Biology subjects

Knepper, M. A.

Publications and source records attributed to Knepper, M. A..

3 recordsLinked to original sources

bioRxiv2018

RNA-Seq and Protein Mass Spectrometry in Microdissected Kidney Tubules Reveal Signaling Processes that Initiate Lithium-Induced Diabetes Insipidus

ABSTRACT1Lithium salts, used for treatment of bipolar disorder, frequently induce nephrogenic diabetes insipidus (NDI), limiting therapeutic success. NDI is associated with loss of expression of the molecular water channel, aquaporin-2, in the renal collecting duct (CD). Here, we use the methods of systems biology in a well-established rat model of lithium-induced NDI to identify signaling pathways activated at the onset of polyuria. Using single-tubule RNA-Seq, full transcriptomes were determined in microdissected cortical CDs of rats 72 hrs after initiation of lithium chloride (LiCl) administration (vs. time-controls without LiCl). Transcriptome-wide changes in mRNA abundances were mapped to gene sets associated with curated canonical signaling pathways, showing evidence for activation of NF-{kappa}B signaling with induction of genes coding for multiple chemokines as well as most components of the Major Histocompatibility Complex (MHC) Class I antigen-presenting complex. Administration of antiinflammatory doses of dexamethasone to LiCl-treated rats countered the loss of aquaporin-2 protein. RNA-Seq also confirmed prior evidence of a shift from quiescence into the cell cycle with arrest. Time course studies demonstrated an early (12 hrs) increase in multiple immediate early genes including several transcription factors. Protein mass spectrometry in microdissected cortical CDs provided corroborative evidence but also identified decreased abundance of several anti-oxidant proteins. Integration of new data with prior data about lithium effects at a molecular level leads to a signaling model in which lithium increases ERK activation leading to induction of NF-{kappa}B signaling and an inflammatory-like response that represses Aqp2 gene transcription.

systems biology↗

bioRxiv2018

Cell-Type Selective Markers Represented in Whole-Kidney RNA-Seq Data

Bulk-tissue RNA-Seq is seeing increasing use in the study of physiological and pathophysiological processes in the kidney. However, the presence of multiple cell types in kidney complicates the data interpretation. Here we address the question, \"What cell types are represented in whole-kidney RNA-Seq data?\" to identify circumstances in which bulk-kidney RNA-Seq can be successfully interpreted. We carried out RNA-Seq in mouse whole kidneys and microdissected renal tubule segments. To aid in the interpretation of the data, we compiled a database of cell-type selective protein markers for 43 cell types believed to be present in kidney tissue. The whole-kidney RNA-Seq analysis identified transcripts corresponding to 17742 genes, distributed over 5 orders of magnitude of expression level. Markers for all 43 curated cell types were detectable. Analysis of the cellular makeup of mouse and rat kidney, calculated from published literature, suggests that proximal tubule cells account for more than half of the mRNA in a kidney. Comparison of RNA-Seq data from microdissected proximal tubules with whole-kidney data supports this view. RNA-Seq data for cell-type selective markers in bulk-kidney samples provide a valid means to identify changes in minority-cell abundances in kidney tissue. Because proximal tubules make up a substantial fraction of whole-kidney samples, changes in proximal tubule gene expression can be assessed presumptively by bulk-kidney RNA-Seq, although results could potentially be obscured by the presence of mRNA from other cell types. The dominance of proximal tubule cells in whole-kidney samples also has implications for the interpretation of single-cell RNA-Seq data.

systems biology↗

bioRxiv2017

Transcriptomes of major renal collecting-duct cell types in mouse identified by single-cell RNA-Seq

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 fluorescence-activated cell sorting (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, viz. c-Kit. Single-cell RNA-Seq using the 1C- 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 both aquapor{inverted exclamation}n-2 and either 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 crosstalk 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.\n\nSIGNIFICANCE STATEMENTA long-term goal in mammalian biology is to identify the genes expressed in every cell type of the body. In kidney, the expressed genes (\"transcriptome\") of all epithelial cell types have already been identified with the exception of the cells that make up the renal collecting duct, responsible for regulation of blood pressure and body fluid composition. Here, a technique called \"single-cell RNA-Seq\" was used in mouse to identify transcriptomes for the major collecting-duct cell types: type A intercalated cells, type B intercalated cells and principal cells. The information was used to create a publicly-accessible online resource. The data allowed identification of genes that are selectively expressed in each cell type, informative for cell-level understanding of physiology and pathophysiology.

physiology↗