingly, these cell-specifically generated expression signatures were successfully used to classify PBMC of viral infection and jSLE by hierarchical cluster analyses. Conclusion This study demonstrated that IFN signature in autoimmunity and that in viral infection are quite different in the number of IFN-related genes activated and their expression magnitudes. These cell-specific signatures are robust enough to classify PBMC samples. In summary, “common” and “autoimmune-specific” IFN signature genes are of potential interest as clinical biomarkers in SLE diagnostics to differentiate between a disease flare and a viral infection.

A10.4 Automated and Standardised Analysis for High Dimensional Cytometric Data Provides New Options for Complex Cell-Associated Biomarker Screening

Background and Objectives Flow cytometry (FCM) is widely used in clinical research and offers rapid and quantitative characterisation at single cell level. Traditional analysis is a semiautomated, time-consuming process of gating and successive 2-D projections, influenced by investigator-specific settings. With an increasing number of parameters for multiplexing, the manual analysis step is most limiting and impedes high throughput analysis in FCM. Therefore, we developed a new algorithm for automated and standardised analysis of multiplex FCM data. Materials and Methods Automation included asinh-transformation of data, cell grouping, population detection and population feature extraction. For grouping of cells, an unbiased unsupervised model based t-mixture approach with Expectation Maximisation (EM)-iteration was applied. Populations were detected and identified by meta-clustering of several experiments according to position and extension of cell-clusters in multi-dimensional space and by including a General Procrustes Analysis (GPA) step. For validation, peripheral leukocytes from healthy donors and patients with rheumatoid arthritis (RA) were prepared by hypoosmotic erythrocyte lysis and stained with different sets of lineage-specific antibodies, including CD3, CD4, CD8, CD56, CD19, CD14 and CD15. In parallel, different leukocyte samples were depleted of one of these populations by magnetic beads. Qualitative and quantitative characteristics of major populations were compared with conventional manual analysis. Results Whole blood leukocytes stained simultaneously with up to 7 markers were correctly distinguished in all major populations including granulocytes (CD15+), T-cells and their subpopulations (CD3+, CD4+, CD8+), monocytes (CD14+), B-cells (CD19+), and NK-cells (CD56+). The result was comparable to the “gold standard” of manual evaluation by an expert. The new technology is able to detect subclusters and to characterise so far neglected smaller populations based on the new parameters generated. Automated clustering did not require fluorescence compensation of data. Cell-grouping is applicable even for large FCM datasets of at least 10 parameters and more than 1 million events. Comparing the cell-clusters between RA and healthy controls, differences were detectable in several cell (sub-)populations, stable enough to perform correct classification into controls and disease. Conclusions Our approach reveals first promising results for the analysis of large datasets as generated by multiplex FCM analysis in an automated and time-saving way. Defined clustering algorithms avoid operator-induced bias. In addition, our unsupervised procedure is able to detect unexpected sub-clusters and to characterise so far neglected smaller populations, which may help not only to distinguish normal from disease but also to develop markers for disease activity and therapeutic stratification. Acknowledgement BTCure IMI grant agreement no. 115142.

A10.13 Identification of Immunophenotypic Signatures in Peripheral Blood of Multiple Sclerosis Patients by Multiparametric Flow Cytometry

Background Multiple Sclerosis (MS) is a chronic inflammatory and demyelinating disease of the CNS with unknown aetiology until now. CD4+ T helper (Th) 1 cells, proinflammatory Th17 cells, CD8+ T cells, B cells, natural killer (NK) cells and denritic cells (DC) are accepted to play in important role in pathogenesis of disease. Lymphocytes of the peripheral blood from multiple sclerosis (MS) patients are characterised by proinflammatory function but robust cell surface markers to distinguish patients from controls are not available until now. Methods/Results In this study we analysed frequency and phenotype of blood cell subsets by multicolour staining including up to 50 different monoclonal antibodies that allowed detecting 894 parameter combinations per sample, including 99 control parameters. Relative event numbers, absolute cell numbers and relative fluorescence intensities of all fluorochromes were compared between 10 patients with a clinically isolated syndrome (CIS) or early relapsing-remitting MS (RRMS) and 10 healthy age- and sex-matched controls and were analysed in a semiautomatic manner. Statistical significant results in expression profile between both groups were found involved receptors in inflammation (CD119, CD62L, CRTH2) on cells of adaptive and also innate immunity (Welch t-test < 0.05). Conclusions The results identify a MS specific profile of peripheral blood leukocytes by a multiparametric cytometric approach. Thus, the large-scale immunophenotyping is appropriate tool to identify new disease relevant leukocyte subtypes and receptors and may have implications for diagnosis, pathophysiological understanding and therapy-monitoring of patients with MS and other autoimmune diseases.