by
Lihe Chen;
Jae Wook Lee;
Chung-Lin Chou;
Anil V. Nair;
Maria A. Battistone;
Teodor G. Paunescu;
Maria Merkulova;
Sylvie Breton;
Jill W. Verlander;
Susan M Wall;
Dennis Brown;
Maurice B. Burg;
Mark A. Knepper
Prior RNA sequencing (RNA-seq) studies have identified complete transcriptomes for most renal epithelial cell types. The exceptions are the cell types that make up the renal collecting duct, namely intercalated cells (ICs) and principal cells (PCs), which account for only a small fraction of the kidney mass, but play critical physiological roles in the regulation of blood pressure, extracellular fluid volume, and extracellular fluid composition. To enrich these cell types, we used FACS that employed well-established lectin cell surface markers for PCs and type B ICs, as well as a newly identified cell surface marker for type A ICs, c-Kit. Single-cell RNA-seq using the IC- and PC-enriched populations as input enabled identification of complete transcriptomes of A-ICs, B-ICs, and PCs. The data were used to create a freely accessible online gene-expression database for collecting duct cells. This database allowed identification of genes that are selectively expressed in each cell type, including cell-surface receptors, transcription factors, transporters, and secreted proteins. The analysis also identified a small fraction of hybrid cells expressing aquaporin-2 and anion exchanger 1 or pendrin transcripts. In many cases, mRNAs for receptors and their ligands were identified in different cells (e.g., Notch2 chiefly in PCs vs. Jag1 chiefly in ICs), suggesting signaling cross-talk among the three cell types. The identified patterns of gene expression among the three types of collecting duct cells provide a foundation for understanding physiological regulation and pathophysiology in the renal collecting duct.
by
Huixue Zhang;
Xiaoyan Lu;
Ning Wang;
Jianjian Wang;
Yuze Cao;
Tianfeng Wang;
Xueling Zhou;
Yang Jiao;
Lei Yang;
Xiaokun Wang;
Lin Cong;
Jianlong Li;
Jie Li;
He-Ping Ma;
Yonghui Pan;
Shangwei Ning;
Lihua Wang
In this study, we identified 74 differentially expressed autophagy-related genes in glioma patients. Analysis using a Cox proportional hazard regression model showed that MAPK8IP1 and SH3GLB1, two autophagy-related genes, were associated with the prognostic signature for glioma. Glioma patients from the CGGA batches 1 and 2, GSE4412 and TCGA datasets could be divided into high- and low-risk groups with different survival times based on levels of MAPK8IP1 and SH3GLB1 expression. The autophagy-related signature was an independent predictor of survival outcomes in glioma patients. MAPK8IP1 overexpression and SH3GLB1 knockdown inhibited glioma cell proliferation, migration and invasi on, and improved Temozolomide sensitivity. These findings suggest autophagy-related genes like MAPK8IP1 and SH3GLB1 could be potential therapeutic targets in glioma.