Differences in Cellular Clearing Mechanisms of Aggregates of Two Subtypes of HLA-B27

Ankylosing spondylitis (AS) belongs to a group of diseases, called spondyloarthropathies (SpA), that are strongly associated with the genetic marker HLA-B27. AS is characterized by inflammation of joints and primarily affects the spine. Over 160 subtypes of HLA-B27 are known, owing to high polymorphism. Some are strongly associated with disease (e.g., B*2704), whereas others are not (e.g., B*2709). Misfolding of HLA-B27 molecules [as dimers, or as high-molecular-weight (HMW) oligomers] is one of several hypotheses proposed to explain the link between HLA-B27 and AS. Our group has previously established the existence of HMW species of HLA-B27 in AS patients. Still, very little is known about the mechanisms underlying differences in pathogenic outcomes of different HLA-B27 subtypes. We conducted a proteomics-based evaluation of the differential disease association of HLA B*2704 and B*2709, using stable transfectants of genes encoding the two proteins. A clear difference was observed in protein clearance mechanisms: whereas unfolded protein response (UPR), autophagy, and aggresomes were involved in the degradation of B*2704, the endosome–lysosome machinery was primarily involved in B*2709 degradation. These differences offer insights into the differential disease association of B*2704 and B*2709.

Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

Many studies have clarified that microRNAs (miRNAs) are associated with many human diseases. Therefore, it is essential to predict potential miRNA-disease associations for disease pathogenesis and treatment. Numerous machine learning and deep learning approaches have been adopted to this problem. In this paper, we propose a Neural Inductive Matrix completion-based method with Graph Autoencoders (GAE) and Self-Attention mechanism for miRNA-disease associations prediction (NIMGSA). Some of the previous works based on matrix completion ignore the importance of label propagation procedure for inferring miRNA-disease associations, while others cannot integrate matrix completion and label propagation effectively. Varying from previous studies, NIMGSA unifies inductive matrix completion and label propagation via neural network architecture, through the collaborative training of two graph autoencoders. This neural inductive matrix completion-based method is also an implementation of self-attention mechanism for miRNA-disease associations prediction. This end-to-end framework can strengthen the robustness and preciseness of both matrix completion and label propagation. Cross validations indicate that NIMGSA outperforms current miRNA-disease prediction methods. Case studies demonstrate that NIMGSA is competent in detecting potential miRNA-disease associations.

Necrotizing Anterior Scleritis in a Woman with Terrien’s Marginal Degeneration: A Case Report

Purpose: The aim of the study was to report the first case of a patient with Terrien’s Marginal Degeneration (TMD) who developed necrotizing anterior scleritis without systemic disease association, requiring systemic immunosuppressive treatment. Case Report: A 32-year-old female consulted for bilateral ocular burning and hyperemia. Initially, she was diagnosed with conjunctivitis and treated with topical antibiotics and corticosteroids, with mild transitory improvement but the progression of the disease. Years later, she attended the ocular immunology consultation for a second opinion where TMD with ocular inflammatory component OU was diagnosed. Seven months later, she presented with severe pain, decreased visual acuity, and photophobia in OS. At the slit-lamp examination, necrotizing anterior scleritis with a high risk of perforation in OS was observed. The patient was referred to the rheumatologist and started treatment with systemic corticosteroids and cyclophosphamide, exhibiting a clinical improvement. The patient did not meet the criteria for any systemic illness associated with scleritis, such as autoimmune diseases or vasculitis. Thus, scleritis was related to the adjacent inflammatory process associated with TMD, as an atypical presentation of this disease. Conclusion: Although an inflammatory type of TMD has been proposed, it is essential to follow up closely these patients and consider necrotizing anterior scleritis, a severe ocular disease that requires prompt immunosuppressive management, as a possible atypical associated presentation of this disease.

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.