Data integration in physiology using Bayes' rule and minimum Bayes' factors: deubiquitylating enzymes in the renal collecting duct

Proximity-dependent biotinylation reveals an interaction between ubiquitin-specific peptidase 46 and centrosome-related proteins

Protein ubiquitination extensively modulates protein functions and controls various biological processes, such as protein degradation, signal transduction, transcription, and DNA repair. Ubiquitination is a reversible post-translational modification, and deubiquitinating enzymes cleave ubiquitin from proteins. Ubiquitin-specific peptidase 46 (USP46), a deubiquitinase, is highly expressed in the brain and regulates neural functions. Deleting lysine 92 (ΔK92) in USP46 reduces murine depression-like behavior in the tail suspension test. However, the molecular basis for USP46's role in regulating neural function has not yet been fully understood. Here we employed a proximity-dependent biotinylation approach to characterize the USP46 protein interaction partners. Using homology-independent targeted integration (HITI), a genome editing technique, we established knockin cell lines that stably express USP46 wildtype- or ΔK92-biotin ligase fusion protein. We identified 286 candidate interaction partners, including well-known binding partners of USP46. Although there were no obvious differences in the interactome of USP46 between wildtype and ΔK92, a gene ontology analysis revealed that centrosome-related proteins were significantly enriched in the proximal proteins of USP46. Several centrosome-related proteins were bound to USP46 in Neuro2a cells, but their protein expression levels were not affected in the brains of USP46-deficient mice. These results uncover a potential relationship between USP46 and centrosome regulation independently of protein stabilization.

OTUB1 regulation of ferroptosis and the protective role of ferrostatin-1 in lupus nephritis

Lupus nephritis (LN) is a prevalent and severe manifestation of systemic lupus erythematosus (SLE), leading to significant morbidity and mortality. OTUB1, a deubiquitinating enzyme, has emerged as a potential therapeutic target due to its role in cellular protection and regulation of ferroptosis, a form of cell death linked to LN. Our study revealed significantly reduced OTUB1 expression in the glomeruli of LN patients and podocytes, correlated with disease severity. CRISPR/Cas9-mediated OTUB1 knockout in podocytes resulted in pronounced injury, indicated by decreased levels of nephrin and podocin. Ferrostatin-1 treatment effectively mitigated this injury, restoring SLC7A11 expression and significantly reducing MDA levels, Fe2+ levels, BODIPY C11 expression, and normalized cysteine and glutathione expression. In the MRL/lpr mouse model, Ferrostatin-1 significantly improved renal function decreased proteinuria, and ameliorated renal histopathological changes, including reduced glomerular endothelial swelling, mesangial cell proliferation, and leukocyte infiltration. These results underscore the protective role of Ferrostatin-1 in modulating the pathogenesis of LN. OTUB1 plays a crucial protective role against podocyte injury in LN by regulating ferroptosis. Ferrostatin-1 effectively mitigates podocyte damage induced by OTUB1 deficiency, suggesting that targeting ferroptosis could be a promising therapeutic strategy for LN.

Bayesian mapping of protein kinases to vasopressin-regulated phosphorylation sites in renal collecting duct

Vasopressin controls water permeability in the renal collecting duct by regulating the water channel protein, aquaporin-2 (AQP2). Phosphoproteomic studies have identified multiple proteins that undergo phosphorylation changes in response to vasopressin. The kinases responsible for the phosphorylation of most of these sites have not been identified. Here, we use large-scale Bayesian data integration to predict the responsible kinases for 51 phosphoproteomically identified vasopressin-regulated phosphorylation sites in the renal collecting duct. To do this, we applied Bayes' rule to rank the 515 known mammalian protein kinases for each site. Bayes' rule was applied recursively to integrate each of the seven independent datasets, each time using the posterior probability vector of a given step as the prior probability vector of the next step. In total, 30 of the 33 phosphorylation sites that increase with vasopressin were predicted to be phosphorylated by protein kinase A (PKA) catalytic subunit-α, consistent with prior studies implicating PKA in vasopressin signaling. Eighteen of the vasopressin-regulated phosphorylation sites were decreased in response to vasopressin and all but three of these sites were predicted to be targets of extracellular signal-regulated kinases, ERK1 and ERK2. This result implies that ERK1 and ERK2 are inhibited in response to vasopressin V2 receptor occupation, secondary to PKA activation. The six phosphorylation sites not predicted to be phosphorylated by PKA or ERK1/2 are potential targets of other protein kinases previously implicated in aquaporin-2 regulation, including cyclin-dependent kinase 18 (CDK18), calmodulin-dependent kinase 2δ (CAMK2D), AMP-activated kinase catalytic subunit-α-1 (PRKAA1) and CDC42 binding protein kinase β (CDC42BPB).NEW & NOTEWORTHY Vasopressin regulates water transport in the renal collecting duct in part through phosphorylation or dephosphorylation of proteins that regulate aquaporin-2. Prior studies have identified 51 vasopressin-regulated phosphorylation sites in 45 proteins. This study uses Bayesian data integration techniques to combine information from multiple prior proteomics and transcriptomics studies to predict the protein kinases that phosphorylate the 51 sites. Most of the regulated sites were predicted to be phosphorylated by protein kinase A or ERK1/ERK2.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

Bayesian analysis of dynamic phosphoproteomic data identifies protein kinases mediating GPCR responses

Background

A major goal in the discovery of cellular signaling networks is to identify regulated phosphorylation sites (“phosphosites”) and map them to the responsible protein kinases. The V2 vasopressin receptor is a G-protein coupled receptor (GPCR) that is responsible for regulation of renal water excretion through control of aquaporin-2-mediated osmotic water transport in kidney collecting duct cells. Genome editing experiments have demonstrated that virtually all vasopressin-triggered phosphorylation changes are dependent on protein kinase A (PKA), but events downstream from PKA are still obscure.

Methods

Here, we used: 1) Tandem mass tag-based quantitative phosphoproteomics to experimentally track phosphorylation changes over time in native collecting ducts isolated from rat kidneys; 2) a clustering algorithm to classify time course data based on abundance changes and the amino acid sequences surrounding the phosphosites; and 3) Bayes’ Theorem to integrate the dynamic phosphorylation data with multiple prior “omic” data sets covering expression, subcellular location, known kinase activity, and characteristic surrounding sequences to identify a set of protein kinases that are regulated secondary to PKA activation.

Results

Phosphoproteomic studies revealed 185 phosphosites regulated by vasopressin over 15 min. The resulting groups from the cluster algorithm were integrated with Bayes’ Theorem to produce corresponding ranked lists of kinases likely responsible for each group. The top kinases establish three PKA-dependent protein kinase modules whose regulation mediate the physiological effects of vasopressin at a cellular level. The three modules are 1) a pathway involving several Rho/Rac/Cdc42-dependent protein kinases that control actin cytoskeleton dynamics; 2) mitogen-activated protein kinase and cyclin-dependent kinase pathways that control cell proliferation; and 3) calcium/calmodulin-dependent signaling.

Conclusions

Our findings identify a novel set of downstream small GTPase effectors and calcium/calmodulin-dependent kinases with potential roles in the regulation of water permeability through actin cytoskeleton rearrangement and aquaporin-2 trafficking. The proposed signaling network provides a stronger hypothesis for the kinases mediating V2 vasopressin receptor responses, encouraging future targeted examination via reductionist approaches. Furthermore, the Bayesian analysis described here provides a template for investigating signaling via other biological systems and GPCRs.

Systems Biology of the Vasopressin V2 Receptor: New Tools for Discovery of Molecular Actions of a GPCR

Systems biology can be defined as the study of a biological process in which all of the relevant components are investigated together in parallel to discover the mechanism. Although the approach is not new, it has come to the forefront as a result of genome sequencing projects completed in the first few years of the current century. It has elements of large-scale data acquisition (chiefly next-generation sequencing-based methods and protein mass spectrometry) and large-scale data analysis (big data integration and Bayesian modeling). Here we discuss these methodologies and show how they can be applied to understand the downstream effects of GPCR signaling, specifically looking at how the neurohypophyseal peptide hormone vasopressin, working through the V2 receptor and PKA activation, regulates the water channel aquaporin-2. The emerging picture provides a detailedframework for understanding the molecular mechanisms involved in water balance disorders, pointing the way to improved treatment of both polyuric disorders and water-retention disorders causing dilutional hyponatremia.

Bayesian identification of candidate transcription factors for the regulation of Aqp2 gene expression

Aquaporin-2 (Aqp2) gene transcription is strongly regulated by vasopressin in the renal collecting duct. However, the transcription factors (TFs) responsible for the regulation of expression of Aqp2 remain largely unknown. We used Bayes' theorem to integrate several -omics data sets to stratify the 1,344 TFs present in the mouse genome with regard to probabilities of regulating Aqp2 gene transcription. Also, we carried out new RNA sequencing experiments mapping the time course of vasopressin-induced changes in the transcriptome of mpkCCD cells to identify TFs that change in tandem with Aqp2. The analysis identified 17 of 1,344 TFs that are most likely to be involved in the regulation of Aqp2 gene transcription. These TFs included eight that have been proposed in prior studies to play a role in Aqp2 regulation, viz., Cebpb, Elf1, Elf3, Ets1, Jun, Junb, Nfkb1, and Sp1. The remaining nine represent new candidates for future studies (Atf1, Irf3, Klf5, Klf6, Mef2d, Nfyb, Nr2f6, Stat3, and Nr4a1). Conspicuously absent is CREB (Creb1), which has been widely proposed to mediate vasopressin-induced regulation of Aqp2 gene transcription (Nielsen S, Frokiaer J, Marples D, Kwon TH, Agre P, Knepper MA. Physiol Rev 82: 205-244, 2002; Kortenoeven ML, Fenton RA. Biochim Biophys Acta 1840: 1533-1549, 2014; Bockenhauer D, Bichet DG. Nat Rev Nephrol 11: 576-588, 2015; Pearce D, Soundararajan R, Trimpert C, Kashlan OB, Deen PM, Kohan DE. Clin J Am Soc Nephrol 10: 135-146, 2015). Instead, another CREB-like TF, Atf1, ranked fourth among all TFs. RNA sequencing time-course experiments showed a rapid increase in Aqp2 mRNA, within 3 h of vasopressin exposure. This response was matched by an equally rapid increase in the abundance of the mRNA coding for Cebpb, which we have shown by chromatin immunoprecipitation-sequencing studies to bind downstream from the Aqp2 gene. The identified TFs provide a roadmap for future studies to understand regulation of Aqp2 gene expression.NEW & NOTEWORTHY Abetted by the advent of systems biology-based ("-omics") techniques in the 21st century, there has been a massive expansion of published data relevant to virtually every physiological question. The authors have developed a large-scale data integration approach based on the application of Bayes'' theorem. In the current work, they integrated 12 different -omics data sets to identify the transcription factors most likely to mediate vasopressin-dependent regulation of transcription of the aquaporin-2 gene.

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.

Deep Proteomic Deconvolution of Interferons and HBV Transfection Effects on a Hepatoblastoma Cell Line

Interferons are commonly utilized in the treatment of chronic hepatitis B virus (HBV) infection but are not effective for all patients. A deep understanding of the limitations of interferon treatment requires delineation of its activity at multiple "omic" levels. While myriad studies have characterized the transcriptomic effects of interferon treatment, surprisingly, few have examined interferon-induced effects at the proteomic level. To remedy this paucity, we stimulated HepG2 cells with both IFN-α and IFN-λ and performed proteomic analysis versus unstimulated cells. Alongside, we examined the effects of HBV transfection in the same cell line, reasoning that parallel IFN and HBV analysis might allow determination of cases where HBV transfection counters the effects of interferons. More than 6000 proteins were identified, with multiple replicates allowing for differential expression analysis at high confidence. Drawing on a compendium of transcriptomic data, as well as proteomic half-life data, we suggest means by which transcriptomic results diverge from our proteomic results. We also invoke a recent multiomic study of HBV-related hepatocarcinoma (HCC), showing that despite HBV's role in initiating HCC, the regulated proteomic landscapes of HBV transfection and HCC do not strongly align. Special focus is applied to the proteasome, with numerous components divergently altered under IFN and HBV-transfection conditions. We also examine alterations of other protein groups relevant to HLA complex peptide display, unveiling intriguing alterations in a number of ubiquitin ligases. Finally, we invoke genome-scale metabolic modeling to predict relevant alterations to the metabolic landscape under experimental conditions. Our data should be useful as a resource for interferon and HBV researchers.

Genome-Wide Mapping of DNA Accessibility and Binding Sites for CREB and C/EBPβ in Vasopressin-Sensitive Collecting Duct Cells

Background Renal water excretion is controlled by vasopressin, in part through regulation of the transcription of the aquaporin-2 gene (Aqp2).Methods To identify enhancer regions likely to be involved in the regulation of Aqp2 and other principal cell-specific genes, we used several next generation DNA-sequencing techniques in a well characterized cultured cell model of collecting duct principal cells (mpkCCD). To locate enhancers, we performed the assay for transposase-accessible chromatin using sequencing (ATAC-Seq) to identify accessible regions of DNA and integrated the data with data generated by chromatin immunoprecipitation followed by next generation DNA-sequencing (ChIP-Seq) for CCCTC binding factor (CTCF) binding, histone H3 lysine-27 acetylation, and RNA polymerase II.Results We identified two high-probability enhancers centered 81 kb upstream and 5.8 kb downstream from the Aqp2 transcriptional start site. Motif analysis of these regions and the Aqp2 promoter identified several potential transcription factor binding sites, including sites for two b-ZIP transcription factors: CCAAT/enhancer binding protein-β (C/EBPβ) and cAMP-responsive element binding protein (CREB). To identify genomic binding sites for both, we conducted ChIP-Seq using well characterized antibodies. In the presence of vasopressin, C/EBPβ, a pioneer transcription factor critical to cell-specific gene expression, bound strongly at the identified enhancer downstream from Aqp2 However, over multiple replicates, we found no detectable CREB binding sites within 390 kb of Aqp2 Thus, any role for CREB in the regulation of Aqp2 gene transcription is likely to be indirect.Conclusions The analysis identified two enhancer regions pertinent to transcriptional regulation of the Aqp2 gene and showed C/EBPβ (but not CREB) binding.

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.