Nur77 serves as a molecular brake of the metabolic switch during T cell activation to restrict autoimmunity.

T cells critically depend on reprogramming of metabolic signatures to meet the bioenergetic demands during activation and clonal expansion. Here we identify the transcription factor Nur77 as a cell-intrinsic modulator of T cell activation. Nur77-deficient T cells are highly proliferative, and lack of Nur77 is associated with enhanced T cell activation and increased susceptibility for T cell-mediated inflammatory diseases, such as CNS autoimmunity, allergic contact dermatitis and collagen-induced arthritis. Importantly, Nur77 serves as key regulator of energy metabolism in T cells, restricting mitochondrial respiration and glycolysis and controlling switching between different energy pathways. Transcriptional network analysis revealed that Nur77 modulates the expression of metabolic genes, most likely in close interaction with other transcription factors, especially estrogen-related receptor α. In summary, we identify Nur77 as a transcriptional regulator of T cell metabolism, which elevates the threshold for T cell activation and confers protection in different T cell-mediated inflammatory diseases.

PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4+T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.

Cpxm2 as a novel candidate for cardiac hypertrophy and failure in hypertension.

Treatment of hypertension-mediated cardiac damage with left ventricular (LV) hypertrophy (LVH) and heart failure remains challenging. To identify novel targets, we performed comparative transcriptome analysis between genetic models derived from stroke-prone spontaneously hypertensive rats (SHRSP). Here, we identified carboxypeptidase X 2 (Cpxm2) as a genetic locus affecting LV mass. Analysis of isolated rat cardiomyocytes and cardiofibroblasts indicated Cpxm2 expression and intrinsic upregulation in genetic hypertension. Immunostaining indicated that CPXM2 associates with the t-tubule network of cardiomyocytes. The functional role of Cpxm2 was further investigated in Cpxm2-deficient (KO) and wild-type (WT) mice exposed to deoxycorticosterone acetate (DOCA). WT and KO animals developed severe and similar systolic hypertension in response to DOCA. WT mice developed severe LV damage, including increases in LV masses and diameters, impairment of LV systolic and diastolic function and reduced ejection fraction. These changes were significantly ameliorated or even normalized (i.e., ejection fraction) in KO-DOCA animals. LV transcriptome analysis showed a molecular cardiac hypertrophy/remodeling signature in WT but not KO mice with significant upregulation of 1234 transcripts, including Cpxm2, in response to DOCA. Analysis of endomyocardial biopsies from patients with cardiac hypertrophy indicated significant upregulation of CPXM2 expression. These data support further translational investigation of CPXM2.