An autoimmune transcriptional circuit drives FOXP3+ regulatory T cell dysfunction.

Autoimmune diseases, among the most common disorders of young adults, are mediated by genetic and environmental factors. Although CD4+FOXP3+ regulatory T cells (Tregs) play a central role in preventing autoimmunity, the molecular mechanism underlying their dysfunction is unknown. Here, we performed comprehensive transcriptomic and epigenomic profiling of Tregs in the autoimmune disease multiple sclerosis (MS) to identify critical transcriptional programs regulating human autoimmunity. We found that up-regulation of a primate-specific short isoform of PR domain zinc finger protein 1 (PRDM1-S) induces expression of serum and glucocorticoid-regulated kinase 1 (SGK1) independent from the evolutionarily conserved long PRDM1, which led to destabilization of forkhead box P3 (FOXP3) and Treg dysfunction. This aberrant PRDM1-S/SGK1 axis is shared among other autoimmune diseases. Furthermore, the chromatin landscape profiling in Tregs from individuals with MS revealed enriched activating protein-1 (AP-1)/interferon regulatory factor (IRF) transcription factor binding as candidate upstream regulators of PRDM1-S expression and Treg dysfunction. Our study uncovers a mechanistic model where the evolutionary emergence of PRDM1-S and epigenetic priming of AP-1/IRF may be key drivers of dysfunctional Tregs in autoimmune diseases.

PLG nanoparticles target fibroblasts and MARCO+ monocytes to reverse multiorgan fibrosis.

Systemic sclerosis (SSc) is a chronic, multisystem orphan disease with a highly variable clinical course, high mortality rate, and a poorly understood complex pathogenesis. We have identified an important role for a subpopulation of monocytes and macrophages characterized by surface expression of the scavenger receptor macrophage receptor with collagenous structure (MARCO) in chronic inflammation and fibrosis in SSc and in preclinical disease models. We show that MARCO+ monocytes and macrophages accumulate in lesional skin and lung in topographic proximity to activated myofibroblasts in patients with SSc and in the bleomycin-induced mouse model of SSc. Short-term treatment of mice with a potentially novel nanoparticle, poly(lactic-co-glycolic) acid (PLG), which is composed of a carboxylated, FDA-approved, biodegradable polymer and modulates activation and trafficking of MARCO+ inflammatory monocytes, markedly attenuated bleomycin-induced skin and lung inflammation and fibrosis. Mechanistically, in isolated cells in culture, PLG nanoparticles inhibited TGF-dependent fibrotic responses in vitro. Thus, MARCO+ monocytes are potent effector cells of skin and lung fibrosis and can be therapeutically targeted in SSc using PLG nanoparticles.

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

Global ridge orientation modeling for partial fingerprint identification.

Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. First, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification.