DNA methylation and mRNA and microRNA expression of SLE CD4+ T cells correlate with disease phenotype.

Systemic lupus erythematosus (SLE) is an autoimmune disease well known for its clinical heterogeneity, and its etiology secondary to a cross-talk involving genetic predisposition and environmental stimuli. Although genome-wide analysis has contributed greatly to our understanding of the genetic basis of SLE, there is increasing evidence for a role of epigenetics. Indeed, recent data have demonstrated that in patients with SLE, there are striking alterations of DNA methylation, histone modifications, and deregulated microRNA expression, the sum of which contribute to over-expression of select autoimmune-related genes and loss of tolerance. To address this issue at the level of clinical phenotype, we performed DNA methylation, mRNA and microRNA expression screening using high-throughput sequencing of purified CD4+ T cells from patients with SLE, compared to age and sex matched controls. In particular, we studied 42 patients with SLE and divided this group into three clinical phenotypes: a) the presence of skin lesions without signs of systemic pathology; b) skin lesions but also chronic renal pathology; and c) skin lesions, chronic renal pathology and polyarticular disease. Interestingly, and as expected, sequencing data revealed changes in DNA methylation in SLE compared to controls. However, and more importantly, although there were common methylation changes found in all groups of SLE compared to controls, there was specific DNA methylation changes that correlated with clinical phenotype. These included changes in the novel key target genes NLRP2, CD300LB and S1PR3, as well as changes in the critical pathways, including the adherens junction and leukocyte transendothelial migration. We also noted that a significant proportion of genes undergoing DNA methylation changes were inversely correlated with gene expression and that miRNA screening revealed the existence of subsets with changes in expression. Integrated analysis of this data highlights specific sets of miRNAs controlled by DNA methylation, and genes that are altered by methylation and targeted by miRNAs. In conclusion, our findings suggest select epigenetic mechanisms that contribute to clinical phenotypes and further shed light on a new venue for basic SLE research.

Screen Magnification for Readers with Low Vision: A Study on Usability and Performance.

We present a study with 20 participants with low vision who operated two types of screen magnification (lens and full) on a laptop computer to read two types of document (text and web page). Our purposes were to comparatively assess the two magnification modalities, and to obtain some insight into how people with low vision use the mouse to control the center of magnification. These observations may inform the design of systems for the automatic control of the center of magnification. Our results show that there were no significant differences in reading performances or in subjective preferences between the two magnification modes. However, when using the lens mode, our participants adopted more consistent and uniform mouse motion patterns, while longer and more frequent pauses and shorter overall path lengths were measured using the full mode. Analysis of the distribution of gaze points (as measured by a gaze tracker) using the full mode shows that, when reading a text document, most participants preferred to move the area of interest to a specific region of the screen.

Total glucosides of paeony induces regulatory CD4(+)CD25(+) T cells by increasing Foxp3 demethylation in lupus CD4(+) T cells.

Total glucosides of paeony (TGP), an active compound extracted from Paeony root, has been used in therapy for autoimmune diseases. However the molecular mechanism of TGP in the prevention of autoimmune response remains unclear. In this study, we found that TGP treatment significantly increased the percentage and number of Treg cells in lupus CD4(+) T cells. Further investigation revealed that treatment with TGP increased the expression of Foxp3 in lupus CD4(+) T cells by down-regulating Foxp3 promoter methylation levels. However, we couldn't observe similar results in healthy control CD4(+) T cells treated by TGP. Moreover, our results also showed that IFN-γ and IL-2 expression was enhanced in TGP-treated lupus CD4(+) T cells. These findings indicate that TGP inhibits autoimmunity in SLE patients possibly by inducing Treg cell differentiation, which may in turn be due to its ability to regulate the methylation status of the Foxp3 promoter and activate IFN-γ and IL-2 signaling.

Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach.

OBJECTIVE: We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. Existing methods for community detection focus only on single-subject analysis of dynamic networks; while recent extensions to multiple-subjects analysis are limited to static networks. METHOD: To overcome these limitations, we propose a multi-subject, Markov-switching stochastic block model (MSS-SBM) to identify state-related changes in brain community organization over a group of individuals. We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. We develop a novel procedure for fitting the multilayer SBM that builds on multislice modularity maximization which can uncover a common community partition of all layers (subjects) simultaneously. By augmenting with a dynamic Markov switching process, our proposed method is able to capture a set of distinct, recurring temporal states with respect to inter-community interactions over subjects and the change points between them. RESULTS: Simulation shows accurate community recovery and tracking of dynamic community regimes over multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful non-assortative brain community motifs, e.g., core-periphery structure at the group level, that are associated with language comprehension and motor functions suggesting their putative role in complex information integration. Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions. CONCLUSION: The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects.

Temporal-Spatial Patterns in Dynamic Functional Brain Network for Self-Paced Hand Movement.

Dynamic functional connectivity is attracting a growing interest as it has been suggested to be a more accurate representation of functional brain networks compared to traditional functional connectivity. It is believed that the functional connectivity fluctuations result from the transitions among different brain states other than continuous changes in the brain. In this paper, we aim to investigate the spatial-temporal changes in the interactions between different brain regions during a self-paced hand movement with EEG signals. A systematic analysis framework, consisting of connectivity metric calculation, brain state segmentation, temporal representative graph extraction, and spatial community detection, is proposed to analyze the dynamic functional connectivity. First, corrected imaginary coherency is applied to measure the functional connectivity as it is insensitive to EEG volume conduction problem. Second, singular value decomposition (SVD) vector space distance between the connectivity matrices at two adjacent time points is calculated. In addition, the brain states are segmented based on the changes in the time series of SVD vector space distances. Third, one representative graph is summarized within each state segment using the SVD vectors corresponding to the k largest singular values. Finally, spatial patterns on the representative graph are detected with a modularity-based community detection method. Based on the SVD vector space distance using the change point detection method, a series of brain states lasting for hundreds of milliseconds are identified. Moreover, we find that the sudden decrease points in SVD vector space distance coincide with early Bereitschafts potential. In addition, we find that there are several connectivity patterns along the time before the onset of movement. At first, the functional connectivity is relatively dispersed. Gradually, the functional connectivity begins to concentrate and the predominant communities in each dynamic functional network can be observed clearly.