Proteomic profiling of nuclear fractions from native renal inner medullary collecting duct cells

Poly(ADP-ribose) polymerase-1 affects vasopressin-mediated AQP2 expression in collecting duct cells of the kidney

Poly(ADP-ribosyl)ation (PARylation), as a posttranslational modification mediated by poly(ADP-ribose) polymerases (PARPs) catalyzing the transfer of ADP-ribose from NAD+ molecules to acceptor proteins, involves a number of cellular processes. As mice lacking the PARP-1 gene (Parp1) produce more urine, we investigated the role of PARP-1, the most prevalent member of the PARP family, in the vasopressin-responsive expression of aquaporin-2 (AQP2). In biotin-conjugated nicotinamide adenine dinucleotide (biotin-NAD+) pulldown and immunoprecipitation assays of poly(ADP)-ribose in mpkCCDc14 cells, immunoblots demonstrated that 1-deamino-8-D-arginine vasopressin (dDAVP) induced the PARylation of total proteins, associated with an increase in the cleavage of PARP-1 and cleaved caspase-3 expression. By inhibiting PARP-1 with siRNA, the abundance of dDAVP-induced AQP2 mRNA and protein was significantly diminished. In contrast, despite a substantial decrease in PARylation, the PARP-1 inhibitor (PJ34) had no effect on the dDAVP-induced regulation of AQP2 expression. The findings suggest that PARP-1 protein expression itself, and not PARP-1-mediated PARylation, is necessary for dDAVP-regulated AQP2 expression. Bioinformatic analysis revealed that 408 proteins interact with PARP-1 in the collecting duct (CD) cells of the kidney. Among them, the signaling pathway of the vasopressin V2 receptor was identified for 49 proteins. In particular, β-catenin, which is phosphorylated at Ser552 by dDAVP, was identified as the PARP-1-interacting protein. A significant decrease of β-catenin phosphorylation (Ser552) in response to dDAVP was associated with siRNA-mediated PARP-1 knockdown. Taken together, PARP-1 is likely to play a role in vasopressin-induced AQP2 expression by interacting with β-catenin in renal CD cells.NEW & NOTEWORTHY The poly(ADP-ribose) polymerase (PARP) family catalyzes poly(ADP-ribosylation) (PARylation), which is one of the posttranslational modifications of largely undetermined physiological significance. This study investigated the role of PARP-1, the most prevalent member of the PARP family, in the vasopressin-responsive expression of aquaporin-2 (AQP2). The results demonstrated that PARP-1 protein expression itself, and not PARP-1-mediated PARylation, is necessary for dDAVP-regulated AQP2 expression. β-Catenin, which is phosphorylated at Ser552 by dDAVP, was identified as the PARP-1-interacting protein.

Phosphoproteomic Identification of Vasopressin/cAMP/Protein Kinase A-Dependent Signaling in Kidney

Water excretion by the kidney is regulated by the neurohypophyseal peptide hormone vasopressin through actions in renal collecting duct cells to regulate the water channel protein aquaporin-2. Vasopressin signaling is initiated by binding to a G-protein-coupled receptor called V2R, which signals through heterotrimeric G-protein subunit Gs α, adenylyl cyclase 6, and activation of the cAMP-regulated protein kinase (PKA). Signaling events coupling PKA activation and aquaporin-2 regulation were largely unknown until the advent of modern protein mass spectrometry techniques that allow proteome-wide quantification of protein phosphorylation changes (phosphoproteomics). This short review documents phosphoproteomic findings in collecting duct cells describing the response to V2R-selective vasopressin agonists and antagonists, the response to CRISPR-mediated deletion of PKA, results from in vitro phosphorylation studies using recombinant PKA, the response to the broad-spectrum kinase inhibitor H89 (N-[2-p-bromocinnamylamino-ethyl]-5-isoquinolinesulphonamide), and the responses underlying lithium-induced nephrogenic diabetes insipidus. These phosphoproteomic data sets have been made available online for modeling vasopressin signaling and signaling downstream from other G-protein-coupled receptors. SIGNIFICANCE STATEMENT: New developments in protein mass spectrometry are facilitating progress in identification of signaling networks. Using mass spectrometry, it is now possible to identify and quantify thousands of phosphorylation sites in a given cell type (phosphoproteomics). The authors describe the use of phosphoproteomics technology to identify signaling mechanisms downstream from a G-protein-coupled receptor, the vasopressin V2 subtype receptor, and its role of the regulation and dysregulation of water excretion in the kidney. Data from multiple phosphoproteomic data sets are provided as web-based resources.

Identification of a foetal epigenetic compartment in adult human kidney

The mammalian kidney has extensive repair capacity; however, identifying adult renal stem cells has proven elusive. We applied an epigenetic marker of foetal cell origin (FCO) in diverse human tissues as a probe for developmental cell persistence, finding a 5.4-fold greater FCO proportion in kidney. Normal kidney FCO proportions averaged 49% with extensive interindividual variation. FCO proportions were significantly negatively correlated with immune-related gene expression and positively correlated with genes expressed in the renal medulla, including those involved in renal organogenesis (e.g., FGF2, PAX8, and HOXB7). FCO associated genes also mapped to medullary nephron segments in mouse and rat, suggesting evolutionary conservation of this cellular compartment. Renal cancer patients whose tumours contained non-zero FCO scores survived longer. The kidney appears unique in possessing substantial foetal epigenetic features. Further study of FCO-related gene methylation may elucidate regenerative regulatory programmes in tissues without apparent discrete stem cell compartments.

Phosphoproteomic identification of vasopressin-regulated protein kinases in collecting duct cells

BACKGROUND AND PURPOSE

The peptide hormone vasopressin regulates water transport in the renal collecting duct largely via the V2 receptor, which triggers a cAMP-mediated activation of a PKA-dependent signalling network. The protein kinases downstream from PKA have not been fully identified or mapped to regulated phosphoproteins.

EXPERIMENTAL APPROACH

We carried out systems-level analysis of large-scale phosphoproteomic data quantifying vasopressin-induced changes in phosphorylation in aquaporin-2-expressing cultured collecting duct (mpkCCD) cells. Quantification was done using stable isotope labelling (SILAC method).

KEY RESULTS

Six hundred forty phosphopeptides were quantified. Stringent statistical analysis identified significant changes in response to vasopressin in 429 of these phosphopeptides. The corresponding phosphoproteins were mapped to known vasopressin-regulated cellular processes. The vasopressin-regulated sites were classified according to the sequences surrounding the phosphorylated amino acids giving 11 groups. Among the vasopressin-regulated phosphoproteins were 25 distinct protein kinases. Among these, six plus PKA appeared to account for phosphorylation of about 81% of the 313 vasopressin-regulated phosphorylation sites. The six downstream kinases were salt-inducible kinase 2 (Sik2), cyclin-dependent kinase 18 (Cdk18), calmodulin-dependent kinase kinase 2 (Camkk2), protein kinase D2 (Prkd2), mitogen-activated kinase 3 (Mapk3) and myosin light chain kinase (Mylk).

CONCLUSION AND IMPLICATIONS

In V2 receptor-mediated signalling, PKA is at the head of a complex network that includes at least six downstream vasopressin-regulated protein kinases that are prime targets for future study. The extensive phosphoproteomic data reported in this study are provided as a web-based data resource for future studies of GPCRs.

Epigenomes in Cardiovascular Disease

Supplemental Digital Content is available in the text.

If unifying principles could be revealed for how the same genome encodes different eukaryotic cells and for how genetic variability and environmental input are integrated to impact cardiovascular health, grand challenges in basic cell biology and translational medicine may succumb to experimental dissection. A rich body of work in model systems has implicated chromatin-modifying enzymes, DNA methylation, noncoding RNAs, and other transcriptome-shaping factors in adult health and in the development, progression, and mitigation of cardiovascular disease. Meanwhile, deployment of epigenomic tools, powered by next-generation sequencing technologies in cardiovascular models and human populations, has enabled description of epigenomic landscapes underpinning cellular function in the cardiovascular system. This essay aims to unpack the conceptual framework in which epigenomes are studied and to stimulate discussion on how principles of chromatin function may inform investigations of cardiovascular disease and the development of new therapies.

From Molecules to Mechanisms: Functional Proteomics and Its Application to Renal Tubule Physiology

Classical physiological studies using electrophysiological, biophysical, biochemical, and molecular techniques have created a detailed picture of molecular transport, bioenergetics, contractility and movement, and growth, as well as the regulation of these processes by external stimuli in cells and organisms. Newer systems biology approaches are beginning to provide deeper and broader understanding of these complex biological processes and their dynamic responses to a variety of environmental cues. In the past decade, advances in mass spectrometry-based proteomic technologies have provided invaluable tools to further elucidate these complex cellular processes, thereby confirming, complementing, and advancing common views of physiology. As one notable example, the application of proteomics to study the regulation of kidney function has yielded novel insights into the chemical and physical processes that tightly control body fluids, electrolytes, and metabolites to provide optimal microenvironments for various cellular and organ functions. Here, we systematically review, summarize, and discuss the most significant key findings from functional proteomic studies in renal epithelial physiology. We also identify further improvements in technological and bioinformatics methods that will be essential to advance precision medicine in nephrology.

Sequence-based searching of custom proteome and transcriptome databases

A long-term goal in renal physiology is to understand the mechanisms involved in collecting duct function and regulation at a cellular and molecular level. The first step in modeling of these mechanisms, which can provide a guide to experimentation, is the generation of a list of model components. We have curated a list of proteins expressed in the rat renal inner medullary collecting duct (IMCD) from proteomic data from 18 different publications. The database has been posted as a public resource at https://hpcwebapps.cit.nih.gov/ESBL/Database/IMCD_Proteome_Database/. It includes 8956 different proteins. To search the IMCD Proteomic Database efficiently, we have created a Java-based program called curated database Basic Local Alignment Search Tool (cdbBLAST), which uses the NCBI BLAST kernel to search for specific amino acid sequences corresponding to proteins in the database. cdbBLAST reports information on the matched protein and identifies proteins in the database that have similar sequences. We have also adapted cdbBLAST to interrogate our previously published IMCD Transcriptome Database. We have made the cdbBLAST program available for use either as a web application or a downloadable .jar file at https://hpcwebapps.cit.nih.gov/ESBL/Database/cdbBLAST/. Database searching based on protein sequence removes ambiguities arising from the standard search method based on official gene symbols and allows the user efficient identification of related proteins that may fulfill the same functional roles.

Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites

INTRODUCTION

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

OBJECTIVES

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

METHODS

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC-ToF-MS) platform after methoximation and silylation.

RESULTS

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

CONCLUSION

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

Identification of UT-A1- and AQP2-interacting proteins in rat inner medullary collecting duct

The urea channel UT-A1 and the water channel aquaporin-2 (AQP2) mediate vasopressin-regulated transport in the renal inner medullary collecting duct (IMCD). To identify the proteins that interact with UT-A1 and AQP2 in native rat IMCD cells, we carried out chemical cross-linking followed by detergent solubilization, immunoprecipitation, and LC-MS/MS analysis of the immunoprecipitated material. The analyses revealed 133 UT-A1-interacting proteins and 139 AQP2-interacting proteins, each identified in multiple replicates. Fifty-three proteins that were present in both the UT-A1 and the AQP2 interactomes can be considered as mediators of housekeeping interactions, likely common to all plasma membrane proteins. Among proteins unique to the UT-A1 list were those involved in posttranslational modifications: phosphorylation (protein kinases Cdc42bpb, Phkb, Camk2d, and Mtor), ubiquitylation/deubiquitylation (Uba1, Usp9x), and neddylation (Nae1 and Uba3). Among the proteins unique to the AQP2 list were several Rab proteins (Rab1a, Rab2a, Rab5b, Rab5c, Rab7a, Rab11a, Rab11b, Rab14, Rab17) involved in membrane trafficking. UT-A1 was found to interact with UT-A3, although quantitative proteomics revealed that most UT-A1 molecules in the cell are not bound to UT-A3. In vitro incubation of UT-A1 peptides with the protein kinases identified in the UT-A1 interactome revealed that all except Mtor were capable of phosphorylating known sites in UT-A1. Overall, the UT-A1 and AQP2 interactomes provide a snapshot of a dynamic process in which UT-A1 and AQP2 are produced in the rough endoplasmic reticulum, processed through the Golgi apparatus, delivered to endosomes that move into and out of the plasma membrane, and are regulated in the plasma membrane.

Identification of β-catenin-interacting proteins in nuclear fractions of native rat collecting duct cells

The gene encoding the aquaporin-2 water channel is regulated transcriptionally in response to vasopressin. In the renal collecting duct, vasopressin stimulates the nuclear translocation and phosphorylation (at Ser⁵⁵²) of β-catenin, a multifunctional protein that acts as a transcriptional coregulator in the nucleus. The purpose of this study was to identify β-catenin-interacting proteins that might be involved in transcriptional regulation in rat inner medullary collecting duct (IMCD) cells, using experimental and computational approaches. We used a standard chromatin immunoprecipitation procedure coupled to mass spectrometry (ChIP-MS) in a nuclear fraction isolated from rat IMCD suspensions. Over four biological replicates, we reproducibly identified 43 β-catenin-binding proteins, including several known β-catenin-binding partners as well as novel interacting proteins. Multiple proteins involved in transcriptional regulation were identified (Taf1, Jup, Tdrd3, Cdh1, Cenpj, and several histones). Many of the identified β-catenin-binding partners were found in prior studies to translocate to the nucleus in response to vasopressin. There was only one DNA-binding transcription factor (TF), specifically Taf1, part of the RNA-polymerase II preinitiation complex. To identify sequence-specific TFs that might interact with β-catenin, Bayes' theorem was used to integrate data from several information sources. The analysis identified several TFs with potential binding sites in the Aqp2 gene promoter that could interact with β-catenin in the regulation of Aqp2 gene transcription, specifically Jun, Junb, Jund, Atf1, Atf2, Mef2d, Usf1, Max, Pou2f1, and Rxra. The findings provide information necessary for modeling the transcriptional response to vasopressin.

Serine/threonine phosphatases and aquaporin-2 regulation in renal collecting duct

Phosphorylation of the aquaporin-2 (AQP2) water channel at four COOH-terminal serines plays a central role in the regulation of water permeability of the renal collecting duct. The level of phosphorylation at these sites is determined by a balance between phosphorylation by protein kinases and dephosphorylation by phosphatases. The phosphatases that dephosphorylate AQP2 have not been identified. Here, we use large-scale data integration techniques to identify serine-threonine phosphatases likely to interact with AQP2 in renal collecting duct principal cells. As a first step, we have created a comprehensive list of 38 S/T phosphatase catalytic subunits present in the mammalian genome. Then we used Bayes' theorem to integrate available information from large-scale data sets from proteomic and transcriptomic studies to rank the known S/T phosphatases with regard to the likelihood that they interact with AQP2 in renal collecting duct cells. To broaden the analysis, we have generated new proteomic data (LC-MS/MS) identifying 4538 distinct proteins including 22 S/T phosphatases in cytoplasmic fractions from native inner medullary collecting duct cells from rats. The official gene symbols corresponding to the top-ranked phosphatases (common names in parentheses) were: Ppp1cb (PP1-β), Ppm1g (PP2C), Ppp1ca (PP1-α), Ppp3ca (PP2-B or calcineurin), Ppp2ca (PP2A-α), Ppp1cc (PP1-γ), Ppp2cb (PP2A-β), Ppp6c (PP6C), and Ppp5c (PP5). This ranking correlates well with results of prior reductionist studies of ion and water channels in renal collecting duct cells.

BIG: a large-scale data integration tool for renal physiology

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.