Introduction: Our aim in this study was to identify the prevalence and clinical characteristics of LRP4/agrin‐antibody–positive double‐seronegative myasthenia gravis (DNMG).Methods: DNMG patients at 16 sites in the United States were tested for LRP4 and agrin antibodies, and the clinical data were collected.Results: Of 181 DNMG patients, 27 (14.9%) were positive for either low‐density lipoprotein receptor–related protein 4 (LRP4) or agrin antibodies. Twenty‐three DNMG patients (12.7%) were positive for both antibodies. More antibody‐positive patients presented with generalized symptoms (69%) compared with antibody‐negative patients (43%) (P ≤ .02). Antibody‐positive patients’ maximum classification on the Myasthenia Gravis Foundation of America (MGFA) scale was significantly higher than that for antibody‐negative patients (P ≤ .005). Seventy percent of antibody‐positive patients were classified as MGFA class III, IV, or V compared with 39% of antibody‐negative patients. Most LRP4‐ and agrin‐antibody–positive patients (24 of 27, 89%) developed generalized myathenia gravis (MG), but with standard MG treatment 81.5% (22 of 27) improved to MGFA class I or II during a mean follow‐up of 11 years.Discussion: Antibody‐positive patients had more severe clinical disease than antibody‐negative patients. Most DNMG patients responded to standard therapy regardless of antibody status.

Persistent STAT5 activation endows antitumor CD4 ⁺ T cells with desirable functional features that optimize adoptive T cell therapy.

Background: Abnormalities in Ca ²⁺ homeostasis are associated with cardiac arrhythmias and heart failure. Triadin plays an important role in Ca ²⁺ homeostasis in cardiomyocytes. Alternative splicing of a single triadin gene produces multiple triadin isoforms. The cardiac-predominant isoform, mouse MT-1 or human Trisk32, is encoded by triadin exons 1 to 8. In humans, mutations in the triadin gene that lead to a reduction in Trisk32 levels in the heart can cause cardiac dysfunction and arrhythmias. Decreased levels of Trisk32 in the heart are also common in patients with heart failure. However, mechanisms that maintain triadin isoform composition in the heart remain elusive. Methods: We analyzed triadin expression in heart explants from patients with heart failure and cardiac arrhythmias and in hearts from mice carrying a knockout allele for Trdn-as , a cardiomyocyte-specific long noncoding RNA encoded by the antisense strand of the triadin gene, between exons 9 and 11. Catecholamine challenge with isoproterenol was performed on Trdn-as knockout mice to assess the role of Trdn-as in cardiac arrhythmogenesis, as assessed by ECG. Ca ²⁺ transients in adult mouse cardiomyocytes were measured with the IonOptix platform or the GCaMP system. Biochemistry assays, single-molecule fluorescence in situ hybridization, subcellular localization imaging, RNA sequencing, and molecular rescue assays were used to investigate the mechanisms by which Trdn-as regulates cardiac function and triadin levels in the heart. Results: We report that Trdn-as maintains cardiac function, at least in part, by regulating alternative splicing of the triadin gene. Knockout of Trdn-as in mice downregulates cardiac triadin, impairs Ca ²⁺ handling, and causes premature death. Trdn-as knockout mice are susceptible to cardiac arrhythmias in response to catecholamine challenge. Normalization of cardiac triadin levels in Trdn-as knockout cardiomyocytes is sufficient to restore Ca ²⁺ handling. Last, Trdn-as colocalizes and interacts with serine/arginine splicing factors in cardiomyocyte nuclei and is essential for efficient recruitment of splicing factors to triadin precursor mRNA. Conclusions: These findings reveal regulation of alternative splicing as a novel mechanism by which a long noncoding RNA controls cardiac function. This study indicates potential therapeutics for heart disease by targeting the long noncoding RNA or pathways regulating alternative splicing.

Biomechanical and molecular stresses may contribute to the pathogenesis of keratoconus (KC). We aimed to profile the transcriptomic changes in healthy primary human corneal (HCF) and KC-derived cells (HKC) combined with TGFβ1 treatment and cyclic mechanical stretch (CMS), mimicking the pathophysiological condition in KC. HCFs (n = 4) and HKCs (n = 4) were cultured in flexible-bottom collagen-coated 6-well plates treated with 0, 5, and 10 ng/mL of TGFβ1 with or without 15% CMS (1 cycle/s, 24 h) using a computer-controlled Flexcell FX-6000T Tension system. We used stranded total RNA-Seq to profile expression changes in 48 HCF/HKC samples (100 bp PE, 70–90 million reads per sample), followed by bioinformatics analysis using an established pipeline with Partek Flow software. A multi-factor ANOVA model, including KC, TGFβ1 treatment, and CMS, was used to identify differentially expressed genes (DEGs, |fold change| ≥ 1.5, FDR ≤ 0.1, CPM ≥ 10 in ≥1 sample) in HKCs (n = 24) vs. HCFs (n = 24) and those responsive to TGFβ1 and/or CMS. PANTHER classification system and the DAVID bioinformatics resources were used to identify significantly enriched pathways (FDR ≤ 0.05). Using multi-factorial ANOVA analyses, 479 DEGs were identified in HKCs vs. HCFs including TGFβ1 treatment and CMS as cofactors. Among these DEGs, 199 KC-altered genes were responsive to TGFβ1, thirteen were responsive to CMS, and six were responsive to TGFβ1 and CMS. Pathway analyses using PANTHER and DAVID indicated the enrichment of genes involved in numerous KC-relevant functions, including but not limited to degradation of extracellular matrix, inflammatory response, apoptotic processes, WNT signaling, collagen fibril organization, and cytoskeletal structure organization. TGFβ1-responsive KC DEGs were also enriched in these. CMS-responsive KC-altered genes such as OBSCN, CLU, HDAC5, AK4, ITGA10, and F2RL1 were identified. Some KC-altered genes, such as CLU and F2RL1, were identified to be responsive to both TGFβ1 and CMS. For the first time, our multi-factorial RNA-Seq study has identified many KC-relevant genes and pathways in HKCs with TGFβ1 treatment under CMS, suggesting a potential role of TGFβ1 and biomechanical stretch in KC development.

Background: Microarray technology provides an efficient means for globally exploring physiological processes governed by the coordinated expression of multiple genes. However, identification of genes differentially expressed in microarray experiments is challenging because of their potentially high type I error rate. Methods for large-scale statistical analyses have been developed but most of them are applicable to two-sample or two-condition data. Results: We developed a large-scale multiple-group F-test based method, named ranking analysis of F-statistics (RAF), which is an extension of ranking analysis of microarray data (RAM) for two-sample t-test. In this method, we proposed a novel random splitting approach to generate the null distribution instead of using permutation, which may not be appropriate for microarray data. We also implemented a two-simulation strategy to estimate the false discovery rate. Simulation results suggested that it has higher efficiency in finding differentially expressed genes among multiple classes at a lower false discovery rate than some commonly used methods. By applying our method to the experimental data, we found 107 genes having significantly differential expressions among 4 treatments at <0.7% FDR, of which 31 belong to the expressed sequence tags (ESTs), 76 are unique genes who have known functions in the brain or central nervous system and belong to six major functional groups. Conclusion: Our method is suitable to identify differentially expressed genes among multiple groups, in particular, when sample size is small.