STAT1 deficiency underlies a proinflammatory imprint of naive CD4+ T cells in spondyloarthritis.

Introduction: In spondyloarthritis (SpA), an increased type 3 immune response, including T helper cells (Th) 17 excess, is observed in both human and SpA animal models, such as the HLA-B27/human ß2-microglobulin transgenic rat (B27-rat). Methods: To investigate this unexplained Th17-biased differentiation, we focused on understanding the immunobiology of B27-rat naive CD4+ T cells (Tn). Results: We observed that neutrally stimulated B27-rat Tn developed heightened Th17 profile even before disease onset, suggesting an intrinsic proinflammatory predisposition. In parallel with this observation, transcriptomic and epigenomic analyses showed that B27-rat Tn exhibited a decreased expression of Interferon/Th1- and increased expression of Th17-related genes. This molecular signature was predicted to be related to an imbalance of STAT1/STAT3 transcription factors activity. Stat1 mRNA and STAT1 protein expression were decreased before disease onset in Tn, even in their thymic precursors, whereas Stat3/STAT3 expression increased upon disease establishment. Confirming the relevance of these results, STAT1 mRNA expression was also decreased in Tn from SpA patients, as compared with healthy controls and rheumatoid arthritis patients. Finally, stimulation of B27-rat Tn with a selective STAT1 activator abolished this preferential IL-17A expression, suggesting that STAT1-altered activity in B27-rats allows Th17 differentiation. Discussion: Altogether, B27-rat Tn harbor a STAT1 deficiency preceding disease onset, which may occur during their thymic differentiation, secondarily associated with a persistent Th17 bias, which is imprinted at the epigenomic level. This early molecular phenomenon might lead to the persistent proinflammatory skew of CD4+ T cells in SpA patients, thus offering new clues to better understand and treat SpA.

Interleukin 27 is a novel cytokine with anti-inflammatory effects against spondyloarthritis through the suppression of Th17 responses.

Introduction: Spondylarthritis (SpA) development in HLA-B27/human ß2-microglobulin transgenic rat (B27-rat) is correlated with altered conventional dendritic cell (cDC) function that promotes an inflammatory pattern of CD4+T cells, including a biased expansion of pro-inflammatory Th17 population and imbalance of regulatory T cells cytokine profile. Transcriptomic analysis revealed that cDCs from B27-rats under express IL-27, an anti-inflammatory cytokine which induces the differentiation of IL-10+ regulatory T cells and inhibits Th17 cells. Methods: Here, we first investigated whether in vitro addition of exogenous IL-27 could reverse the inflammatory pattern observed in CD4+ T cells. Next, we performed preclinical assay using IL-27 to investigate whether in vivo treatment could prevent SpA development in B27-rats. Results: in vitro addition of IL-27 to cocultures of cDCs and CD4+ T cell subsets from B27-rats reduced IL-17 and enhanced IL-10 production by T cells. Likewise, IL-27 inhibited the production of IL-17 by CD4+ T cells from SpA patients. Interestingly, in vivo treatment with recombinant IL-27 starting before SpA onset, inhibited SpA development in B27-rats through the suppression of IL-17/TNF producing CD4+ T cells. Discussion: Overall, our results reveal a potent inhibitory effect of IL-27 and highlight this cytokine as a promising new therapeutic target in SpA, especially for SpA patients non responders to currently approved biotherapies.

A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data.

BACKGROUND: Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations. RESULTS: To this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question. CONCLUSION: Our approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes.

Fast tree aggregation for consensus hierarchical clustering.

BACKGROUND: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. RESULTS: We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be [Formula: see text], n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product. CONCLUSIONS: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.

Female ponderal index at birth and idiopathic infertility.

Epidemiological studies have demonstrated an increased risk of developing non-transmittable diseases in adults subjected to adverse early developmental conditions. Metabolic and cardiovascular diseases have been the focus of most studies. Nevertheless, data from animal models also suggest early programming of fertility. In humans, it is difficult to assess the impact of the in utero environment retrospectively. Birthweight is commonly used as an indirect indicator of intrauterine development. This research is part of the ALIFERT study. We investigated a potential link between ponderal index at birth and female fertility in adulthood. Data from 51 infertile and 74 fertile women were analysed. BW was on average higher in infertile women, whereas birth length did not differ between the two groups; thus, resulting in a significantly higher ponderal index at birth in infertile women. Ponderal index at birth has been identified as a risk factor for infertility. These results suggest the importance of the intra-uterine environment, not only for long-term metabolic health but also for fertility.

Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction.

Heart failure (HF) remains a main cause of mortality worldwide. Risk stratification of patients with systolic chronic HF is critical to identify those who may benefit from advanced HF therapies. The aim of this study is to identify plasmatic proteins that could predict the early death (within 3 years) of HF patients with reduced ejection fraction hospitalized in CHRU de Lille. The subproteome targeted by an aptamer-based technology, the Slow Off-rate Modified Aptamer (SOMA) scan assay of 1310 proteins, was profiled in blood samples from 168 HF patients, and 203 proteins were significantly modulated between patients who died of cardiovascular death and patients who were alive after 3 years of HF evaluation (Wilcoxon test, FDR 5%). A molecular network was built using these 203 proteins, and the resulting network contained 2281 molecules assigned to 34 clusters annotated to biological pathways by Gene Ontology. This network model highlighted extracellular matrix organization as the main mechanism involved in early death in HF patients. In parallel, an adaptive Least Absolute Shrinkage and Selection Operator (LASSO) was performed on these 203 proteins, and six proteins were selected as candidates to predict early death in HF patients: complement C3, cathepsin S and F107B were decreased and MAPK5, MMP1 and MMP7 increased in patients who died of cardiovascular causes compared with patients living 3 years after HF evaluation. This proteomic signature of 6 circulating plasma proteins allows the identification of systolic HF patients with a risk of early death.

Transient Receptor Potential Channel Expression Signatures in Tumor-Derived Endothelial Cells: Functional Roles in Prostate Cancer Angiogenesis.

Background: Transient receptor potential (TRP) channels control multiple processes involved in cancer progression by modulating cell proliferation, survival, invasion and intravasation, as well as, endothelial cell (EC) biology and tumor angiogenesis. Nonetheless, a complete TRP expression signature in tumor vessels, including in prostate cancer (PCa), is still lacking. Methods: In the present study, we profiled by qPCR the expression of all TRP channels in human prostate tumor-derived ECs (TECs) in comparison with TECs from breast and renal tumors. We further functionally characterized the role of the 'prostate-associated' channels in proliferation, sprout formation and elongation, directed motility guiding, as well as in vitro and in vivo morphogenesis and angiogenesis. Results: We identified three 'prostate-associated' genes whose expression is upregulated in prostate TECs: TRPV2 as a positive modulator of TEC proliferation, TRPC3 as an endothelial PCa cell attraction factor and TRPA1 as a critical TEC angiogenic factor in vitro and in vivo. Conclusions: We provide here the full TRP signature of PCa vascularization among which three play a profound effect on EC biology. These results contribute to explain the aggressive phenotype previously observed in PTEC and provide new putative therapeutic targets.