Concepción Marañón

Concordance of Increased B1 Cell Subset and Lupus Phenotypes in Mice and Humans Is Dependent on BLK Expression Levels

Polymorphisms in the B lymphoid tyrosine kinase (BLK) gene have been associated with autoimmune diseases, including systemic lupus erythematosus, with risk correlating with reduced expression of BLK. How reduced expression of BLK causes autoimmunity is unknown. Using Blk+/+, Blk+/−, and Blk−/− mice, we show that aged female Blk+/− and Blk−/− mice produced higher anti-dsDNA IgG Abs and developed immune complex–mediated glomerulonephritis, compared with Blk+/+ mice. Starting at young age, Blk+/− and Blk−/− mice accumulated increased numbers of splenic B1a cells, which differentiated into class-switched CD138+ IgG-secreting B1a cells. Increased infiltration of B1a-like cells into the kidneys was also observed in aged Blk+/− and Blk−/− mice. In humans, we found that healthy individuals had BLK genotype-dependent levels of anti-dsDNA IgG Abs as well as increased numbers of a B1-like cell population, CD19+CD3−CD20+CD43+CD27+, in peripheral blood. Furthermore, we describe the presence of B1-like cells in the tubulointerstitial space of human lupus kidney biopsies. Taken together, our study reveals a previously unappreciated role of reduced BLK expression on extraperitoneal accumulation of B1a cells in mice, as well as the presence of IgG autoantibodies and B1-like cells in humans.

Multi-center harmonization of flow cytometers in the context of the European “PRECISESADS” project

The innovative medicine initiative project called PRECISESADS will study 2.500 individuals affected by systemic autoimmune diseases (SADs) and controls. Among extensive OMICS approaches, multi-parameter flow cytometry analyses will be performed in eleven different centers. Therefore, the integration of all data in common bioinformatical and biostatistical investigations requires a fine mirroring of all instruments. We describe here the procedure elaborated to achieve this prerequisite. One flow cytometer chosen as reference instrument fixed the mean fluorescence intensities (MFIs) of 8 different fluorochrome-conjugated antibodies (Abs) using VersaComp Ab capture beads. The ten other centers adjusted their own PMT voltages to reach the same MFIs. Subsequently, all centers acquired Rainbow 8-peak beads data on a daily basis to follow the stability of their instrument overtime. One blood sample has been dispatched and concomitantly stained in all centers. Comparison of leukocytes frequencies and cell surface marker MFIs demonstrated the close sensitivity of all flow cytometers, allowing a multicenter analysis. The effective multi-center harmonization enables the constitution of a workable wide flow cytometry database for the identification of specific molecular signatures in individuals with SADs.

Myeloid Populations in Systemic Autoimmune Diseases

Systemic autoimmune diseases (SADs) encompass a wide spectrum of clinical signs as a reflection of their complex physiopathology. A variety of mechanisms related with the innate immune system are in the origin of the loss of self-tolerance in these diseases, and for most of them, the myeloid leukocytes are key actors. Monocytes, macrophages, dendritic cells, and neutrophils are first-line immune effectors located in the interface between innate and adaptive immunity. They are crucial in the organization of the local and systemic responses to damage-associated molecular patterns (DAMPs) and determine the intensity, orientation, and duration of the local immune response through the expression of chemokines, costimulatory or protolerogenic factors. In this review, we summarize the current knowledge about the role of the main myeloid populations in the induction and maintenance of systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), primary antiphospholipid antibody syndrome (PAPS), systemic sclerosis (SSc), and Sjögren’s syndrome (SjS), based on the data from both mouse preclinical models and patients. According to these data, our challenge in the next few years is to better dissect the fine mechanisms underlying the pathological role of myeloid cells in these diseases in order to define specific cell subsets or proteins that can be potential targets for drug development.

The SLE variant Ala71Thr of BLK severely decreases protein abundance and binding to BANK1 through impairment of the SH3 domain function.

The B-lymphocyte kinase (BLK) gene is associated genetically with several human autoimmune diseases including systemic lupus erythematosus. We recently described that the genetic risk is given by two haplotypes: one covering several strongly linked single-nucleotide polymorphisms within the promoter of the gene that correlated with low transcript levels, and a second haplotype that includes a rare nonsynonymous variant (Ala71Thr). Here we show that this variant, located within the BLK SH3 domain, is a major determinant of protein levels. In vitro analyses show that the 71Thr isoform is hyperphosphorylated and promotes kinase activation. As a consequence, BLK is ubiquitinated, its proteasomal degradation enhanced and the average life of the protein is reduced by half. Altogether, these findings suggest that an intrinsic autoregulatory mechanism previously unappreciated in BLK is disrupted by the 71Thr substitution. Because the SH3 domain is also involved in protein interactions, we sought for differences between the two isoforms in trafficking and binding to protein partners. We found that binding of the 71Thr variant to the adaptor protein BANK1 is severely reduced. Our study provides new insights on the intrinsic regulation of BLK activation and highlights the dominant role of its SH3 domain in BANK1 binding.

Metagene projection characterizes GEN2.2 and CAL-1 as relevant human plasmacytoid dendritic cell models

Motivation Plasmacytoid dendritic cells (pDC) play a major role in the regulation of adaptive and innate immunity. Human pDC are difficult to isolate from peripheral blood and do not survive in culture making the study of their biology challenging. Recently, two leukemic counterparts of pDC, CAL-1 and GEN2.2, have been proposed as representative models of human pDC. Nevertheless, their relationship with pDC has been established only by means of particular functional and phenotypic similarities. With the aim of characterizing GEN2.2 and CAL-1 in the context of the main circulating immune cell populations we have performed microarray gene expression profiling of GEN2.2 and carried out an integrated analysis using publicly available gene expression datasets of CAL-1 and the main circulating primary leukocyte lineages. Results Our results show that GEN2.2 and CAL-1 share common gene expression programs with primary pDC, clustering apart from the rest of circulating hematopoietic lineages. We have also identified common differentially expressed genes that can be relevant in pDC biology. In addition, we have revealed the common and differential pathways activated in primary pDC and cell lines upon CpG stimulatio. Availability and implementation R code and data are available in the supplementary material. Contact pedro.carmona@genyo.es or concepcion.maranon@genyo.es. Supplementary information Supplementary data are available at Bioinformatics online.

Differential Phenotypic and Functional Profiles of TcCA-2 -Specific Cytotoxic CD8+ T Cells in the Asymptomatic versus Cardiac Phase in Chagasic Patients

It has been reported that the immune response mediated by T CD8+ lymphocytes plays a critical role in the control of Trypanosoma cruzi infection and that the clinical symptoms of Chagas disease appear to be related to the competence of the CD8+ T immune response against the parasite. Herewith, in silico prediction and binding assays on TAP-deficient T2 cells were used to identify potential HLA-A*02:01 ligands in the T. cruzi TcCA-2 protein. The TcCA-2-specific CD8+ T cells were functionality evaluated by Granzyme B and cytokine production in peripheral blood mononuclear cells (PBMC) from Chagas disease patients stimulated with the identified HLA-A*02:01 peptides. The specific cells were phenotypically characterized by flow cytometry using several surface markers and HLA-A*02:01 APC-labeled dextramer loaded with the peptides. In the T. cruzi TcCA-2 protein four T CD8+ epitopes were identified which are processed and presented during Chagas disease. Interestingly, a differential cellular phenotypic profile could be correlated with the severity of the disease. The TcCA-2-specific T CD8+ cells from patients with cardiac symptoms are mainly effector memory cells (TEM and TEMRA) while, those present in the asymptomatic phase are predominantly naive cells (TNAIVE). Moreover, in patients with cardiac symptoms the percentage of cells with senescence features is significantly higher than in patients at the asymptomatic phase of the disease. We consider that the identification of these new class I-restricted epitopes are helpful for designing biomarkers of sickness pathology as well as the development of immunotherapies against T. cruzi infection.

02.16 Machine learning of flow cytometry data encompassing seven systemic autoimmune diseases and controls

Background PRECISESADS1 is an innovative medicine initiative project that is studying systemic autoimmune disease (SAD) patients using OMICs as well as multi-parameter flow cytometry in order to molecularly reclassify SADs. Here, we report on the results of supervised and unsupervised learning of nine flow cytometry panels each containing seven or eight cell surface markers that delineate 357 cellular population signatures across 298 individuals encompassing controls and seven SADs; mixed connective tissue disease (MCTD), primary antiphospholipid syndrome (PAPS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (Ssc), Sjögren’s syndrome (Sjs) and undifferentiated connective tissue disease (UCTD). Materials and methods In order to find the most relevant cellular populations differentiating each disease from controls, 10-fold cross-validated Boruta2 feature selection was used in which a feature was deemed significant only if it was considered a relevant feature in each of the ten cross-validation folds. The features for each disease were then ranked using an ensemble of models. The cross-validated feature selection process was also coupled with random forest3 classification to judge how predictive the disease-specific features are. Then all features were joined into a single matrix and filtered of controls in order to cluster and to visually reveal relationships through force-directed networks and t-Distributed Stochastic Neighbour Embedding.4 Results Feature selection provided a list of relevant cellular populations for each disease that both agree with previous literature and point to previously unidentified SAD drivers. Random forest classification yielded high accuracy and moderate to substantial Cohen’s kappa agreement for each disease, respectively. Clustering and network building using the combined feature matrix showed that while intra-disease micro-clusters frequently occur, SAD clusters are also highly heterogeneous. Conclusions We leveraged the power of machine learning to data-mine a comprehensive list of SAD-pertinent cell populations. Our results verify well-documented relationships between specific cell populations and SADs and also reveal new relationships that warrant further investigation. Lastly, clustering and visualising the diseases using the relevant cell populations gives new insight into the inter-disease relationships of the highly heterogeneous SADs as well as providing valuable information for future studies that will come out of the PRECISESADS project. Funding This work has received support from the EU/EFPIA/Innovative Medicines Initiative Joint Undertaking PRECISESADS grant n° 1 15 565. References Hofmann-Apitius M, Alarcón-Riquelme ME, Chamberlain C and McHale D. “Towards the taxonomy of human disease.”Nat Rev Drug Discov. 2015;14(2):75–6. Miron B. Kursa and Witold R. Rudnicki. “Feature Selection with the Boruta Package.”Journal of Statistical Software.2010;6(11):1–13. Leo Breiman. “Random Forests.”Machine Learning. 2001;45(1):5–32. van der Maaten LJP and Hinton GE. “Visualising high-dimensional data using t-SNE.”J Mach Learn Res. 2008;9:2579–2605.