Intestinal Microbiota Reduction Followed by Fasting Discloses Microbial Triggering of Inflammation in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) synovitis is dominated by monocytes/macrophages with inflammatory patterns resembling microbial stimulation. In search of triggers, we reduced the intestinal microbiome in 20 RA patients (open label study DRKS00014097) by bowel cleansing and 7-day fasting (≤250 kcal/day) and performed immune monitoring and microbiome sequencing. Patients with metabolic syndrome (n = 10) served as a non-inflammatory control group. Scores of disease activity (DAS28/SDAI) declined within a few days and were improved in 19 of 20 RA patients after breaking the fast (median ∆DAS28 = -1.23; ∆SDAI = -43%) or even achieved remission (DAS28 < 2.6/n = 6; SDAI < 3.3/n = 3). Cytometric profiling with 46 different surface markers revealed the most pronounced phenomenon in RA to be an initially increased monocyte turnover, which improved within a few days after microbiota reduction and fasting. Serum levels of IL-6 and zonulin, an indicator of mucosal barrier disruption, decreased significantly. Endogenous cortisol levels increased during fasting but were insufficient to explain the marked improvement. Sequencing of the intestinal microbiota indicated that fasting reduced potentially arthritogenic bacteria and changed the microbial composition to species with broader metabolic capabilities. More eukaryotic, predominantly fungal colonizers were observed in RA, suggesting possible involvement. This study demonstrates a direct link between the intestinal microbiota and RA-specific inflammation that could be etiologically relevant and would support targeted nutritional interventions against gut dysbiosis as a causal therapeutic approach.

Synovial tissue transcriptomes of long-standing rheumatoid arthritis are dominated by activated macrophages that reflect microbial stimulation.

Advances in microbiome research suggest involvement in chronic inflammatory diseases such as rheumatoid arthritis (RA). Searching for initial trigger(s) in RA, we compared transcriptome profiles of highly inflamed RA synovial tissue (RA-ST) and osteoarthritis (OA)-ST with 182 selected reference transcriptomes of defined cell types and their activation by exogenous (microbial) and endogenous inflammatory stimuli. Screening for dominant changes in RA-ST demonstrated activation of monocytes/macrophages with gene-patterns induced by bacterial and fungal triggers. Gene-patterns of activated B- or T-cells in RA-ST reflected a response to activated monocytes/macrophages rather than inducing their activation. In contrast, OA-ST was dominated by gene-patterns of non-activated macrophages and fibroblasts. The difference between RA and OA was more prominent in transcripts of secreted proteins and was confirmed by protein quantification in synovial fluid (SF) and serum. In total, 24 proteins of activated cells were confirmed in RA-SF compared to OA-SF and some like CXCL13, CCL18, S100A8/A9, sCD14, LBP reflected this increase even in RA serum. Consequently, pathogen-like response patterns in RA suggest that direct microbial influences exist. This challenges the current concept of autoimmunity and immunosuppressive treatment and advocates new diagnostic and therapeutic strategies that consider microbial persistence as important trigger(s) in the etiopathogenesis of RA.

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.

BACKGROUND: Therapeutic 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. METHODS: A 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). RESULTS: As 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. CONCLUSIONS: This 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.

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.

immunoClust--An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets.

Multiparametric fluorescence and mass cytometry offers new perspectives to disclose and to monitor the high diversity of cell populations in the peripheral blood for biomarker research. While high-end cytometric devices are currently available to detect theoretically up to 120 individual parameters at the single cell level, software tools are needed to analyze these complex datasets automatically in acceptable time and without operator bias or knowledge. We developed an automated analysis pipeline, immunoClust, for uncompensated fluorescence and mass cytometry data, which consists of two parts. First, cell events of each sample are grouped into individual clusters. Subsequently, a classification algorithm assorts these cell event clusters into populations comparable between different samples. The clustering of cell events is designed for datasets with large event counts in high dimensions as a global unsupervised method, sensitive to identify rare cell types even when next to large populations. Both parts use model-based clustering with an iterative expectation maximization algorithm and the integrated classification likelihood to obtain the clusters. A detailed description of both algorithms is presented. Testing and validation was performed using 1) blood cell samples of defined composition that were depleted of particular cell subsets by magnetic cell sorting, 2) datasets of the FlowCAP III challenges to identify populations of rare cell types and 3) high-dimensional fluorescence and mass-cytometry datasets for comparison with conventional manual gating procedures. In conclusion, the immunoClust-algorithm is a promising tool to standardize and automate the analysis of high-dimensional cytometric datasets. As a prerequisite for interpretation of such data, it will support our efforts in developing immunological biomarkers for chronic inflammatory disorders and therapy recommendations in personalized medicine. immunoClust is implemented as an R-package and is provided as source code from www.bioconductor.org.

Detection of cancer-related gene expression profiles in severe cervical neoplasia.

The molecular signatures of 20 severe cervical intraepithelial neoplasia (CIN3) cases and 10 cervical squamous cell cancers were determined to define cancer-related gene expression profiles. RNAs extracted from microdissected tissues were amplified by SMART technology and used as probes for hybridization of commercially available cDNA array filters comprising 1,176 cancer-related genes. Ninety-two differentially expressed genes were identified by comparison of pooled cDNA from CIN3 vs. cervical cancer. Heterogeneity in expression of this subset of genes was then analyzed for each biopsy using an algorithm for self-organizing maps. For several gene clusters, the expression pattern for CIN3 differed significantly from that of cancer. Moreover, hierarchical clustering revealed significant differences in distribution of CIN and cancer. Several CIN cases were more strongly related to cancer, suggesting that gene expression profiling may be useful for subdividing pathologically indistinguishable precancers into different biologic entities. This approach also provides a basis for the identification of putative prognostic markers and for targeted molecular therapy.