DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

A Genetic Screen for Anchorage-Independent Proliferation in Mammalian Cells Identifies a Membrane-Bound Neuregulin

Anchorage-independent proliferation is a hallmark of oncogenic transformation and is thought to be conducive to proliferation of cancer cells away from their site of origin. We have previously reported that primary Schwann cells expressing the SV40 Large T antigen (LT) are not fully transformed in that they maintain a strict requirement for attachment, requiring a further genetic change, such as oncogenic Ras, to gain anchorage-independence. Using the LT-expressing cells, we performed a genetic screen for anchorage-independent proliferation and identified Sensory and Motor Neuron Derived Factor (SMDF), a transmembrane class III isoform of Neuregulin 1. In contrast to oncogenic Ras, SMDF induced enhanced proliferation in normal primary Schwann cells but did not trigger cellular senescence. In cooperation with LT, SMDF drove anchorage-independent proliferation, loss of contact inhibition and tumourigenicity. This transforming ability was shared with membrane-bound class III but not secreted class I isoforms of Neuregulin, indicating a distinct mechanism of action. Importantly, we show that despite being membrane-bound signalling molecules, class III neuregulins transform via a cell intrinsic mechanism, as a result of constitutive, elevated levels of ErbB signalling at high cell density and in anchorage-free conditions. This novel transforming mechanism may provide new targets for cancer therapy

Subcellular Localization of SUN2 Is Regulated by Lamin A and Rab5

SUN2 is an inner nuclear membrane protein with a conserved Sad1/UNC-84 homology SUN-domain at the C-terminus. Intriguingly, SUN2 has also been reported to interact with Rab5, which localizes in early endosomes. To clarify the dual subcellular localization of SUN2, we investigated its localization in lamin A/C deficient cells rescued with lamin A or lamin C isoform, and in HeLa cells transfected with Rab5 or its mutants. We found that expression of lamin A but not lamin C partly restored the nuclear envelope localization of SUN2. SUN2 was redistributed to endosomes upon overexpression of Rab5, but remained on the nuclear envelope when the SUN domain was deleted. To explore the physiological function of SUN2 in vesicle trafficking and endocytosis, we demonstrated the colocalization of endogenous SUN2 and Rab5. Moreover, overexpression of SUN2 stimulated the uptake of transferrin while suppression of SUN2 expression attenuated the process. These findings support a role of SUN2 in endocytosis

The nuclear receptor PPARγ selectively inhibits Th17 differentiation in a T cell–intrinsic fashion and suppresses CNS autoimmunity

T helper cells secreting interleukin (IL)-17 (Th17 cells) play a crucial role in autoimmune diseases like multiple sclerosis (MS). Th17 differentiation, which is induced by a combination of transforming growth factor (TGF)-β/IL-6 or IL-21, requires expression of the transcription factor retinoic acid receptor–related orphan receptor γt (RORγt). We identify the nuclear receptor peroxisome proliferator–activated receptor γ (PPARγ) as a key negative regulator of human and mouse Th17 differentiation. PPARγ activation in CD4+ T cells selectively suppressed Th17 differentiation, but not differentiation into Th1, Th2, or regulatory T cells. Control of Th17 differentiation by PPARγ involved inhibition of TGF-β/IL-6–induced expression of RORγt in T cells. Pharmacologic activation of PPARγ prevented removal of the silencing mediator for retinoid and thyroid hormone receptors corepressor from the RORγt promoter in T cells, thus interfering with RORγt transcription. Both T cell–specific PPARγ knockout and endogenous ligand activation revealed the physiological role of PPARγ for continuous T cell–intrinsic control of Th17 differentiation and development of autoimmunity. Importantly, human CD4+ T cells from healthy controls and MS patients were strongly susceptible to PPARγ-mediated suppression of Th17 differentiation. In summary, we report a PPARγ-mediated T cell–intrinsic molecular mechanism that selectively controls Th17 differentiation in mice and in humans and that is amenable to pharmacologic modulation. We therefore propose that PPARγ represents a promising molecular target for specific immunointervention in Th17-mediated autoimmune diseases such as MS

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children.

Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.This article is freely available via Open Access. Click on the Additional Link above to access the full-text via the publisher's site

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

BackgroundAround 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.Methods and findingsThe Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3-to 6-monthly intervals from birth for the development of islet auto-antibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P 14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of 14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.ConclusionsA type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials