Comprehensive analysis of the mouse renal cortex using two-dimensional HPLC - tandem mass spectrometry

Glutathione metabolism rewiring protects renal tubule cells against cisplatin-induced apoptosis and ferroptosis

Renal proximal tubular cells are highly vulnerable to different types of assaults during filtration and reabsorption, leading to acute renal dysfunction and eventual chronic kidney diseases (CKD). The chemotherapeutic drug cisplatin elicits cytotoxicity causing renal tubular cell death, but its executing mechanisms of action are versatile and elusive. Here, we show that cisplatin induces renal tubular cell apoptosis and ferroptosis by disrupting glutathione (GSH) metabolism. Upon cisplatin treatment, GSH metabolism is impaired leading to GSH depletion as well as the execution of mitochondria-mediated apoptosis and lipid oxidation-related ferroptosis through activating IL6/JAK/STAT3 signaling. Inhibition of JAK/STAT3 signaling reversed cell apoptosis and ferroptosis in response to cisplatin induction. Using a cisplatin-induced acute kidney injury (CAKI) mouse model, we found that inhibition of JAK/STAT3 significantly mitigates cisplatin nephrotoxicity with a reduced level of serum BUN and creatinine as well as proximal tubular distortion. In addition, the GSH booster baicalein also reclaims cisplatin-induced renal tubular cell apoptosis and ferroptosis as well as the in vivo nephrotoxicity. In conclusion, cisplatin disrupts glutathione metabolism, leading to renal tubular cell apoptosis and ferroptosis. Rewiring glutathione metabolism represents a promising strategy for combating cisplatin nephrotoxicity.

Mitogen-Activated Protein Kinase 14 Promotes AKI

An improved understanding of pathogenic pathways in AKI may identify novel therapeutic approaches. Previously, we conducted unbiased liquid chromatography-tandem mass spectrometry-based protein expression profiling of the renal proteome in mice with acute folate nephropathy. Here, analysis of the dataset identified enrichment of pathways involving NFκB in the kidney cortex, and a targeted data mining approach identified components of the noncanonical NFκB pathway, including the upstream kinase mitogen-activated protein kinase kinase kinase 14 (MAP3K14), the NFκB DNA binding heterodimer RelB/NFκB2, and proteins involved in NFκB2 p100 ubiquitination and proteasomal processing to p52, as upregulated. Immunohistochemistry localized MAP3K14 expression to tubular cells in acute folate nephropathy and human AKI. In vivo, kidney expression levels of NFκB2 p100 and p52 increased rapidly after folic acid injection, as did DNA binding of RelB and NFκB2, detected in nuclei isolated from the kidneys. Compared with wild-type mice, MAP3K14 activity-deficient aly/aly (MAP3K14^aly/aly) mice had less kidney dysfunction, inflammation, and apoptosis in acute folate nephropathy and less kidney dysfunction and a lower mortality rate in cisplatin-induced AKI. The exchange of bone marrow between wild-type and MAP3K14^aly/aly mice did not affect the survival rate of either group after folic acid injection. In cultured tubular cells, MAP3K14 small interfering RNA targeting decreased inflammation and cell death. Additionally, cell culture and in vivo studies identified the chemokines MCP-1, RANTES, and CXCL10 as MAP3K14 targets in tubular cells. In conclusion, MAP3K14 promotes kidney injury through promotion of inflammation and cell death and is a promising novel therapeutic target.

Integrative proteomic analysis reveals reprograming tumor necrosis factor signaling in epithelial mesenchymal transition

The airway epithelium is a semi-impermeable barrier whose disruption by growth factor reprogramming is associated with chronic airway diseases of humans. Transforming growth factor beta (TGFβ)-induced epithelial mesenchymal transition (EMT) plays important roles in airway remodeling characteristic of idiopathic lung fibrosis, asthma and chronic obstructive pulmonary disease (COPD). Inflammation of the airways leads to airway injury and tumor necrosis factor alpha (TNFα) plays an important pro-inflammatory role. Little systematic information about the effects of EMT on TNFα signaling is available. Using an in vitro model of TGFβ-induced EMT in primary human small airway epithelial cells (hSAECs), we applied quantitative proteomics and phosphoprotein profiling to understand the molecular mechanism of EMT and the impact of EMT on innate inflammatory responses. We quantified 7925 proteins and 1348 phosphorylation sites by stable isotope labeling with iTRAQ technology. We found that cellular response to TNFα is cell state dependent and the relative TNFα response in mesenchymal state is highly compressed. Combined bioinformatics analyses of proteome and phosphoproteome indicate that the EMT state is associated with reprogramming of kinome, signaling cascade of upstream transcription regulators, phosphor-networks, and NF-κB dependent cell signaling.

BIOLOGICAL SIGNIFICANCE

Epithelial mesenchymal transition and inflammation have important implications for clinical and physiologic manifestations of chronic airway diseases such as severe asthma, COPD, and lung fibrosis. Little systematic information on the interplay between EMT and innate inflammation is available. This study combined quantitative proteomics and phosphorproteomics approach to obtain systems-level insight into the upstream transcription regulators involved in the TGFβ-induced EMT in primary human small airway epithelial cells and to elucidate how EMT impacts on the TNFα signaling pathways. The proteomics and phosphoproteomics analysis indicates that many signaling pathways involved in TGFβ-induced EMT and EMT has profound reprogramming effects on innate inflammation response.

Label-free quantitative shotgun proteomics using normalized spectral abundance factors

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

Detection of structural and metabolic changes in traumatically injured hippocampus by quantitative differential proteomics

Traumatic brain injury (TBI) is a complex and common problem resulting in the loss of cognitive function. In order to build a comprehensive knowledge base of the proteins that underlie these cognitive deficits, we employed unbiased quantitative mass spectrometry, proteomics, and bioinformatics to identify and quantify dysregulated proteins in the CA3 subregion of the hippocampus in the fluid percussion model of TBI in rats. Using stable isotope 18O-water differential labeling and multidimensional tandem liquid chromatography (LC)-MS/MS with high stringency statistical analyses and filtering, we identified and quantified 1002 common proteins, with 124 increased and 76 decreased. The ingenuity pathway analysis (IPA) bioinformatics tool identified that TBI had profound effects on downregulating global energy metabolism, including glycolysis, the Krebs cycle, and oxidative phosphorylation, as well as cellular structure and function. Widespread upregulation of actin-related cytoskeletal dynamics was also found. IPA indicated a common integrative signaling node, calcineurin B1 (CANB1, CaNBα, or PPP3R1), which was downregulated by TBI. Western blotting confirmed that the calcineurin regulatory subunit, CANB1, and its catalytic binding partner PP2BA, were decreased without changes in other calcineurin subunits. CANB1 plays a critical role in downregulated networks of calcium signaling and homeostasis through calmodulin and calmodulin-dependent kinase II to highly interconnected structural networks dominated by tubulins. This large-scale knowledge base lays the foundation for the identification of novel therapeutic targets for cognitive rescue in TBI.

Proteomics and systems biology for understanding diabetic nephropathy

Like many diseases, diabetic nephropathy is defined in a histopathological context and studied using reductionist approaches that attempt to ameliorate structural changes. Novel technologies in mass spectrometry-based proteomics have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure. The goal is to translate these new definitions into improved patient outcomes through diagnostic, prognostic, and therapeutic tools. Here, we review progress made in studying the proteomics of diabetic nephropathy and provide an introduction to the informatics tools used in the analysis of systems biology data, while pointing out statistical issues for consideration. Novel bioinformatics methods may increase biomarker identification, and other tools, including selective reaction monitoring, may hasten clinical validation.

Regular patterns for proteome-wide distribution of protein abundance across species

A proteome of the bio-entity, including cell, tissue, organ, and organism, consists of proteins of diverse abundance. The principle that determines the abundance of different proteins in a proteome is of fundamental significance for an understanding of the building blocks of the bio-entity. Here, we report three regular patterns in the proteome-wide distribution of protein abundance across species such as human, mouse, fly, worm, yeast, and bacteria: in most cases, protein abundance is positively correlated with the protein's origination time or sequence conservation during evolution; it is negatively correlated with the protein's domain number and positively correlated with domain coverage in protein structure, and the correlations became stronger during the course of evolution; protein abundance can be further stratified by the function of the protein, whereby proteins that act on material conversion and transportation (mass category) are more abundant than those that act on information modulation (information category). Thus, protein abundance is intrinsically related to the protein's inherent characters of evolution, structure, and function.

Less label, more free: approaches in label-free quantitative mass spectrometry

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.

Comparison of Programmatic Approaches for Efficient Accessing to mzML Files

The Human Proteome Organization (HUPO) Proteomics Standard Initiative has been tasked with developing file formats for storing raw data (mzML) and the results of spectral processing (protein identification and quantification) from proteomics experiments (mzIndentML). In order to fully characterize complex experiments, special data types have been designed. Standardized file formats will promote visualization, validation and dissemination of data independent of the vendor-specific binary data storage files. Innovative programmatic solutions for robust and efficient data access to standardized file formats will contribute to more rapid wide-scale acceptance of these file formats by the proteomics community.In this work, we compare algorithms for accessing spectral data in the mzML file format. As an XML file, mzML files allow efficient parsing of data structures when using XML-specific class types. These classes provide only sequential access to files. However, random access to spectral data is needed in many algorithmic applications for processing proteomics datasets. Here, we demonstrate implementation of memory streams to convert a sequential access into random access. Our application preserves the elegant XML parsing capabilities. Benchmarking file access times in sequential and random access modes show that while for small number of spectra the random access is more time efficient, when retrieving large number of spectra sequential access becomes more efficient. We also provide comparisons to other file accessing methods from academia and industry.

Using power spectrum analysis to evaluate (18)O-water labeling data acquired from low resolution mass spectrometers

We describe a method for ratio estimations in (18)O-water labeling experiments acquired from low resolution isotopically resolved data. The method is implemented in a software package specifically designed for use in experiments making use of zoom-scan mode data acquisition. Zoom-scan mode data allow commonly used ion trap mass spectrometers to attain isotopic resolution, which makes them amenable to use in labeling schemes such as (18)O-water labeling, but algorithms and software developed for high resolution instruments may not be appropriate for the lower resolution data acquired in zoom-scan mode. The use of power spectrum analysis is proposed as a general approach that may be uniquely suited to these data types. The software implementation uses a power spectrum to remove high-frequency noise and band-filter contributions from coeluting species of differing charge states. From the elemental composition of a peptide sequence, we generate theoretical isotope envelopes of heavy-light peptide pairs in five different ratios; these theoretical envelopes are correlated with the filtered experimental zoom scans. To automate peptide quantification in high-throughput experiments, we have implemented our approach in a computer program, MassXplorer. We demonstrate the application of MassXplorer to two model mixtures of known proteins and to a complex mixture of mouse kidney cortical extract. Comparison with another algorithm for ratio estimations demonstrates the increased precision and automation of MassXplorer.

Altered retinoic acid metabolism in diabetic mouse kidney identified by O isotopic labeling and 2D mass spectrometry

BACKGROUND

Numerous metabolic pathways have been implicated in diabetes-induced renal injury, yet few studies have utilized unbiased systems biology approaches for mapping the interconnectivity of diabetes-dysregulated proteins that are involved. We utilized a global, quantitative, differential proteomic approach to identify a novel retinoic acid hub in renal cortical protein networks dysregulated by type 2 diabetes.

METHODOLOGY/PRINCIPAL FINDINGS

Total proteins were extracted from renal cortex of control and db/db mice at 20 weeks of age (after 12 weeks of hyperglycemia in the diabetic mice). Following trypsinization, (18)O- and (16)O-labeled control and diabetic peptides, respectively, were pooled and separated by two dimensional liquid chromatography (strong cation exchange creating 60 fractions further separated by nano-HPLC), followed by peptide identification and quantification using mass spectrometry. Proteomic analysis identified 53 proteins with fold change >or=1.5 and p<or=0.05 after Benjamini-Hochberg adjustment (out of 1,806 proteins identified), including alcohol dehydrogenase (ADH) and retinaldehyde dehydrogenase (RALDH1/ALDH1A1). Ingenuity Pathway Analysis identified altered retinoic acid as a key signaling hub that was altered in the diabetic renal cortical proteome. Western blotting and real-time PCR confirmed diabetes-induced upregulation of RALDH1, which was localized by immunofluorescence predominantly to the proximal tubule in the diabetic renal cortex, while PCR confirmed the downregulation of ADH identified with mass spectrometry. Despite increased renal cortical tissue levels of retinol and RALDH1 in db/db versus control mice, all-trans-retinoic acid was significantly decreased in association with a significant decrease in PPARbeta/delta mRNA.

CONCLUSIONS/SIGNIFICANCE

Our results indicate that retinoic acid metabolism is significantly dysregulated in diabetic kidneys, and suggest that a shift in all-trans-retinoic acid metabolism is a novel feature in type 2 diabetic renal disease. Our observations provide novel insights into potential links between altered lipid metabolism and other gene networks controlled by retinoic acid in the diabetic kidney, and demonstrate the utility of using systems biology to gain new insights into diabetic nephropathy.

Proteome analysis of mouse model systems: A tool to model human disease and for the investigation of tissue-specific biology

The molecular dissections of the mechanistic pathways involved in human disease have always relied on the use of model organisms. Among the higher mammalian organisms, the laboratory mouse (Mus musculus) is the most widely used model. A large number of commercially-available, inbred strains are available to the community, including an ever growing collection of transgenic, knock-out, and disease models. Coupled to availability is the fact that animal colonies can be kept under standardized housing condition at most major universities and research institutes, with relative ease and cost efficiency (compared to larger vertebrates). As such, mouse models to study human biology and disease remains extremely attractive. In the current review we will provide an historic overview of the use of mouse models in proteome research with a focus on general tissue and organelle biology, comparative proteomics of human and mouse and the use of mouse models to study cardiac disease.

Quantitative mass spectrometry of diabetic kidney tubules identifies GRAP as a novel regulator of TGF-beta signaling

The aim of this study was to define novel mediators of tubule injury in diabetic kidney disease. For this, we used state-of-the-art proteomic methods combined with a label-free quantitative strategy to define protein expression differences in kidney tubules from transgenic OVE26 type 1 diabetic and control mice. The analysis was performed with diabetic samples that displayed a pro-fibrotic phenotype. We have identified 476 differentially expressed proteins. Bioinformatic analysis indicated several clusters of regulated proteins in relevant functional groups such as TGF-beta signaling, tight junction maintenance, oxidative stress, and glucose metabolism. Mass spectrometry detected expression changes of four physiologically relevant proteins were confirmed by immunoblot analysis. Of these, the Grb2-related adaptor protein (GRAP) was up-regulated in kidney tubules from diabetic mice and fibrotic kidneys from diabetic patients, and subsequently confirmed as a novel component of TGF-beta signaling in cultured human renal tubule cells. Thus, indicating a potential novel role for GRAP in TGF-beta-induced tubule injury in diabetic kidney disease. Although we targeted a specific disease, this approach offers a robust, high-sensitivity methodology that can be applied to the discovery of novel mediators for any experimental or disease condition.