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IntroductionDisturbances in peripheral blood memory B cell subpopulations have been observed in various autoimmune diseases, but have not been fully delineated in rheumatoid arthritis (RA). Additionally, the possible role of tumour necrosis factor (TNF) in regulating changes in specific peripheral blood memory B cell subsets in RA is still unclear.MethodsThe frequency and distribution of B cell subsets in the peripheral blood and synovial membrane of active RA patients with long-standing disease have been analysed. Additionally, the possible role of TNF in causing disturbances in memory B cell subsets in RA patients was assessed in a clinical trial with the specific TNF-neutralising antibody, infliximab.ResultsRA patients, independent of disease duration, have a significantly lower frequency of peripheral blood pre-switch IgD+CD27+ memory B cells than healthy individuals, whereas post-switch IgD-CD27+ accumulate with increased disease duration. Notably, both pre-switch IgD+CD27+ and post-switch IgD-CD27+ memory B cells accumulate in the synovial membrane of RA patients. Finally, anti-TNF therapy increased the frequency of pre-switch IgD+CD27 memory B cells in the peripheral blood.ConclusionsThe data suggest that decreases in peripheral blood IgD+CD27+ pre-switch memory B cells in RA reflect their accumulation in the synovial tissue. Moreover, the significant increase in the peripheral blood pre-switch memory B cells in patients who underwent specific TNF-blockade with infliximab indicates that trafficking of memory B cells into inflamed tissue in RA patients is regulated by TNF and can be corrected by neutralising TNF.

We analyzed 77 nonproductive and 574 productive human VHDJH rearrangements with a newly developed program, JOINSOLVER. In the productive repertoire, the H chain complementarity determining region 3 (CDR3H) was significantly shorter (46.7 ± 0.5 nucleotides) than in the nonproductive repertoire (53.8 ± 1.9 nucleotides) because of the tendency to select rearrangements with less TdT activity and shorter D segments. Using criteria established by Monte Carlo simulations, D segments could be identified in 71.4% of nonproductive and 64.4% of productive rearrangements, with a mean of 17.6 ± 0.7 and 14.6 ± 0.2 retained germline nucleotides, respectively. Eight of 27 D segments were used more frequently than expected in the nonproductive repertoire, whereas 3 D segments were positively selected and 3 were negatively selected, indicating that both molecular mechanisms and selection biased the D segment usage. There was no bias for D segment reading frame (RF) use in the nonproductive repertoire, whereas negative selection of the RFs encoding stop codons and positive selection of RF2 that frequently encodes hydrophilic amino acids were noted in the productive repertoire. Except for serine, there was no consistent selection or expression of hydrophilic amino acids. A bias toward the pairing of 5′ D segments with 3′ JH segments was observed in the nonproductive but not the productive repertoire, whereas VH usage was random. Rearrangements using inverted D segments, DIR family segments, chromosome 15 D segments and multiple D segments were found infrequently. Analysis of the human CDR3H with JOINSOLVER has provided comprehensive information on the influences that shape this important Ag binding region of VH chains.

We conclude that the lack of phagocyte-derived oxidative burst is associated with spontaneous autoimmunity and linked with type I IFN signature in both mice and humans.

ObjectivesThe differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.MethodsSera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.ResultsUnivariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.ConclusionsPsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

Objective CD8+ T cells contribute to rheumatoid arthritis (RA) by releasing proinflammatory and cytolytic mediators, even in a challenging hypoxic and nutrient‐poor microenvironment such as the synovial membrane. This study was undertaken to explore the mechanisms through which CD8+ T cells meet their metabolic demands in the blood and synovial membrane of patients with RA. Methods Purified blood CD8+ T cells from patients with RA, patients with psoriatic arthritis (PsA), and patients with spondyloarthritis (SpA), as well as healthy control subjects, and CD8+ T cells from RA synovial membrane were stimulated in medium containing 13C‐labeled metabolic substrates in the presence or absence of metabolic inhibitors, under conditions of normoxia or hypoxia. The production of metabolic intermediates was quantified by 1H‐nuclear magnetic resonance. The expression of metabolic enzymes, transcription factors, and immune effector molecules was assessed at both the messenger RNA (mRNA) and protein levels. CD8+ T cell functional studies were performed. Results RA blood CD8+ T cells met their metabolic demands through aerobic glycolysis, production of uniformly 13C‐enriched lactate in the RA blood (2.6 to 3.7–fold higher than in patients with SpA, patients with PsA, and healthy controls; P < 0.01), and induction of glutaminolysis. Overexpression of Warburg effect–linked enzymes in all RA CD8+ T cell subsets maintained this metabolic profile, conferring to the cells the capacity to proliferate under hypoxia and low‐glucose conditions. In all RA CD8+ T cell subsets, lactate dehydrogenase A (LDHA) was overexpressed at the mRNA level (P < 0.03 versus controls; n = 6 per group) and protein level (P < 0.05 versus controls; n = 17 RA patients, n = 9 controls). In RA blood, inhibition of LDHA with FX11 led to reductions in lipogenesis, migration and proliferation of CD8+ T cells, and CD8+ T cell effector functions, while production of reactive oxygen species was increased by 1.5‐fold (P < 0.03 versus controls). Following inhibition of LDHA with FX11, RA CD8+ T cells lost their capacity to induce healthy B cells to develop a proinflammatory phenotype. Similar metabolic alterations were observed in RA CD8+ T cells from the synovial membrane. Conclusion Remodeling glucose and glutamine metabolism in RA CD8+ T cells by targeting LDHA activity can reduce the deleterious inflammatory and cytolytic contributions of these cells to the development of autoimmunity.

Extracellular vesicles (EVs) are released from nearly all mammalian cells and different EV populations have been described. Microvesicles represent large EVs (LEVs) released from the cellular surface, while exosomes are small EVs (SEVs) released from an intracellular compartment. As it is likely that different stimuli promote the release of distinct EV populations, we analyzed EVs from human lymphocytes considering the respective release stimuli (activation Vs. apoptosis induction). We could clearly separate two EV populations, namely SEVs (average diameter <200 nm) and LEVs (diameter range between 200 and 1000 nm). Morphology and size were analyzed by electron microscopy and nanoparticle tracking analysis. Apoptosis induction caused a massive release of LEVs, while activated T-cells released SEVs and LEVs in considerably lower amounts. The release of SEVs from apoptotic T-cells was comparable with LEV release from activated ones. LEVs contained signaling proteins and proteins of the actin-myosin cytoskeleton. SEVs carried cytoplasmic/endosomal proteins like the 70-kDa heat shock protein 70 (HSP70) or tumor susceptibility 101 (TSG101), microtubule-associated proteins, and ubiquitinated proteins. The protein expression profile of SEVs and LEVs changed substantially after the induction of apoptosis. After apoptosis induction, HSP70 and TSG101 (often used as exosome markers) were highly expressed within LEVs. Interestingly, in contrast to HSP70 and TSG101, gelsolin and eps15 homology domain-containing protein 3 (EHD3) turned out to be specific for SEVs irrespective of the stimulus causing the EV release. Finally, we detected several subunits of the proteasome (PSMB9, PSMB10) as well as the danger signal HMGB1 exclusively within apoptotic cell-released LEVs. Thus, we were able to identify new marker proteins that can be useful to discriminate between distinct LEV subpopulations. The mass spectrometry proteomics data are available via ProteomeXchange with identifier PXD009074.

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