MiR-1165-3p Suppresses Th2 Differentiation via Targeting IL-13 and PPM1A in a Mouse Model of Allergic Airway Inflammation

Zhengxia Wang,Ningfei Ji,Zhongqi Chen,Zhixiao Sun,Chaojie Wu,Wenqing Yu,Fan Hu,Mao Huang,Mingshun Zhang 대한천식알레르기학회 2020 Allergy, Asthma & Immunology Research Vol.12 No.5

Purpose: CD4+T cells are essential in the pathogenesis of allergic asthma. We have previously demonstrated that microRNA-1165-3p (miR-1165-3p) was significantly reduced in T-helper type (Th) 2 cells and that miR-1165-3p was a surrogate marker for atopic asthma. Little is known about the mechanisms of miR-1165-3p in the regulation of Th2-dominated allergic inflammation. We aimed to investigate the associations between Th2 differentiation and miR-1165b-3p in asthma as well as the possible mechanisms. Methods: CD4+ naïve T cells were differentiated into Th1 or Th2 cells in vitro. MiR-1165-3p was up-regulated or down-regulated using lentiviral systems during Th1/Th2 differentiation. In vivo, the lentiviral particles with the miR-1165-3p enhancer were administered by tail vein injection on the first day of a house dust mite -induced allergic airway inflammation model. Allergic inflammation and Th1/Th2 differentiation were routinely monitored. To investigate the potential targets of miR-1165-3p, biotin-microRNA pull-down products were sequenced, and the candidates were further verified with a dual-luciferase reporter assay. The roles of a target protein phosphatase, Mg2+/Mn2+-dependent 1A (PPM1A), in Th2 cell differentiation and allergic asthma were further explored. Plasma PPM1A was determined by ELISA in 18 subjects with asthma and 20 controls. Results: The lentivirus encoding miR-1165-3p suppressed Th2-cell differentiation in vitro. In contrast, miR-1165-3p silencing promoted Th2-cell development. In the HDM-induced model of allergic airway inflammation, miR-1165-3p up-regulation was accompanied by reduced airway hyper-responsiveness, serum immunoglobulin E, airway inflammation and Th2-cell polarization. IL-13 and PPM1A were the direct targets of miR-1165-3p. The expression of IL-13 or PPM1A was inversely correlated with that of miR-1165-3p. PPM1A regulated the signal transducer and activator of transcription and AKT signaling pathways during Th2 differentiation. Moreover, plasma PPM1A was significantly increased in asthmatic patients. Conclusions: MiR-1165-3p negatively may regulate Th2-cell differentiation by targeting IL-13 and PPM1A in allergic airway inflammation.

Robust multi-atlas label propagation by deep sparse representation

Zu, Chen,Wang, Zhengxia,Zhang, Daoqiang,Liang, Peipeng,Shi, Yonghong,Shen, Dinggang,Wu, Guorong Elsevier 2017 Pattern recognition Vol.63 No.-

<P><B>Abstract</B></P> <P>Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional <I>flat and shallow</I> dictionary to a deep multi-layer structure, where the top layer (<I>label-specific dictionaries</I>) consists of groups of representative atlas patches and the subsequent layers (<I>residual dictionaries</I>) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared to other counterpart label fusion methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Develop a novel multi-atlas patch based label fusion method; </LI> <LI> Alter the conventional flat and shallow dictionary to a deep multi-layer structure; </LI> <LI> Obtain more accurate label fusion results than conventional state-of-the-art methods. </LI> </UL> </P>

Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

Li, Weikai,Wang, Zhengxia,Zhang, Limei,Qiao, Lishan,Shen, Dinggang Frontiers Media S.A. 2017 Frontiers in neuroinformatics Vol.11 No.-

<P>Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L<SUB>1</SUB>-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods.</P>

LincR-PPP2R5C Promotes Th2 Cell Differentiation Through PPP2R5C/PP2A by Forming an RNA–DNA Triplex in Allergic Asthma

Ji Ningfei,Chen Zhongqi,Wang Zhengxia,Sun Wei,Yuan Qi,Zhang Xijie,Jia Xinyu,Wu Jingjing,Jiang Jingxian,Song Meijuan,Xu Tingting,Liu Yanan,Ma Qiyun,Sun Zhixiao,Bao Yanmin,Zhang Mingshun,Huang Mao 대한천식알레르기학회 2024 Allergy, Asthma & Immunology Research Vol.16 No.1

Purpose: The roles and mechanisms of long noncoding RNAs (lncRNAs) in T helper 2 (Th2) differentiation from allergic asthma are poorly understood. We aimed to explore a novel lncRNA, LincR-protein phosphatase 2 regulatory subunit B' gamma (PPP2R5C), in Th2 differentiation in a mouse model of asthma. Methods: LincR-PPP2R5C from RNA-seq data of CD4+ T cells of asthma-like mice were validated and confirmed by quantitative reverse transcription polymerase chain reaction, northern blotting, nuclear and cytoplasmic separation, and fluorescence in situ hybridization (FISH). Lentiviruses encoding LincR-PPP2R5C or shRNA were used to overexpress or silence LincR-PPP2R5C in CD4+ T cells. The interactions between LincR-PPP2R5C and PPP2R5C were explored with western blotting, chromatin isolation by RNA purification assay, and fluorescence resonance energy transfer. An ovalbumin-induced acute asthma model in knockout (KO) mice (LincR-PPP2R5C KO, CD4 conditional LincR-PPP2R5C KO) was established to explore the roles of LincR-PPP2R5C in Th2 differentiation. Results: LncR-PPP2R5C was significantly higher in CD4+ T cells from asthmatic mice ex vivo and Th2 cells in vitro. The lentivirus encoding LincR-PPP2R5C suppressed Th1 differentiation; in contrast, the short hairpin RNA (shRNA) lentivirus decreased LincR-PPP2R5C and Th2 differentiation. Mechanistically, LincR-PPP2R5C deficiency suppressed the phosphatase activity of the protein phosphatase 2A (PP2A) holocomplex, resulting in a decline in Th2 differentiation. The formation of an RNA-DNA triplex between LincR-PPP2R5C and the PPP2R5C promoter enhanced PPP2R5C expression and activated PP2A. LincR-PPP2R5C KO and CD4 conditional KO decreased Th2 differentiation, airway hyperresponsiveness and inflammatory responses. Conclusions: LincR-PPP2R5C regulated PPP2R5C expression and PP2A activity by forming an RNA-DNA triplex with the PPP2R5C promoter, leading to Th2 polarization in a mouse model of acute asthma. Our data presented the first definitive evidence of lncRNAs in the regulation of Th2 cells in asthma.