A new IRES-mediated truncated Cx32 isoform inhibits global mRNA translation to suppress glioblastoma.

Glioblastoma (GBM) is the most lethal malignant primary brain tumor. Although multimodal therapy has been applied for GBM, the median survival time remains less than 16 months. Thus, better therapeutic targets in GBM are urgently needed. Herein, we first identified five new N-terminal-truncated Cx32 isoforms (GJB1-28k, GJB1-22k, GJB1-20k, GJB1-15k, and GJB1-13k) and further demonstrated that they were generated via cap-independent internal translation through internal ribosome entry sites (IRESs) in the coding sequence of GJB1 mRNA. Among these isoforms, GJB1-13k inhibited proliferation, promoted apoptosis, and limited cell cycle progression in GBM cells by inhibiting global mRNA translation. In vivo experiments further confirmed the antitumor activity of GJB1-13k against GBM cells. In addition, TSR3, a ribosomal maturation factor, was demonstrated to directly interact with GJB1-13k. Moreover, GBM cells with high TSR3 expression exhibited low sensitivity to GJB1-13k treatment, while GJB1-13k sensitivity was restored by TSR3 knockdown. Our work identifies a new IRES-mediated protein, GJB1-13k, and suggests that overexpression of GJB1-13k in GBM cells with low TSR3 expression or combined targeting of GJB1-13k and TSR3 in GBM cells with high TSR3 expression constitutes a potential therapeutic strategy for GBM.

Single-Cell Transcriptome Identifies the Renal Cell Type Tropism of Human BK Polyomavirus.

BK polyomavirus (BKPyV) infection is the main factor affecting the prognosis of kidney transplant recipients, as no antiviral agent is yet available. A better understanding of the renal-cell-type tropism of BKPyV can serve to develop new treatment strategies. In this study, the single-cell transcriptomic analysis demonstrated that the ranking of BKPyV tropism for the kidney was proximal tubule cells (PT), collecting duct cells (CD), and glomerular endothelial cells (GEC) according to the signature of renal cell type and immune microenvironment. In normal kidneys, we found that BKPyV infection-related transcription factors P65 and CEBPB were PT-specific transcription factors, and PT showed higher glycolysis/gluconeogenesis activities than CD and GEC. Furthermore, in the BKPyV-infected kidneys, the percentage of late viral transcripts in PT was significantly higher than in CD and GEC. In addition, PT had the smallest cell-cell interactions with immune cells compared to CD and GEC in both normal and BKPyV-infected kidneys. Subsequently, we indirectly demonstrated the ranking of BKPyV tropism via the clinical observation of sequential biopsies. Together, our results provided in-depth insights into the renal cell-type tropism of BKPyV in vivo at single-cell resolution and proposed a novel antiviral target.

Automatic Multilabel Classification of Multiple Fundus Diseases Based on Convolutional Neural Network With Squeeze-and-Excitation Attention.

Purpose: Automatic multilabel classification of multiple fundus diseases is of importance for ophthalmologists. This study aims to design an effective multilabel classification model that can automatically classify multiple fundus diseases based on color fundus images. Methods: We proposed a multilabel fundus disease classification model based on a convolutional neural network to classify normal and seven categories of common fundus diseases. Specifically, an attention mechanism was introduced into the network to further extract information features from color fundus images. The fundus images with eight categories of labels were applied to train, validate, and test our model. We employed the validation accuracy, area under the receiver operating characteristic curve (AUC), and F1-score as performance metrics to evaluate our model. Results: Our proposed model achieved better performance with a validation accuracy of 94.27%, an AUC of 85.80%, and an F1-score of 86.08%, compared to two state-of-the-art models. Most important, the number of training parameters has dramatically dropped by three and eight times compared to the two state-of-the-art models. Conclusions: This model can automatically classify multiple fundus diseases with not only excellent accuracy, AUC, and F1-score but also significantly fewer training parameters and lower computational cost, providing a reliable assistant in clinical screening. Translational Relevance: The proposed model can be widely applied in large-scale multiple fundus disease screening, helping to create more efficient diagnostics in primary care settings.