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

Markus M Rinschen; Kavee Limbutara; Mark A Knepper; D Michael Payne; Trairak Pisitkun

doi:10.1152/physrev.00057.2017

. 2018 Sep 5;98(4):2571–2606. doi: 10.1152/physrev.00057.2017

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

Markus M Rinschen ¹, Kavee Limbutara ¹, Mark A Knepper ¹, D Michael Payne ¹, Trairak Pisitkun ¹

PMCID: PMC6335097 PMID: 30182799

Abstract

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.

I. FROM PHYSIOLOGY TO PROTEOMICS TO MECHANISMS

Life is dynamic. To survive, organisms must respond appropriately to fluctuating environments, both internal and external. These responses result from intricate coordinated changes in the quantities and activities of biomolecules within a living system, including proteins, DNA, RNA, lipids, and metabolites. These coordinated changes are orchestrated almost exclusively by the proteome, defined as the total repertoire of proteins (and their individual abundances) present within a cell/tissue/organism. Likewise, the observable phenotypes of any cell/tissue/organism are a direct manifestation of the respective proteomes. In humans, for example, the total number of protein-encoding genes is just over 20,000 (20,441 genes; http://www.ensembl.org/Homo_sapiens/Info/Annotation). However, almost 200,000 different gene transcripts (198,002) from the human genome have been identified, arising from processes like alternative mRNA splicing and encoding multiple isoforms of individual proteins. From such numbers, it would seem that the array of different combinations of proteins that could possibly be expressed in cells, and hence the array of different resulting cellular phenotypes, is essentially infinite. Surprisingly perhaps, this is not the case.

Adult humans (and other vertebrates) are comprised of a finite and rather limited number of distinct cell types, which can be distinguished on the basis of combined morphological, (ultra)structural, molecular biological and biochemical properties (6). In the most well-conceived and comprehensive analysis published to date, at least 411 distinct cell (pheno)types (and thus distinct proteomes) were identified for adult Homo sapiens (169). This number represented a minimum, due in part to the likelihood of incomplete identification of diversity among certain classes of cell types (primarily neurons, smooth muscle cells, and fibroblasts). A similar picture emerges from analysis at the organ system level: the adult human kidney, for example, contains at least 30 different cell types (5). The emerging science of single-cell “-omics” (genomics/epigenomics/transcriptomics/proteomics/metabolomics) holds the promise of an unbiased, high-resolution expanded classification of not only distinct cell (pheno)types, but also of stable cell states and even states of transition during dynamic responses to environmental changes or disease (68, 165). Nevertheless, even if the total number of unique cell types (i.e., unique proteomes) ultimately increases by an order of magnitude (or more), it will still represent a very small fraction of that which is theoretically possible. Thus, given the “relatively modest” number of distinct cell types and the rapid technological advances in high-throughput sequencing, proteomics, and bioinformatics, it seems possible that every distinct proteome defining its respective cellular phenotype could be fully characterized in the not-too-distant future.

In fact, two recent studies described attempts to identify every single protein corresponding to a protein-coding gene in the human body (85, 174). These draft “human tissue proteomes” resulted in a relatively comprehensive, albeit still incomplete (48), map of proteins obtained from nearly every human tissue. Despite the impressive successes of such modern systems biology studies, it should be pointed out that a significant fraction of the proteins (one-third or more) from the proteomes of every species still have no known functions (44a, 76, 116, 125). This situation represents a serious obstacle to the comprehensive classification of distinct cell types/states and full characterization of the respective proteomes that determine those phenotypes. But even more importantly, it is impossible to imagine how a complete understanding of physiological processes and regulation for any given species can be achieved in the absence of functional knowledge for more than one-third of that species’ proteome. Thus a global collaborative effort on functional studies of proteins at the proteome level is necessary to close the gap between breadth and depth of proteomic studies. One example of such a global project is The International Mouse Phenotyping Consortium, which began 7 yr ago with the goal of identifying the function of every mouse gene (using the C57BL/6 strain) through knockout technologies (https://www.mousephenotype.org) (39).

In the following sections, we summarize major aspects of 1) how protein functions are regulated at the molecular level, resulting in the orchestrated control of the internal milieu, and 2) the historical development of modern proteomic techniques for deciphering protein regulation and control of cellular physiology. Subsequently, we present a literature review in the field of kidney physiology to illustrate the use of proteomic technology for advancing knowledge in this field. Finally, we discuss important statistical and bioinformatics considerations related to appropriate interpretation of proteomics data.

A. Physiological Stimuli and Complex Dynamic Biological Responses

To understand how complex cellular processes are regulated at the molecular level, comprehensive systems biology approaches can be used for exploration of responses to specific stimuli. One such approach is proteomics, in which fundamental aspects affecting global protein function, abundance, posttranslational modifications (PTMs), protein intermolecular interactions, and localization, can be studied in an unbiased, large-scale manner (FIGURE 1).

FIGURE 1. — An overview of mechanisms regulating protein functions. Four major aspects controlling protein functions are illustrated. First, the abundance of any particular protein is tightly regulated and determined by the integration of transcriptional regulation, protein translation, and protein degradation processes. Second, numerous posttranslational modifications (PTMs) of proteins play major roles in controlling protein functions and subsequently help determine cellular responses to the changing environment. Acetylation, phosphorylation, and ubiquitinylation are depicted as examples of well-known PTMs. Third, protein intermolecular interactions, including protein-protein, protein-DNA/RNA, and protein-small molecule interactions, which define targets of protein functions, serve as another major regulatory point. Finally, the (re)localization of proteins to specific subcellular regions, such as nucleus, mitochondria, or plasma membrane, governs their appropriate site of action. See text for details.

1. Protein abundance

For any given protein, the total amount of that protein present under any specific conditions can affect its functional activities and the phenotype of its respective biological system. Thus, precise regulation of the copy number of proteins is essential. Proteins are continuously produced (via gene transcription and translation) and degraded in a regulated fashion (see FIGURE 1, top). The net result of these processes determines the abundance of any specific protein at any given time. It is worth noting that all of these processes (transcription, translation, and degradation) are themselves controlled by the activities of numerous proteins, the abundances of which are likewise regulated in a recursive fashion. The roles of multiple processes, in addition to transcriptional control (i.e., mRNA levels), in regulating protein abundance explain the discordant results between proteomics and transcriptomics studies in many instances (100). Therefore, the determination of protein abundance requires consideration of all of these mechanisms and is crucial for the accurate interpretation of quantitative proteomic analyses and ultimately for understanding the complex cellular physiology in response to specific stimuli.

2. Posttranslational modifications

Protein functions can be altered by either reversible or irreversible modifications to one or more of its constituent amino acids. These modifications lead to changes in the physicochemical, and ultimately biological, properties of proteins and greatly expand the combinatorial possibilities of protein structures beyond those afforded by the basic 20 amino acid building blocks. Currently, there are 480 different known protein modifications reported in the Uniprot database (9) (https://www.uniprot.org/docs/ptmlist). Some of these modifications have been extensively studied [e.g., phosphorylation (60), ubiquitinylation (166), and acetylation (32)], and their roles in physiological regulation in general are well understood (see FIGURE 1, bottom). Many other modifications, which have been difficult to study previously, including PTMs such as arginine methylation (94), succinylation (173), malonylation (178), and redox-mediated cysteine modifications such as glutathionylation and sulfenylation (58), are now gaining increased interest due to advances in the capabilities of proteomic strategies and technologies. Despite these advances, identification and study of the myriad possible modifications, which also often occur transiently and/or at extremely low levels, remain a major challenge to completely understanding the mechanism regulating cellular responses to any specific stimulus. Mass spectrometry-based proteomic analysis has provided a powerful and sensitive means for examining global changes in posttranslational protein modifications associated with physiological responses to alterations in the cellular environment.

3. Protein intermolecular interactions

Many different biomolecules are packed into the complex cellular and extracellular compartments of living systems. Such environments, unlike the purified protein system, promote physical interactions among proteins and other biomolecules such as DNAs, RNAs, small-molecule metabolites, as well as other proteins (see FIGURE 1, right). In fact, it is through these very interactions that the biological functions of proteins and their contributions to overall phenotype are manifested [with the possible exception of heretofore unproven quantum phenomena, which even Albert Einstein could not predict, calling them “spooky” (129)]. Changes in the conformational state of a protein (or even one of its subunits) induced by some stimulus may promote or disrupt these intermolecular interactions and, consequently, serve as a way to control functions of entire protein complexes. Stimuli that lead to derangements in some of these interactions could result in phenotypic changes associated with many diseases, including malignancy, autoimmune disorders, neurological disorders, and cardiovascular disease (cf. Refs. 97, 105). In any biological system, there is a vast array of protein intermolecular interactions occurring as spatially and temporally distinct, yet globally integrated, clusters, collectively referred to as the interactome. As will be illustrated in subsequent sections, mass spectrometry (MS)-based proteomics, in conjunction with immunoaffinity and/or other high-throughput techniques, have been invaluable for interactome analysis.

4. Protein localization

Yet another layer of complexity involving the contribution of protein functions to cellular phenotypes and physiology is regulation of the precise localization of proteins within appropriate cellular compartments (FIGURE 1, left). For many proteins, initial control of protein localization is directly encoded in their amino acid sequences (e.g., signal sequences and other targeting motifs) for directed transport into the correct compartment, such as nucleus, mitochondria, or endoplasmic reticulum (ER). To execute the appropriate physiological programs in response to stimuli, subsequent relocalization of proteins is regulated through many types of PTMs, altered interactions with other biomolecules, and physical movement via molecular motor-driven processes (15). Protein MS has been utilized to great effect in elucidating subcellular proteomes and mechanisms for regulating spatial (re)distribution of proteins as a function of physiological status.

B. Development of Modern High-Throughput Proteomics Techniques for Elucidating Physiological Mechanisms

Attempts to utilize MS for peptide/protein sequencing began as early as 1958; however, significant hurdles prevented routine sequence analysis, especially of complex samples, until the early 1990s (18). The most obvious question that arises regarding this topic is: “How is it even possible to sequence a protein using mass spectrometry?” First, the most fundamental concept related to this question is that every amino acid residue (except leucine and isoleucine) has a unique mass, and the mass spectrometer is the most accurate method for mass determination (technically, m/z, mass-to-charge ratio). Thus, if it were possible to fragment a peptide/protein randomly at each peptide bond, a statistical population representing every possible fragment would be produced. For this series of fragments, ordered by increasing or decreasing mass, the mass difference between any two consecutive fragments would specify the identity of the extra amino acid residue in the larger fragment (FIGURE 2). Sequence determination from such a series of ion fragments is called de novo sequencing. To measure mass (m/z), mass spectrometers require charged molecules (either positively or negatively charged ions) and a very high vacuum; in other words, the peptide/protein fragments must be ionized and in the gas phase. Ultimately, through the persistent systematic efforts of Klaus Biemann, Don Hunt, and their many talented coworkers and colleagues in this field, ionization and fragmentation methods were devised and subsequently refined for delivery of peptide/protein ions into the mass spectrometer and sequence determination. Another crucial development that dramatically accelerated progress in peptide/protein sequencing by MS was the coupling of multiple mass spectrometers, which greatly facilitated generation and detection of the requisite series of ions randomly fragmented at every peptide bond.

FIGURE 2. — Simplified fragmentation scheme and sequence determination for an example peptide using tandem mass spectrometry. Following ionization and introduction into the vacuum environment of the mass spectrometer system, protonation of the amide nitrogens in the peptide bonds occurs randomly across the population of gas-phase peptide molecules. The schematic structure for a singly-charged example peptide, protonated at the peptide bond between residues 3 and 4, is depicted at the top of the figure. Regardless of which peptide bond is protonated, the entire population of peptide molecules would be detected in the MS1 as the (M+H)⁺ precursor ion, with mass (*m/z*) 459. Subsequent transfer of the peptide precursor ions into the collision chamber results in random fragmentation at the peptide bonds (dashed lines), yielding two predominant series of fragment product ions (“b” and “y” ions, as described in the text). For the specific (M+H)⁺ precursor ion shown as an example in the figure, fragmentation at the indicated peptide bond would produce y₁ ions from molecules in which the positive charge remains with the amide nitrogen, or b₃ ions from molecules in which the positive charge segregates with the carbonyl carbon (forming an acylium ion) (*top*). Due to the random peptide bond protonation and fragmentation, a statistical population of all possible y and b ion pairs (*top*) is generated in the collision chamber. Subsequent analysis of these fragment product ions in the MS2 results in the MS2 spectrum depicted in the lower panel. Subtraction of *m/z* values for any two consecutive ions in the same series (either “y” or “b”) provides the residue mass (and hence, the identity) of the extra amino acid present in the larger fragment ion. In the example shown, (y₃ – y₂) = (402 – 288) = 114, identifying asparagine as the NH₂-terminal residue in y₃; similarly, (b₃ – b₂) = (285 – 172) = 113, identifying either leucine or isoleucine as the COOH-terminal residue in b₃, and so forth. Ultimately, this process (called de novo sequencing) allows deduction of the complete sequence of the original peptide precursor ion. Notes: the diagrams and explanation presented here have been simplified somewhat: 1) for instance, using modern ionization methods and instrumentation, typically the peptide precursor ions (and fragment product ions) are multiply protonated, at the amide nitrogens in the peptide bonds plus the NH₂-terminal amine group and/or the R groups of lysine/arginine; 2) as another example, the acylium ion at the COOH-terminus of b ions may undergo a structural rearrangement(s) [e.g., to form a cyclic oxazolone (25, 61, 134)]. Despite this additional “complexity” of multiple charge states and multiple possible structures for b ions, the actual masses of the peptide precursor and product ions are perfectly conserved, and thus the simplified conceptual description presented here is still completely valid.

In this tandem mass spectrometry (MS/MS) configuration, a peptide mass (m/z) can be measured in the first mass spectrometer (MS1), and this precursor ion can then be passed into a collision chamber (containing an optimal level of inert gas) to induce fragmentation at the weakest bonds (i.e., the peptide bonds). The series of fragment product ions generated by collision with the gas molecules [collision-induced dissociation (CID)] is subsequently passed into a second mass spectrometer (MS2) for m/z measurement of each product ion. During the evolution of MS protein sequencing technology, it was discovered that, not one, but two predominant sets of fragments are actually generated by CID, namely “b ions” (containing the NH₂-terminal amino acid plus 1, 2, 3, etc., aa residues) and “y ions” (containing the COOH-terminal amino acid plus 1, 2, 3, etc., aa residues) (FIGURE 2). For a more complete overview of the principles of MS-based proteomics in general, the reader is directed to some recent reviews (12, 126, 152, 154).

More recently, numerous refinements and innovations have led to the current state of the art in high-throughput proteomics analysis. First, several new developments in sample preparation have aimed to streamline multiple sample preparation steps within an easily scalable platform, thus allowing rapid, parallel sample preparation (46, 74, 175). Second, novel liquid chromatographic techniques also have helped increase the number and reliability of peptide/protein identifications and quantification by implementing orthogonal strategies for prefractionation and/or enrichment while simultaneously minimizing sample loss (90, 150, 181). Third, advancements in ionization methods, fragmentation methods, and instrumentation have all dramatically enhanced modern proteomic analyses. The two most widely used advanced ionization methods are MALDI (matrix-assisted laser dissociation ionization) (81, 158) and ESI (electrospray ionization) (49). Although MALDI still has important applications in protein imaging (56, 88), the innovative coupling of microcolumn liquid chromatography (nano-LC) to ESI-MS/MS (75) provided an optimal platform that is still the basis for most high-throughput proteomic analyses. Additionally, new methods of fragmentation (such as higher-energy C-trap dissociation and electron transfer dissociation) have extended the sensitivity and range of proteomic analyses (122, 156). Finally, MS instrumentation itself has rapidly improved. New types of mass spectrometers have been developed, including the ion trap, as well as hybrid tandem mass spectrometers comprised of multiple types, most notably the “orbitrap” (a hybrid between ion trap and Fourier-transform ion cyclotron resonance mass spectrometers) (72). Development and coupling of additional robust automation technologies with the advances described above is expected to further increase throughput and reduce human errors, thereby paving the way toward even more routine comprehensive proteome characterization in the future.

The workflow for modern MS-based proteomic analyses is supported by three “pillars”: biochemical sample preparation, MS technique, and bioinformatics processing of the acquired data (including peptide matching algorithms for sequence determination, software for identification and quantification of proteins and PTMs, as well as tools for identifying functional molecular networks). These three areas are highly interrelated, and advances in one area have often inspired progress in the other. More details describing proteomic workflow, data acquisition, and peptide/protein quantification techniques are provided in Sect. II, additional concepts for understanding ms-based proteomics, below. Further reviews on MS principles and technical topics can be found elsewhere (3, 8, 12, 154).

To reach the ultimate goal of complete understanding of physiological mechanisms, however, further advances in integrating proteomics with other “omics” types of studies, such as genomics (“proteogenomics”), transcriptomics, and metabolomics, are occurring (7). Relatively few contemporary proteomic studies currently reach this level of integration, and even fewer such studies involve relevant experimental physiological settings or relate the findings to actual physiological functions within the same sample or system. In this regard, renal proteomics may serve as an example of how proteome studies, integrated with comprehensive physiological and systems biology approaches, can be utilized to significantly advance a research field for a better understanding of complex dynamic biological mechanisms (reviewed in Sect. III, application of functional proteomics to renal tubule physiology).

II. ADDITIONAL CONCEPTS FOR UNDERSTANDING MS-BASED PROTEOMICS

A. Essential Workflow for Typical High-Throughput MS-Based Proteomics

In a typical proteomic workflow (see FIGURE 3), starting with biochemical extraction from a cellular protein source of interest, the complex protein mixture is first subjected to proteolytic digestion (FIGURE 3A, top), a so-called bottom-up or shotgun proteomic strategy (in contrast to the less frequently utilized top-down approach, which analyzes the intact proteins). Prefractionation and/or enrichment could be performed either before or after proteolytic digestion. Prefractionation procedures include electrophoresis or chromatography [e.g., ion exchange, high-pH reversed-phase liquid chromatography (RPLC), or hydrophilic interaction liquid chromatography (LC)]. Enrichment procedures are mainly affinity based, including mono-specific interactions (e.g., antibodies) or group-specific interactions [e.g., immobilized metal affinity chromatography (IMAC) and metal oxide affinity chromatography for phosphoprotein/peptide enrichment]. The obvious advantage of prefractionation and/or enrichment is that it reduces sample complexity, thereby permitting an overall increase in the number of identified peptides and proteins and an increased detection of low-abundance peptide species (especially those containing PTMs). To eliminate interfering substances from samples before MS analysis, clean-up methods like StageTips, a C18-resin-based method, are usually executed (136). Subsequently, peptide mixtures are subjected to nano-flow RPLC, and the eluted peptides are directly ionized by ESI and introduced into the first mass analyzer for survey mass analysis (MS1) (FIGURE 3A, middle). The most abundant precursor peptides ion(s) eluting at a particular retention time (e.g., the precursor peptide ion with m/z = 859.9, illustrated as a green peak in FIGURE 3A, middle) is (are) automatically selected for fragmentation by CID and subsequently transferred to the second mass analyzer for m/z analysis of fragment product ions (MS2) (FIGURE 3A, bottom). This method for selecting precursor peptides to be fragmented is the most common and is called data-dependent acquisition (DDA) (see next section for additional details and descriptions of other methods).

A typical high-throughput proteomic analysis results in the generation of a vast number of MS2 fragmentation spectra (typically 25,000 spectra per 90-min LC-MS/MS run), prohibiting manual interpretation of sequence data. As an alternative to de novo sequencing (as described in the sect. IB), many automated search engines have been developed to permit sequence determination based on fragment product ion spectral matching. SEQUEST was the first such search algorithm (45), followed later by release of several other widely distributed search algorithms (34–36, 55, 128). These automated approaches all consist of three major components: 1) a proteome database; 2) a list of all predicted peptides and their m/z values following in silico proteolytic digestion; and 3) a peptide-spectral matching algorithm (FIGURE 3B). An ideal proteome database should contain all protein sequences from the species of interest. Consequently, comprehensive proteomics is possible nowadays only after the completion of genome sequencing for the experimental species being investigated (33, 155). Currently, well-accepted, open-access proteome databases include the National Center for Biotechnology Information Reference Sequence Database (RefSeq) (121), the Universal Protein Resource (UniProt) (161), and the Ensembl Project (4). All species-specific protein sequences in a given database are subsequently processed in silico to simulate the proteolytic digestion performed in the actual complex protein mixture being analyzed. This process results in a complete computer-generated list containing all predicted peptides and their respective masses (m/z) that would be produced following cleavage of all proteins in the database with a specific protease. In silico digested peptides with m/z values approximately equal to each detected precursor ion m/z value are selected as candidate sequences, and then theoretical MS2 spectra of these sequences are generated. As illustrated in FIGURE 3B, using the “green” precursor peptide ion with m/z 859.9 (from FIGURE 3A, middle) as an example, three candidate peptide sequences with essentially identical m/z values are identified from the complete list of in silico digested peptides. Matching between the observed MS2 spectrum and theoretical MS2 spectra of the candidate sequences is then performed (a process called “peptide-spectrum matching”) based on various scoring schemes to assess matching quality (106, 168). In the example shown, the second candidate peptide is identified with the highest probability, based on the degree of matching between the actual and predicted MS2 spectra (e.g., 12 of 13 predicted y ions and 6 of 13 predicted b ions detected in the actual MS2 spectrum of the example precursor peptide ion) (FIGURE 3, A AND B, bottom).

B. Data Acquisition Techniques for MS-Based Proteomics

Among the various factors influencing results of protein/peptide sequencing by MS, data acquisition techniques have been a major focus in the field; differences among these techniques can lead to dramatically different results in magnitude, depth, and overall reproducibility of peptide identifications, as well as precision of peptide quantification. These data acquisition techniques include DDA, data-independent acquisition (DIA), and targeted strategies. The choice among these options must be tailored to fit the experimental question being addressed (71).

The DDA strategy is typically used in discovery proteomics studies. In this technique, from the group of precursor ions present in an MS1 spectrum, the selection of a particular ion for further fragmentation is “dependent” on predetermined rules that are applied at the MS1 level. FIGURE 4A shows the commonly used rule for precursor ion selection, which is based on the relative signal intensity (among all precursor ions in a particular MS1 spectrum). In the example shown, the red ion is selected for fragmentation followed by an MS2 scan. Generally, a cycle of MS/MS analysis consists of an MS1 scan of the sample peptide mixture, followed by a rapid sequence of MS2 scans of the 10 most abundant precursor ions. Although the MS approach using DDA has an undemanding data analysis pipeline, and its instrumentation is easy to set up, the data-dependent nature of this approach obviously limits both completeness and reproducibility of peptide identification (i.e., not every precursor ion present in the sample is selected for fragmentation in each run, nor are the same precursor ions consistently selected for fragmentation between different runs).

To overcome the limitations of DDA, the DIA strategy has been developed to acquire the entire range of precursor ions (FIGURE 4B). The most well-known DIA technique called SWATH (sequential window acquisition of all theoretical fragment-ion spectra) divides an entire MS1 mass window into smaller consecutive mass windows (e.g., windows of 25 m/z units wide, as shown in FIGURE 4B, left) (57). All precursor ions in each mass window are isolated, fragmented, and analyzed simultaneously, creating a complex MS2 spectrum. This process is sequentially performed across the entire mass range, systematically collecting MS2 data from all detected precursors. The resulting fragment spectra are more complex than those obtained by DDA technique and require specialized algorithms to identify and quantify peptide sequences.

Another strategy developed to circumvent the reproducibility issue of the DDA technique is the so-called targeted strategy (FIGURE 4C) (54, 131). In contrast to DDA, the acquisition process for the targeted strategy is controlled by a predefined list of peptide masses of interest, thereby increasing the consistency of identifying the targeted peptides between experiments. A prior knowledge and selection of specific peptide masses (and hence, proteins) of interest is required for the utilization of this strategy, a task often completed using spectral libraries. Two common targeted methods are illustrated in FIGURE 4C. In the selected reaction monitoring method, a set of transition pairs (each pair consisting of m/z values for the selected precursor and one of its fragment ions) for each target is required for the setup; ideally, the expected chromatographic retention time of each transition pair can also be specified. During MS, the acquisition for each transition pair begins with an isolation of the pre-specified precursor ion using a very narrowly selective mass filter. After fragmentation, a second mass filter is applied on the resulting fragment ions to select for the pre-specified fragment ion. Alternatively, the parallel reaction monitoring method requires only a list of preselected precursor ion m/z values (plus optional expected retention times). The acquisition for each pre-specified precursor ion is similar to selected reaction monitoring method; however, after fragmentation, a full MS2 scan is performed for each targeted precursor ion. Targeted proteomics is reproducible and is robust even in cross-laboratory comparisons (1).

C. An Overview of Protein/Peptide Quantification Methods for MS-Based Proteomics

To elucidate complex dynamic biological responses to physiological stimuli, it is crucial to quantify various parameters of proteins, including abundance, PTMs, intermolecular interactions, and localization in a large-scale fashion, as explained in the first section of this review. The ultimate goal of quantitative proteomics analysis is to determine the copy number of every protein in samples, also known as “absolute quantification.” However, this goal is currently difficult to achieve in a high-throughput manner. More commonly, “relative quantification” techniques, which measure the amount of a particular protein in one condition relative to another condition, have been successfully utilized in numerous proteomic studies. Thus we will discuss the principle of relative quantification in proteomics in the following paragraphs.

Two strategies could be used for the relative quantification in MS-based proteomics studies: 1) stable isotopic labeling methods, and 2) label-free methods. For stable isotopic labeling methods, various combinations of heavy isotopes (such as deuterium, carbon-13, nitrogen-15, and oxygen-18) are employed to create predictable mass shifts of peptides, while preserving other physicochemical properties, either before or after proteolytic digestion. The labeling of each sample with a unique isotope combination allows mixing of samples from different conditions because the same peptide from each sample has a distinguishable mass observed in LC-MS/MS analysis. This technique eliminates the variation between each LC-MS/MS run and allows more accurate relative quantification. Several labeling reagents have been developed for quantitative proteomics including SILAC (stable isotope labeling by amino acid in cell culture) (123), dimethyl labeling (70), and isobaric labeling [such as tandem mass tag (TMT), (162) and isobaric tags for relative and absolute quantitation (iTRAQ) (143)]. FIGURE 5A illustrates examples of different stable isotopic labeling methods. In SILAC, special amino acids containing heavy isotopes are metabolically incorporated into proteins of each sample. Labeled samples are then mixed, processed, and subjected to LC-MS/MS analysis, thus minimizing variation from sample preparation procedures. Quantitative data could then be obtained from MS1 spectra based on relative ion intensity of light and heavy forms of individual peptides (FIGURE 5A, top right). On the other hand, dimethyl labeling and isobaric labeling employ chemical reactions at the peptide level to “mark” individual peptides from each sample at the peptide level. The different mass shifts between light, medium, and heavy dimethyl-labeled peptides in MS1 spectra allows comparison of precursor ion intensities for an individual peptide sequence between each sample (FIGURE 5A, middle right), similar to the SILAC method. Alternatively, isobaric labeling uses a set of special chemical tags that have exactly the same mass (i.e., isobaric), therefore producing sample-inseparable MS1 peaks. On fragmentation in the mass spectrometer, these isobaric tags are designed to generate mass-differing reporter product ions for which MS2 intensities are quantified (FIGURE 5A, bottom right). This strategy currently permits highly multiplexed proteomics analyses for up to 11 samples simultaneously (and potentially even more as new higher-resolution individual mass tags become available).

FIGURE 5. — Quantification methods for mass spectrometry (MS)-based proteomics. A: label-based quantification methods incorporate various labeling molecules with different isotopic mass properties into each sample. These techniques can be performed at either the protein or peptide level. The mass difference of labeled peptides is distinguishable by mass spectrometry, enabling the mixing of multiple samples for the same liquid chromatography-tandem mass spectrometry (LC-MS/MS) run. B: diagrams illustrate the principle of an extracted ion chromatogram (XIC). This reconstructed curve integrates ion intensity information from multiple spectra across the dimension of time [i.e., “elution time” or “retention time” (RT)] for a specific peptide. The area under an XIC curve can be used as a measure of abundance for that specific ion (i.e., peptide). C: diagrams illustrate a comparison of label-free vs. stable isotope-based quantification for two different samples (e.g., replicates, or control and experimentally treated samples). While sample preparation is much simpler for label-free methods, stable isotope-based methods reduce the total number of LC-MS/MS analyses required, as well as eliminating (or reducing) chromatographic variation between runs.

An additional factor complicating quantification in MS-based proteomics is the fact that individual peptides obviously do not elute instantaneously from the chromatography column; instead, peptides elute as “peaks” over a finite time interval. Thus measuring the amount of an individual peptide actually requires integration of its ion intensities over the duration of its chromatographic elution time [also called “retention time” (RT)]. The resulting chromatogram is called an extracted ion chromatogram (XIC) (115). FIGURE 5B illustrates the XIC reconstruction process for one precursor ion with m/z value m₁ from six sequential MS1 spectra. This concept could be applied to both MS1 and MS2 level quantification techniques.

The label-free quantification approach compares the abundance of a particular precursor ion from one LC-MS/MS run to the corresponding ion from another run. The ease of sample preparation is clearly an advantage of the label-free approach. In addition, without any labeling reagent requirement, a limitless number of conditions can be compared. A diagram illustrating the concept of label-free quantification, in comparison to stable isotope-based quantification, is shown in FIGURE 5C. Two XICs of a precursor ion with m/z value m₁ from LC-MS/MS run numbers 1 and 2 are compared for the label-free method (FIGURE 5C, left). Some degree of retention time shift can be observed in FIGURE 5C, demonstrating the expected run-to-run chromatographic variation. In contrast, label-based quantification allows simultaneous comparison of each XIC from multiple samples within a single LC-MS/MS run, thus eliminating the variation between runs (FIGURE 5C, right).

III. APPLICATION OF FUNCTIONAL PROTEOMICS TO RENAL TUBULE PHYSIOLOGY

The human kidney consists of ~1 million functional units, called nephrons (and the associated collecting ducts) (FIGURE 6). The nephron consists of a glomerulus, where the primary urine is filtered, and the renal tubules, with specialized “segments” organized in series. The nephron is followed by the collecting duct system, which carries out fine regulation of urine volume and composition. The renal tubule is responsible for multiple transport processes and consists of four major specialized segments: proximal tubule, loop of Henle, distal convoluted tubule (DCT), and collecting duct. When the primary urine flows through this system, solutes, metabolites, and electrolytes are reabsorbed or secreted, and these processes require tight regulation to maintain body homeostasis. To this end, different tubule epithelia express specific transporters, channels, and pumps that are distributed with segment-specific localization patterns, often in a polarized manner, along the tubule. Collectively, the regulation of these transport proteins is essential for maintaining body homeostasis in response to changing environmental conditions. Activities of these transport proteins are tightly controlled by gene expression, PTMs, and/or protein turnover. In the past decade, significant insights into the regulation of these proteins have been generated chiefly by applying MS-based proteomic analyses to renal tubule epithelia. The aim of this review section is to provide a systematic overview of insights into these processes obtained from proteomics-driven studies. FIGURE 6 shows the major physiological processes examined in the reviewed proteomic studies of the four major renal tubule segments. As can be seen in FIGURE 7A, the number of publications in this field has significantly increased since 2001, the year the human genome project was completed (92). Proteomic studies focused on the various kidney segments and urine have been performed (FIGURE 7B). To systematically summarize the studies involving renal tubule physiology, we grouped the studies according to the main renal segments examined (proximal tubule, the loop of Henle, distal tubule, or collecting duct). Some of these studies were selected for in-depth analysis in this review to highlight technological or biological advancements of our knowledge of kidney physiology. In each section, we first review the sample preparation protocols. Then we review profiling studies (which are not quantitative studies per se), followed by quantitative studies in response to physiological stimuli.

FIGURE 7. — Analysis of kidney-related proteomic studies by category. A: the number of kidney-related proteomic studies appearing in each publication year. This analysis illustrates the rapid increase in proteomics research activity that coincided with completion of the human genome project. B: the distribution among kidney-related proteomic studies for the different anatomical components of the kidney (and urine). DT, distal tubules; mTAL, medullary thick ascending limb; PT, proximal tubules.

A. Response to Metabolic Acidosis and Angiotensin in the Proximal Tubule

1. Isolation

The proximal tubule is the renal tubule segment that reabsorbs the majority of solutes from the primary urine. The isolation of proximal tubules from native kidneys has been performed in many renal physiological studies and modelling. One of the commonly used isolation methods is Percoll density gradient centrifugation, in which the renal cortical tissue is removed by dissection and digested with collagenase to release the cortical tubules, which are then resuspended in a 42.5–45% Percoll solution and centrifuged. Approximately 95% purity of proximal tubules can be obtained with this method (28, 38, 52, 53, 99, 148, 170, 171). Recently, a rapid immunofluorescence staining technique coupled with laser microdissection was successfully used for isolating proximal tubule segments from mouse kidney tissues (111). Since proximal tubule cells are highly polarized epithelial cells expressing many solute transporters along their apical brush-border plasma membrane, the isolation of this membrane compartment has played a significant role in many earlier physiological studies of proximal tubules. To isolate brush-border membrane vesicles (BBMVs) from proximal tubules, the MgCl₂ precipitation method has been used as a standard technique (16). Feric et al. (51) and Walmsley et al. (170, 171) applied this technique to obtain a highly enriched BBMV preparation for studying the BBMV proteome from rat proximal tubules. The mitochondrion is another subcellular fraction in proximal tubule cells that has been studied to a great extent because of its crucial role in the many active transport processes occurring in these cells. To enable more in-depth proteomic studies of this organelle, mitochondrial isolation from proximal tubule cells has been accomplished using differential and sucrose density centrifugation (52, 53).

2. Profiling

Several studies have been performed to reveal the proteome and subproteome of proximal tubule cells [including apical membrane (BBMV), mitochondrial, and cytosolic fractions] (53, 148, 171). These results were combined and deposited in a publicly accessible online database (https://hpcwebapps.cit.nih.gov/ESBL/Database/PCT/), which currently contains information for 1,660 proteins. To demonstrate the utility of this data set, Table 1 lists apical membrane transporters (identified via BBMV isolation) extracted from this database and sorted based on their abundance levels (calculated by normalized spectral counting). As expected based on previous knowledge in the renal physiology field, transport proteins for solutes, and water were the most abundant proteins detected in the brush border membrane. In addition, the observed order of relative abundance for specific transport proteins (neutral amino acids > basic amino acids > water > sodium-glucose > sodium-bicarbonate > sodium-phosphate > other solutes) provides additional insights that can be useful for further physiological studies and modeling. Feric et al. (51) carried out shotgun phospho-proteomic analysis of proximal tubule membrane proteins from normal rats, reporting 743 phospho-peptides, many of them novel and many of them in key transporter proteins of the proximal tubule. For example, two novel sites were identified in the COOH-terminal tail of Na-Pi2 (Slc34a1) and in various domains in the Na/H exchanger (NHE3) (Slc9a3).

Table 1.

A list of apical membrane transporters reported in a proximal tubular cell proteome database

Accession No.	Gene Name	Protein Name	Transporter Activity	Normalized Spectral Counts
Q794F9	Slc3a2	Solute carrier family 3 (amino acid transporter heavy chain), member 2	Neutral amino acid	85.39
Q64319	Slc3a1	Solute carrier family 3 (amino acid transporter heavy chain), member 1	l-cystine; amino acid; basic amino acid	43.44
P29975	Aqp1	Aquaporin 1	Ammonium; carbon dioxide; glycerol; nitric oxide; potassium ion; water	35.95
P53792	Slc5a2	Solute carrier family 5 (sodium/glucose cotransporter), member 2	Glucose-sodium symporter	32.34
Q9JI66	Slc4a4	Solute carrier family 4, sodium bicarbonate cotransporter, member 4	Sodium-bicarbonate symporter	22.24
Q2A865	Slc6a19	Solute carrier family 6 (neutral amino acid transporter), member 19	Neurotransmitter-sodium symporter; neutral amino acid	20.50
Q06496	Slc34a1	Solute carrier family 34 (type II sodium/phosphate cotransporter), member 1	Sodium-phosphate symporter	17.27
P51907	Slc1a1	Solute carrier family 1 (neuronal/epithelial high affinity glutamate transporter, system Xag), member 1	l-glutamate; cysteine; glutamate-sodium symporter; sodium-dicarboxylate symporter	12.10
D3Z9E5	Slc5a8	Solute carrier family 5 (sodium/monocarboxylate cotransporter), member 8	Sodium-monocarboxylate symporter	9.28
Q9R1U7	Slc22a8	Solute carrier family 22 (organic anion transporter), member 8	Organic anion; quaternary ammonium group	6.85
B1WBS5	Slc5a10	Solute carrier family 5 (sodium/sugar cotransporter), member 10	Sodium/glucose cotransporter	6.16
P28570	Slc6a8	Solute carrier family 6 (neurotransmitter transporter), member 8	Choline; creatine; creatine-sodium symporter; neurotransmitter-sodium symporter	5.78
Q8K4R8	Slc34a3	Solute carrier family 34 (type II sodium/phosphate cotransporter), member 3	Sodium-phosphate symporter	5.54
Q62687	Slc6a18	Solute carrier family 6 (neutral amino acid transporter), member 18	Amino acid; neurotransmitter-sodium symporter	4.88
Q80WK7	Slc29a3	Solute carrier family 29 (equilibrative nucleoside transporter), member 3	Nucleoside	4.21
P53790	Slc5a1	Solute carrier family 5 (sodium/glucose cotransporter), member 1	Glucose; glucose-sodium symporter	4.02
Q63089	Slc22a1	Solute carrier family 22 (organic cation transporter), member 1	Acetylcholine; dopamine; monoamine; norepinephrine; organic cation; quaternary ammonium group; secondary active organic cation	3.60
P53987	Slc16a1	Solute carrier family 16 (monocarboxylate transporter), member 1	Lactate	2.69
A2VD10	Slc13a3	Solute carrier family 13 (sodium-dependent dicarboxylate transporter), member 3	l-Aspartate; citrate; dicarboxylic acid; high-affinity sodium-dicarboxylate symporter; organic acid-sodium symporter; sodium-dicarboxylate symporter; succinate	2.22
Q80W57	Abcg2	ATP-binding cassette, subfamily G (WHITE), member 2	Drug	2.02
Q3ZAV1	Slc22a12	Solute carrier family 22 (organic anion/urate transporter), member 12	Organic anion; urate	1.81

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See https://hpcwebapps.cit.nih.gov/ESBL/Database/PCT/ (53, 148, 171). The list is sorted based on the abundance levels of transporters (calculated from spectral counts normalized by amino acid length) for normal/control rats.

3. Proximal tubule response to metabolic acidosis

One of the major roles of proximal tubules is in the regulation of acid-base balance. Most of the filtered bicarbonate is effectively handled by a process called “bicarbonate reclamation” in proximal tubules, helping maintain serum bicarbonate levels within the normal range. A defect in this process can cause renal bicarbonate loss and the resultant metabolic acidosis. In situations that increase acid production in the body, proximal tubules will respond by enhancing plasma glutamine extraction and glutamine catabolism, promoting the generation of intracellular ammonium and bicarbonate ions that assist the excretion of excess acid. In the response to metabolic acidosis, the ammonium is selectively secreted into the urine, while the bicarbonate exits across the basolateral plasma membrane into the blood.

Previously, using traditional approaches, metabolic acidosis has been demonstrated to increase the expression levels of several transporters and enzymes in proximal tubules that facilitate glutamine uptake and catabolism, such as the basolateral glutamine transporter SLC38A3 (sodium-coupled neutral amino acid transporter 3), the apical NHE3, the mitochondrial aquaporin-8 (AQP8), phosphoenolpyruvate carboxykinase (PEPCK), glutaminase (GA), and glutamate dehydrogenase (37). Recently, a series of proteomic studies from the Curthoys laboratory has further expanded the molecular understanding of this complex adaptive response in proximal tubules as described below.

First, Curthoys et al. (38) used two-dimensional difference gel electrophoresis (2D-DIGE) to analyze changes in the total cellular proteome of renal proximal tubules from acidotic rats. Acidosis was induced in rats by stomach loading of NH₄Cl, followed by its supplement in drinking water. After 2 h and 1, 3, and 7 days of the experiment, proximal tubules from acidotic rats were isolated by Percoll density gradient centrifugation (∼95% pure) and then studied using the 2D-DIGE technique. The results confirmed the adaptive increases in the protein abundance of GA and PEPCK in response to metabolic acidosis, as previously described (79, 164). In addition, 17 and 16 new proteins were found to be increased and decreased, respectively, after 7 days of metabolic acidosis. The time course analysis of these protein abundance changes allowed the investigators to classify the kinetic profiles of these altered proteins into two groups (i.e., those exhibiting rapid vs. gradual changes), thus assisting them in the formulation of a theoretical mechanism responsible for specific changes. The finding that all of the mRNAs encoding the gradually-changed proteins contain a putative pH response element within their 3′-untranslated region led to the conclusion that selective mRNA stabilization may be the primary mechanism for regulating levels of the proteins that were gradually increased in response to metabolic acidosis.

Second, Walmsley et al. (171) initiated a study at the subproteome level of proximal tubule cells by investigating the effect of metabolic acidosis on BBMV proteome using quantitative MS. Using the MgCl₂ precipitation technique, BBMVs were isolated from proximal tubules (which were initially purified by Percoll density gradient centrifugation) from 1-, 3-, and 7-day acidotic rats and then subjected to a label-free LC-MS/MS analysis. In total, 298 proteins were identified, for which about one-fourth were predicted to have at least one transmembrane domain. The quantitative analysis of protein expression for control and each acidosis time point revealed several patterns of temporal changes in the protein abundance levels of apical membrane and associated proteins, reflecting the intricate dynamic molecular adaptation to acute and chronic phases of metabolic acidosis in proximal tubules. These changes were found in various groups of proteins that are essential to the adaptation, such as transporters (e.g., SLC5A2 and SLC5A8 were significantly increased), peptidases (e.g., γ-glutamyltranspeptidase and glutamate carboxypeptidase were significantly increased), glycolytic and gluconeogenic enzymes (most of them were significantly decreased), and trafficking proteins (e.g., myosin-9 and DAB2 were significantly increased and decreased, respectively).

In the third study of the series, mitochondrial proteins from proximal tubules were extracted and compared between control and 7-day chronic acidotic rats by two-dimensional LC-MS/MS (53). Based on spectral counting, the abundance levels of 33 proteins (from the total identification of 901 proteins) were reported to be significantly altered in the acidotic condition. This study, again, confirmed the upregulation of the two crucial enzymes in glutamine catabolism, GA, and glutamate dehydrogenase, in response to metabolic acidosis. In addition, the other regulated proteins discovered included not only several mitochondrial proteins, but also proteins from other organelles that were co-isolated with mitochondria (by differential and sucrose density centrifugation), such as peroxisomes and the ER. Among these regulated proteins, an increase in the expression level of mitochondrial carbonic anhydrase (CA5B) was of particular interest. This adaptation might help increase the translocation of bicarbonate ions generated in the mitochondria to the cytosol, thus facilitating the restoration of acid-base balance. Since acetylation of lysine in known to alter the abundance of mitochondrial proteins (188), the authors investigated lysine acetylation of mitochondrial proteins from the chronic acidotic rats using immunoblotting (probed with an anti-acetyl lysine antibody) and observed a marked increase in a number of these proteins. Subsequently, LC-MS/MS analysis identified a total of 39 lysine acetylation sites, 22 of which were novel sites. Future studies are required to investigate the role of lysine acetylation in the regulation of glutamine catabolism in mitochondria during metabolic acidosis.

In an attempt to complete the analysis on all of the relevant subcellular fractions from proximal tubule cells, the last study was conducted to perform a quantitative proteomic analysis of the cytosolic fraction isolated from proximal tubules of acidotic rats (148). This study identified a total of 461 proteins, 24 of which showed statistically significant changes in abundance in response to metabolic acidosis, as evaluated by spectral counting and average MS/MS total ion current. To better understand the complex molecular response to metabolic acidosis, the data from the apical membrane (BBMV), mitochondrial, and cytosolic proteome studies were combined and analyzed with the STRING database and network analysis tool for identifying/predicting protein-protein interactions (https://www.string-db.org/cgi/input.pl). For the upregulated proteins, clusters of interconnected proteins involved in ammoniagenesis, retinol metabolism, hydrogen ion transport, and fatty acid metabolism were identified. On the other hand, only a cluster of proteins involved in phenylalanine metabolism was detected for the downregulated proteins. The comprehensive list of proteins identified and quantified in this series of proteomic studies can be found at https://hpcwebapps.cit.nih.gov/ESBL/Database/PCT/. Although a significant amount of information has been revealed by these proteomic studies, future studies will still be needed to determine the precise role of each altered protein and the dynamic interplay between them in the adaptive response of proximal tubules to metabolic acidosis. Systems-level studies of the other modes of protein regulation in proximal tubules beyond abundance changes, e.g., PTMs (phosphorylation, acetylation, and ubiquitination), subcellular translocation, and protein-protein interactions, would help further elucidate this adaptive process.

4. Angiotensin and the proximal tubule

The renin-angiotensin-aldosterone system is a key regulator of blood pressure and sodium reabsorption in the kidney. The proximal tubule is one of the target sites of the activated peptide hormone angiotensin II (135). Many proteomic studies have been conducted to comprehend the molecular physiology of proximal tubule response to angiotensin stimulation in terms of total proteome and signaling phosphoprotein abundance changes, as well as response to the inhibition of angiotensin II production.

To comprehensively uncover proteome changes in response to angiotensin, Konvalinka et al. (89) incubated SILAC-labeled primary human proximal tubule cells with and without 10⁻⁷ M angiotensin II for 8 h. Two-dimensional LC-MS/MS analysis identified a total of 5,011 proteins, 53 and 30 of which were upregulated and downregulated, respectively. Gene ontology analysis revealed that biological processes, such as regulation of immune response, cell proliferation, and response to stress, were enriched in the upregulated proteins, while the regulatory process of lipoprotein particle clearance was enriched in the downregulated proteins. Importantly, heme oxygenase 1 was identified as the most significantly upregulated protein in response to angiotensin II stimulation, and this finding was validated via an in vivo experiment in angiotensin II type 1 receptor (AT1R) knockout mice. Heme oxygenase 1 in urine was also positively correlated with its kidney expression and could be a promising tool for noninvasively measuring angiotensin II activity in proximal tubules.

In another study, phosphorylation-mediated signaling in response to angiotensin II was examined by pathway-specific multi-immunoblotting to quantify 38 phosphorylation sites of signaling proteins in proximal tubules (99). Rats were treated with angiotensin II at a pressor or nonpressor dose for 2 wk in the presence or absence of an AT1R blocker, losartan, and then proximal tubules were freshly isolated and analyzed. The abundance of 14 phosphoproteins was demonstrated to be significantly altered by the pressor dose of angiotensin II, and these changes could be generally restored by losartan. With the nonpressor dose of angiotensin II, only seven phosphoproteins were reported to show changes in their abundance. Overall, this study revealed major responses to angiotensin II in signaling for protein kinase C, glycogen synthase kinase 3, and cAMP-dependent pathways in proximal tubules.

Leong et al. studied the effect of blocking angiotensin II formation (using captopril, an angiotensin-converting enzyme inhibitor) on the redistribution between different membrane domains of apical membrane-associated proteins in rat proximal tubules (98). After treatment with intravenous captopril for 20 min, a significant diuretic response, as demonstrated by an increase in urine output and proximal tubule flow, was induced in rats. Subsequently, rat kidney cortical tissues were homogenized and then fractionated by density gradient centrifugation to segregate the two apical membrane domains of proximal tubule cells, i.e., those located either at apical microvilli (low density) or intermicrovillar cleft (higher density). Based on immunoblot analysis, proximal tubule Na⁺ transporters, NHE3 (SLC9A3), and NaPi2 (SLC34A1), and their candidate regulators, NHERF-1 (SLC9A3R1) and unconventional myosin-VI (MYO6), were found to redistribute from low-density apical microvillus-enriched fractions into higher-density intermicrovillar cleft fractions after captopril administration. To discover more proteins that exhibited the same redistribution pattern as NHE3, an apical microvillus-enriched fraction that demonstrated a large redistribution of NHE3 was investigated by SDS-PAGE coupled with MALDI-time-of-flight MS (MALDI-TOF MS). Eight Coomassie blue-stained bands that showed a discernible difference in density between control and captopril-treated samples were detected, and the following proteins were identified in those bands: megalin, myosin II-A, clathrin, aminopeptidase N, dipeptidyl peptidase IV, ezrin, moesin, and vacuolar H⁺-ATPase subunit-β₂. This redistribution of apical transporters and associated proteins in proximal tubules is potentially involved in the diuretic response and may contribute to the antihypertensive effect of angiotensin-converting enzyme inhibitors.

B. Response to Osmotic Stress and Hormonal Activation of cAMP in the Thick Ascending Limb of the Loop of Henle

1. Isolation

The loop of Henle is a major tubule segment regulating urinary concentrating ability of the kidney. Medullary thick ascending limbs (mTAL) of Henle’s loop cells can be isolated using the density gradient centrifugation method (47). Gunaratne et al. (62) slightly modified this method to isolate mTAL tubules from rat kidneys, which helped shorten the isolation duration, thus preserving tubule viability. Due to a high aerobic energy consumption for this cell type, furosemide was injected to the animals before mTAL isolation, which inhibited the activity of the sodium-potassium-chloride cotransporter (NKCC2), the main sodium transporter in mTAL, ultimately reducing ATP consumption by the Na-K-ATPase. In addition, oxygenated buffer was used during digestion with collagenase. The mTAL tubules were isolated by low-speed centrifugation, which separated the heavier mTAL tubules from other structures in the renal medulla. Alternatively, Zheleznova et al. (190) used a sieving approach with 100-µm nylon mesh to retain long mTAL tubules. This method allowed quick mTAL isolation with a purity of 93.6%. Another approach to isolate TAL segments is immunofluorescence laser microdissection (111). With a staining of cell-type specific markers, this technique could permit an isolation of target areas from tissue sections with very high purity.

2. Profiling

Proteomic profiling of native mTAL cells has been performed only on mitochondrial-and phosphoprotein-enriched fractions of mTAL cells; therefore, comprehensive proteomic studies of this segment have not yet been performed. In a study by Zheleznova et al. (190), mitochondria of mTAL cells were isolated from Dahl salt-sensitive rats and salt-resistant control rats and analyzed with LC-MS/MS. Ninety-six mTAL mitochondrial proteins were identified. Another study examined phosphoproteome of rat mTAL cells (62). A total of 654 phosphopeptides were identified (https://esbl.nhlbi.nih.gov/mtal-phospho/).

3. Response to osmotic stress

The tight regulation of cell volume is important for a cell to maintain its proper function. Extracellular osmolality is the major factor that affects cell volume. Cells in the renal medulla, including mTAL cells, are usually exposed to a high osmolality environment. Thus these cells must have special adaptations to conquer challenging osmotic conditions. Several studies by Dihazi et al. (17, 21, 41) used proteomic tools to explore these adaptations in an mTAL cell line derived from the outer medulla of a rabbit kidney. The cells were stressed with a 600 mosmol/kgH₂O NaCl medium, compared with a control group cultivated in a standard medium (300 mosmol/kgH₂O). Two-dimensional gel electrophoresis coupled with MALDI-TOF MS analysis was employed for protein quantification and identification. Consistent with preexisting knowledge (22), aldose reductase was identified among the upregulated proteins. This protein is known to mediate sorbitol synthesis; thus the increase in its abundance could explain the adaptive increase in intracellular osmolality of the mTAL cells. The proteomic study further identified 24 additional upregulated proteins, including proteins involved in energy metabolism, cytoskeleton-associated proteins, and molecular chaperone proteins. Moreover, four differentially expressed vimentin isoforms were also identified. In addition to osmotic stress due to NaCl, high glucose (30 vs. 5.5 mM for control cells) was also used to induce osmotic stress in the mTAL cells (21). This condition appeared to regulate different isoforms of vimentin in comparison to the high NaCl condition. Several ER-calcium binding chaperones, namely 78-kDa glucose regulated protein, Erp72, and calreticulin, were downregulated in response to NaCl-induced osmotic stress, and these changes were reversible after the high osmolality medium was exchanged back to a normal medium. The follow-up experiments suggested that the calcium binding and storage capacity of calreticulin is one of the key mechanisms that promotes the adaptive response of mTAL cells to osmotic stress (17).

4. Regulation of solute transport by phosphorylation-driven networks

Solute transport in the thick ascending limb of Henle’s loop is regulated by many hormones and intracellular chloride levels. Parathyroid hormone, calcitonin, glucagon, and vasopressin are known to trigger intracellular signaling networks in TAL cells via heterotrimeric G protein receptors that mediate an increase in the second-messenger cAMP (114). However, the downstream signaling pathways of these hormones are still elusive. To further elucidate the pathways involved, Gunaratne et al. (62) isolated mTAL tubules and incubated with a cocktail of these four hormones for 15 min. It was shown that the mixture of all four hormones induced the highest cAMP level, compared with levels with each hormone alone. The phosphoproteome of mTAL treated with the hormone mixture was compared with the control. Enrichment of phosphopeptides was done using Ga³⁺-IMAC. Label-free quantitative LC-MS/MS analysis found a total of 654 unique phosphopeptides; among these, 48 and 28 peptides were significantly increased and decreased, respectively. Bioinformatics analysis revealed many important findings. First, for upregulated phosphopeptides, most phosphorylations occurred at basophilic sites (a phosphorylated residue surrounded by basic amino acids, i.e., arginine, lysine, or histidine). On the other hand, a majority of downregulated phosphopeptides exhibited proline-directed sites (a phosphorylated residue surrounded by a proline, usually at positions +1 and −2). This study was one of the first to identify specific classes of kinases differentially regulated in the response to cAMP. Overall, the largest groups of phosphoproteins regulated by cAMP included transmembrane transporters, protein phosphatase regulators, and cytoskeletal binding proteins. For example, an interesting finding from the proteomic analysis was an increase in phosphorylation of a basophilic site, S552, of NHE3. This phosphorylated site is known to inhibit NHE3 transport activity in the proximal tubule (187). In an in vitro experiment to test the proteomic results, a basophilic kinase, protein kinase A, was shown to phosphorylate NKCC2 at S126 and S874, both of which were upregulated in the proteomic analysis. Finally, a minority of cAMP-regulated phosphorylated sites detected were neither basophilic nor proline-directed sites, such as T96 of NKCC2 (which was increased by cAMP activation). Another group of researchers used proteomic techniques to study the regulation of NKCC2 in response to hypotonic low-chloride conditions (138). They found that five phosphorylated sites on the NH₂-terminal tail of NKCC2, i.e., S87, T91, T96, T101, and S126, were upregulated in NKCC2-overexpressing human embryonic kidney-293 cells when they were exposed to a hypotonic low-chloride medium. Mutation of T101 and S126 sites was demonstrated to significantly reduce the NKCC2 activity, supporting the physiological role of these phosphorylated residues. While the WNK/SPAK/OSR1 signaling pathway was shown to be responsible for T91, T96, and T101 phosphorylation, AMP-activated protein kinase potentially accounted for S126 phosphorylation in this hypotonic low-chloride condition, but a kinase for S87 was not identified. FIGURE 8 illustrates the proposed signaling network that regulates NKCC2 and NHE3, the key sodium transporters in TAL cells, from these proteomic studies. With a better insight in NKCC2 regulation, novel therapeutic agents could be developed to treat sodium-retention states that are resistant to the conventional diuretics.

FIGURE 8. — Signaling network regulating sodium-potassium-chloride cotransporter (NKCC2) and Na/H exchanger (NHE3) in thick ascending limb (TAL) cells. A proposed signaling network for various stimuli that upregulate NKCC2 and NHE3 activities in TAL cells is shown. Parathyroid hormone (PTH), glucagon, calcitonin, vasopressin, and β-adrenergic agonists mainly activate the adenylyl cyclase/cAMP pathway, which subsequently activates protein kinase A (PKA). Other downstream mediators of cAMP, which phosphorylate T96 of NKCC2 and S552 of NHE3, are still unidentified. The low-chloride hypoosmotic condition also activates NKCC2 through WNK/SPAK/OSR and AMPK pathways. S87 of NKCC2 is also found to be phosphorylated in the low-chloride condition; however, the kinase responsible for this phosphorylation is still unidentified.

C. Response to Aldosterone and Vasopressin in the Distal Convoluted Tubule and Connecting Tubule

1. Isolation

The distal tubule controls fine adjustment of renal salt excretion. Late segments of the DCT2, along with connecting tubules (CNT) and initial cortical collecting ducts (iCCD), were successfully isolated for proteomic study using a fluorescence-activated cell sorting (FACS) method (80). Kidneys from transgenic mice expressing enhanced green fluorescent protein (eGFP) driven by the TRPv5 promoter were harvested, and tubules were digested to single cells with trypsin. These cells were sieved through 40-μm mesh filters and isolated using a FACSAria III instrument (BD Biosciences). Approximately 1.4 million cells per mouse could be isolated. Even though this technique can help effectively isolate specific cells, it requires expression of transgenic fluorescence proteins.

2. Profiling

The MS-based proteomic analysis of the distal nephron isolated by FACS (>70% purity, based on TRPv5-eGFP positive cells) identified a total of 506 unique proteins (80). However, this study included only the late distal tubule segments (i.e., DCT2, CNTs, and early CCDs). Another proteomic study related to DCTs was recently reported (26). Using cultured mpkDCT cells (originally isolated from microdissected mouse DCT), this study provided a more comprehensive list of proteins associated with distal tubule-derived cells. In total, 6,330 proteins were identified, including 3,270 phosphoproteins. This database can be accessed via http://interpretdb.au.dk/database/mpkDCT/Total_Proteome.html for the total proteome and via http://interpretdb.au.dk/database/mpkDCT/Phosphoproteome.html for the phosphoproteome. Analysis of this proteome, together with the total mouse kinome from Kinbase, also provided a comprehensive DCT kinome, including 186 kinases.

3. Response to aldosterone

Aldosterone exerts its effects on the aldosterone-sensitive portions of the distal nephron, including DCT2, CNT, and CCD (108). Jensen et al. (80) studied the proteome changes at 24 h in the transgenic TRPv5-eGFP-positive distal tubule cells after a single subcutaneous injection with aldosterone (2 mg/kg). Using an isobaric tag for relative and absolute quantification (iTRAQ), only 20 proteins were identified as differentially expressed (mostly downregulated) after aldosterone treatment. DCT cells are among the most mitochondria rich in the kidney (42), consistent with the consensus that the DCT requires high levels of ATP consumption for active transport of electrolytes driven by the basolateral Na⁺-K⁺-ATPase (107). Nevertheless, only five of the identified proteins were mitochondrial enzymes; the remainder representing a diverse group of biological functions. In addition, even though the changes did not reach the threshold level for significance, a trend toward aldosterone regulation was observed for several proteins involved in glycolysis (upregulated) and glyconeogenesis (downregulated). Since, the iTRAQ method has been shown to suffer from a “ratio compression” problem in complex samples that may result in many changes not reaching statistical significance (124), pyruvate kinase (tentatively identified as upregulated) was selected for a confirmation study by immunohistochemistry, and an increase in abundance of this protein in the late distal tubule segments was confirmed. The protein database from this study can be accessed online via https://hpcwebapps.cit.nih.gov/ESBL/Database/STADT/.

4. Response to vasopressin

In addition to the well-known water permeability effect on collecting ducts, vasopressin also affects sodium transport in DCT (44). Cheng et al. (26) conducted a large-scale proteomic analysis of vasopressin-mediated signaling networks in mpkDCT cells. With SILAC-labeling followed by LC-MS/MS, mpkDCT cells treated with 1 nM dDAVP (a selective vasopressin analog) for 15 min were compared with the control. Eighty-six phosphorylation sites were upregulated, and 99 sites were downregulated. A set of responsible kinases could be predicted by analysis of amino acid sequence around these sites. Combining these data with the known regulatory pathways for the sodium-chloride cotransporter (NCC or SLC12A3) also produced a potential vasopressin signaling network in these cells. FIGURE 9 shows a simplified version of the modeled signaling pathway with cAMP/PKA, Ca-calmodulin/CAMKK, and phosphatidylinositol 3-kinase (PI3K) as key mediators downstream of the vasopressin receptor. The roles of these kinases were confirmed in ex vivo experiments with specific kinase inhibitors. Thus vasopressin acts at two levels to regulate NCC: 1) potentiation of NCC transport activity through PI3K-mediated phosphorylation; and 2) attenuation of ubiquitin-mediated NCC degradation through cAMP/PKA and CAMKK activation. Although not all parts of the network were validated, this study exemplifies the elucidation of complex signaling cascades using phosphoproteomics.

FIGURE 9. — Simplified signaling pathway for vasopressin-mediated sodium-chloride cotransporter (NCC) regulation in distal convoluted tubule (DCT) cells. Vasopressin stimulation of DCT cells activates the cAMP, Ca-calmodulin/CAMKK, and phosphatidylinositol 3-kinase (PI3K) second-messenger pathways, which subsequently activate PKA, SGK1, and Akt kinases. Final major effectors of NCC activity are WNK/SPAK/OSR (which phosphorylate NCC) and NEDD4L (which ubiquitinates NCC). Solid black lines represent pathways supported by experimental results for NCC phosphorylation in the presence of specific kinase inhibitors. Dashed lines represent effectors deduced from experiments with other stimuli such as aldosterone. Solid black and dashed gray lines designate stimulatory phosphorylation of NCC, whereas red dashed lines denote inhibitory regulation of NEDD4L, resulting in increased abundance and overall activity of NCC. V2R, vasopressin receptor 2.

D. Response to Vasopressin in the Collecting Duct

1. Isolation

The main physiological roles of the renal collecting duct are to concentrate urine and to control fine adjustment of renal salt and water homeostasis. This is mainly mediated via highly abundant transport proteins, such as urea transporters (e.g., Slc14a2), as well as the vasopressin-regulated water channel aquaporin-2 (AQP2), which translocate to the apical membrane of cells undergoing vasopressin exposure (127). Collecting ducts, especially inner medullary collecting ducts (IMCD), have been isolated for use in many proteomic studies. The method is based on the physical property that IMCD cells are heavier than other non-IMCD cells present in the renal inner medulla. In brief, renal inner medullas are removed by dissection, minced, and then digested with collagenase B and hyaluronidase. After that, the suspensions are subjected to low-speed centrifugation (50–80 g for 10–30 s). This method yields high purity IMCD tubules, characterized by enrichment of AQP2 and depletion of noncollecting duct markers, such as AQP1 (66, 67, 69, 167).

In addition to isolation of the IMCD tubules, further separation of subcellular membrane fractions from IMCD cells would be invaluable in deciphering the molecular physiology of IMCD. Such fractionation steps would allow subcellular localization of proteins and lead to a considerable increase in the depth of proteomic analyses. Some of the fractions that have been studied include AQP2-containing intracellular vesicles, both apical and basolateral plasma membrane fractions, and the nuclei.

In an early study of AQP2 trafficking, standard differential centrifugation was employed for separation of the plasma membrane fraction (17,000 g spin) from the intracellular vesicle fraction (200,000 g spin) (103). However, a subsequent study by Sachs et al. (144) suggested that this approach may not be valid, at least for IMCD. Using MS to compare the proteomes of multiple fractions obtained by sequential differential centrifugation of IMCD homogenates revealed that both the 17,000 g and 200,000 g fractions are highly heterogeneous and cannot be equated with “plasma membrane” and “intracellular vesicle” fractions, respectively. Thus alternative or augmented approaches to differential centrifugation are required for studying AQP2 trafficking.

To selectively isolate AQP2-containing intracellular vesicles, Barile et al. (13) generated biotinylated anti-AQP2 antibodies and mixed these antibodies with streptavidin-coated magnetic beads. The 200,000 g fraction from IMCD cells (including intracellular vesicles) was mixed with the antibody-coated beads to immunoisolate the AQP2-containing vesicles for proteomic analysis. Identification of multiple Rab GTPases confirmed the localization of AQP2 in various endosomal compartments.

For polarized epithelial cells, such as collecting duct cells, each region of the plasma membrane (apical and basolateral) contains a highly distinct set of proteins. As this difference is crucial for the collecting duct to function properly, effective separation techniques for proteins from these specific surfaces of the plasma membrane are of great importance. Yu et al. (183) first successfully biotinylated IMCD apical cell surfaces via perfusion of intact kidney medulla and used avidin affinity chromatography to enrich apical membrane proteins. To biotinylate the apical membrane, a special double-barreled pipette is required to deliver the fixative and biotinylation reagent. Second, for biotinylation of the basolateral membrane, IMCD suspensions were isolated, fixed, and incubated with biotinylation reagent. High-density membrane fractions were then prepared from biotinylated samples by differential centrifugation and enriched for biotinylated basolateral membrane proteins using CaptAvidin-agarose or streptavidin-agarose beads. Biotinylated apical and basolateral membrane proteins were then analyzed in proteomic experiments. A similar approach was applied to a collecting duct cell line (mpkCCD cells) to isolate apical plasma membrane proteins (101).

Isolations of nuclei were performed to study transcriptional regulation by vasopressin in native IMCD and mpkCCD cells, including effects on nucleus-cytoplasmic protein translocation and PTMs (19, 130, 149). In these studies, cellular fractionation into cytoplasm, soluble nuclear extract, and nuclear membrane pellet was accomplished using stepwise extraction by a commercial reagent kit.

2. Profiling

Proteome databases of IMCD and a collecting duct cell line (mpkCCD) are extensive. Published databases obtained from native kidney tissue include the IMCD proteome, apical membrane IMCD proteome, basolateral membrane IMCD proteome, nuclei IMCD proteome, IMCD phosphoproteome, and IMCD phosphotyrosinome (11, 13, 66, 67, 69, 130, 132, 144, 151, 160, 167, 183, 186). Currently, more than 8,000 proteins have been identified from the rat IMCD. The database can be accessed online via https://hpcwebapps.cit.nih.gov/ESBL/Database/IMCD_Proteome_Database. In addition, comprehensive proteomic analyses of subcellular fractions from mouse mpkCCD cells have also identified more than 8,000 proteins which, along with virtual Western blots, are publicly available at https://hpcwebapps.cit.nih.gov/ESBL/Database/mpkFractions/ (179, 180).

3. Response to vasopressin

Vasopressin is the key hormone in water homeostasis, which binds to cells of CNTs and collecting ducts expressing vasopressin type 2 receptor (gene symbol: Avpr2) (86). AQP2 (gene symbol: Aqp2) is the vasopressin-regulated water channel of the renal collecting duct and, as such, is central to the regulation of water excretion. As the circulating level of the peptide hormone, vasopressin, increases in the blood, water excretion decreases largely, but not solely, through the regulation of AQP2. We know from studies done in the 20th century that vasopressin regulates AQP2 in two main ways (87): 1) vasopressin-mediated regulation of trafficking of AQP2-containing membrane vesicles to and from the apical plasma membrane of collecting duct principal cells; and 2) vasopressin-mediated regulation of the total abundance of the AQP2 protein in collecting duct principal cells. Recently, advances in understanding vasopressin-mediated processes has been driven by a series of proteomic studies. FIGURE 10 outlines various aspects of the mechanisms involved in regulatory actions of vasopressin and the corresponding proteomic techniques that have been employed.

FIGURE 10. — Outline depicting proteomic studies designed to elucidate the mechanisms of vasopressin action on the collecting duct. Vasopressin effects on the collecting duct are traditionally divided into short-term effects and long-term effects. The numerous proteomic strategies that have been employed to decipher the detailed mechanism(s) of vasopressin are indicated in the green boxes. AQP2, aquaporin-2; MS, mass spectrometry; SILAC, stable isotope labeling by amino acid in cell culture.

a) short-term effects.

Activation of the vasopressin receptor in collecting ducts acutely triggers complex intracellular signaling cascades that control translocation of AQP2 molecules from intracellular vesicles to the apical plasma membrane. The vasopressin signal is rapidly transduced by PTMs, especially phosphorylation, of an extensive network of proteins. The overall response to vasopressin has been characterized in isolated perfused collecting ducts (147). The response is reproduced by addition of cyclic AMP (153) and occurs relatively rapidly, with an initial increase in water permeability ~40 s after vasopressin addition and a half-time for the full response in 8 min. Because of the dependence of this response on cyclic AMP, it has been widely assumed that the trafficking effects of vasopressin are mediated by one or both of the known cyclic AMP-dependent protein kinases (PKAcα or PKAcβ). Analysis of the phosphoproteome after dDAVP administration has provided valuable insight into vasopressin-signaling pathways.

Rinschen et al. (142) studied the phosphoproteome of mpkCCD cells after short-term treatment by dDAVP. Using SILAC labeling, followed by strong cation exchange fractionation and IMAC phosphopeptide enrichment, phosphoproteomes were compared between dDAVP-treated and control groups. A total of 3,869 phosphopeptides corresponding to 1,695 phosphoproteins were identified. Among vasopressin-regulated peptides, 273 were increased, whereas 254 were decreased. Gene ontology analysis of this data showed “transcriptional regulation,” “cell adhesion,” and “protein amino acid phosphorylation” to be prominent processes. Of interest, most upregulated phosphorylation sites were basophilic, which was consistent with vasopressin-mediated activation of kinases, such as PKA and CAMK2. Activation of CAMK2 is consistent with previous studies suggesting a link between calcium/calmodulin-mediated signaling and the regulation of water permeability (29, 31, 43, 63). Among downregulated phosphorylation sites, the predominant P-X-S-P sequences suggested that the activities of proline-directed kinases (e.g., MAPKs and cyclin-dependent kinases) were inhibited by vasopressin. To further expand the phosphoproteome database, Hoffert et al. (65) used the iTRAQ labeling technique to study dynamic changes in the phosphoproteome of native IMCD cells in response to vasopressin. IMCD tubule suspensions (see sect. IID1, above) were incubated with 1 nM dDAVP for 0.5, 2, 5, and 15 min. A total of 12,167 phosphopeptides were identified, and 273 phosphopeptides exhibited a significant change in abundance at a minimum of one time point. PANTHER pathway analysis of these changes revealed both known and unknown vasopressin signaling pathways, such as PI3K, PKB/Akt, MAPKs, Rho kinases, angiogenesis, Wnt, and heterotrimeric G protein Gα_q- and Gα_o-mediated pathways. For longitudinal analysis, the temporal pattern mining algorithm (https://hpcwebapps.cit.nih.gov/TPM/) (145) was used to cluster peptides with similar temporal profiles. Thirty clusters were identified, and gene ontology biological process analysis showed several processes regulated by vasopressin. One of the most prominent processes downregulated by short-term vasopressin signaling was apoptosis, involving altered phosphorylation of many proteins, such as Bok, Bad, and Krt8. This suggests a role for vasopressin in promoting cell survival of IMCDs.

Similarly, several phosphoproteomic studies of rat native IMCD cells have also been conducted. Hoffert et al. (66) isolated native IMCDs from rat kidneys and incubated with dDAVP for 10 min, then digested peptides were fractionated with strong cation exchange, and phosphopeptides were enriched with IMAC. These phosphopeptides were analyzed with LC-MS multisequential (LC-MSⁿ) neutral loss scanning. A total of 223 unique phosphoproteins were identified. The most influential finding of this study was a cluster of four phosphorylation sites in the COOH-terminal tail of AQP2, i.e., at S256, S261, S264, and S269. Using more powerful MS techniques, Bansal et al. (11) later confirmed the essential findings of Hoffert et al. and demonstrated that vasopressin increases phosphorylation of the urea channel UT-A1 at three sites, S84, S486, and S499, while markedly expanding the number of known phosphorylation sites in the native IMCD. Additionally, comparison between dDAVP-treated and control groups found many new vasopressin-regulated phosphorylation sites. For example, S552 phosphorylation of β-catenin was significantly increased by dDAVP, as detected by both LC-MS/MS and Western blotting analysis. This phosphorylation site was previously shown to have a role in cell proliferation via promoting an interaction with the T-cell factor transcription factor (159). The identification of the four phosphorylation sites in AQP2 opened the door for future work to understand short-term regulation of trafficking by vasopressin, which we review in the following sections.

I) S256. S256 of AQP2 was originally predicted to be a vasopressin-regulated site by Kuwahara et al. (91) based on their initial identification of the full amino acid sequence of AQP2. Phosphorylation of this site was first demonstrated by Nishimoto et al. (118) using phospho-specific antibodies. Quantification of S256 phosphorylation in AQP2 shows that, even in the absence of vasopressin, phosphorylation of this site is high (177). Some studies have shown increases in phospho-occupancy at this site in response to vasopressin (66), others have shown decreases (177), and others no change (185). Time course studies using phospho-specific antibodies showed a vasopressin-induced increase in phosphorylation that occurs within 1 min of exposure to vasopressin (64). Mutational analysis shows that S256 must be phosphorylated for downstream sites S264 or S269 to become phosphorylated in response to vasopressin, indicating that S256 plays a permissive role in short-term regulation by vasopressin. Accordingly, mutation of S256 to other amino acids blocks AQP2 exocytosis (10, 102).

The amino acid sequence surrounding S256 of AQP2 (-R-R-R-Q-S*-V-E-) suggested to Kuwahara et al. (91) that it is phosphorylated by protein kinase A. However, in cultured mouse mpkCCD cells in which both genes encoding protein kinase A catalytic subunits were deleted, S256 still became phosphorylated in the presence of vasopressin (78), implying that S256 is phosphorylated by one or more basophilic protein kinases with specificities similar to protein kinase A. Kinase profiling with LC-MS/MS coupled with kinase inhibitor data (20) suggested that this kinase may be the delta isoform Ca²⁺/calmodulin-dependent protein kinase II (Camk2d). The involvement of this kinase fits with the observation that vasopressin increases intracellular calcium spike frequency in collecting duct cells (133), which is thought to be the result of protein kinase A-mediated phosphorylation of the Itpr3 (S934 and S1832) calcium release channel in the ER of collecting duct principal cells (78).

II) S261. In contrast to the other AQP2 phosphorylation sites, S261 phosphorylation decreases in response to vasopressin (66). Time course studies show that the decrease occurs over several minutes, i.e., much more slowly than the increase in S256 phosphorylation (64). The sequence surrounding this site (-V-E-L-H-S*-P-Q-) is consistent with phosphorylation by one of the proline-directed kinase family members, which would include both MAPKs and cyclin-dependent kinases. Bayesian analysis based on subcellular fraction in mpkCCD cells identified ERK1, ERK2, and p38α as the kinases most likely to phosphorylate this site. A previous study by Nedvetsky et al. (117) also implicated p38 in AQP2 phosphorylation at S261. A recent study by Cheung et al. (27) pinpointed the protein phosphatase PP2C in dephosphorylation of S261.

The role of S261 phosphorylation in AQP2 trafficking is controversial. Deen’s group has reported evidence for a role for S261 phosphorylation in AQP2 endocytosis (157). In contrast, Lu et al. (102) and Moeller et al. (113) reported no effect of S261 mutation to a nonphosphorylatable amino acid on AQP2 localization. Yui et al. (184) recently reported that S261 phosphorylation is important in AQP2 apical translocation.

III) S264. In IMCD, phosphorylation at S264 of AQP2 increases in response to vasopressin in a time course that lags behind that of S256 (64). However, S264 phospho-occupancy never increases above 4% of all AQP2 molecules in response to vasopressin (177), suggesting that phosphorylation at this site may be either unimportant or confined to some small subcellular compartment. Indeed, work by Fenton et al. (50) indicates that S264 phosphorylation is confined to clathrin-coated vesicles, early endosomal compartments, and recycling compartments. Its potential role in AQP2 trafficking needs more study.

IV) S269. Phosphorylation of AQP2 at S269 is strongly regulated by vasopressin (64). In IMCD, AQP2 S269 phospho-occupancy was seen to increase from 3 to 26% of all AQP2 molecules in response to vasopressin (177), an order of magnitude increase. Immunogold localization in collecting duct cells (64, 112) demonstrated that AQP2 phosphorylated at S269 is limited to the apical plasma membrane with virtually no intracellular labeling. This observation lead to the hypothesis that S269 phosphorylation regulates AQP2 abundance in the apical plasma membrane by blocking or inhibiting AQP2 endocytosis. S269 is the third from last amino acid in AQP2 and is part of a putative PDZ ligand (172). Wang et al. (172) proposed that S269 phosphorylation inhibits endocytosis of AQP2 by inhibiting its ability to bind to the PDZ-protein, Sipa1l1 (rodents; SIPA1L1, most other species). Sipa1l1 is a Rap-GAP, i.e., it stimulates the GTPase activity of Rap, a small GTP-binding protein that regulates the Raf-MEK-ERK signaling pathway by activating Raf. S1611 of Sipa1l1 was identified by phosphoproteomics in mpkCCD cells as a protein kinase A target that may regulate Sipa1l1 GAP activity (78). Previously, Noda et al. (120) used MALDI-TOF MS to identify a Sipa1l1 paralog, SPA1 (gene symbol: Sipa1) as an AQP2 interacting protein. AQP2 trafficking to the apical membrane was inhibited when SPA-1 was mutated so that it lacked Rap1GAP activity (119). Collectively, these results demonstrate a substantial role for vasopressin-induced phosphorylation of S269 in the vesicular trafficking and localization of AQP2, likely via regulation of specific AQP2 protein-protein interactions.

Although the cAMP/PKA pathway is a major mediator of vasopressin signaling, pathways controlled by other kinases may also be involved in regulating vasopressin signaling. To address this possibility, Bradford et al. collected extensive previously published data and estimated probabilities (using Bayes’ theorem) for involvement of each kinase in the entire rat proteome in phosphorylation of AQP2 S256 (as a surrogate for vasopressin signaling) (20). Next, various specific kinase inhibitors were applied to isolated IMCDs before incubation with dDAVP, followed by Western blotting to quantify phosphorylated AQP2 S256. The results from this inhibitor experiment were combined with the initial probability estimates. Based on these collective analyses, the top-ranked kinases for vasopressin-mediated AQP2 S256 phosphorylation were CAMK2, followed by PKA, and PKB/Akt. In a follow-up experiment, dephosphorylated IMCD lysates were treated with three basophilic recombinant kinases, i.e., PKA, CAMK2, or serum/glucocorticoid-regulated kinase (SGK, which exhibits similar kinase substrate specificity to PKB/Akt), then a phosphoproteomic analysis was performed using LC-MS/MS. The results confirmed that, while all three of these kinases could phosphorylate AQP2 at S256, both CAMK2 and PKA were more potent than serum/glucocorticoid-regulated kinase, in agreement with the predicted probability ranking.

The second component of the short-term vasopressin response is the regulation of AQP2 trafficking. Tracking the intracellular trafficking of AQP2 in response to vasopressin is crucial to fully understand molecular mechanisms of vasopressin actions. Quantitative MS, combined with biochemical procedures that isolate particular subcellular fractions, has the potential to identify the movement of particular proteins from one location to another in cells. In an experiment to establish AQP2 subcellular localization in the unstimulated IMCD cells, Barile et al. (13) isolated AQP2-containing vesicles by immunoaffinity purification as described above and fractionated these vesicles by SDS-PAGE into 35 gel fractions. In-gel digestion followed by LC-MS/MS analysis identified 181 proteins, many of which are markers of subcellular membrane domains, such as members of the Rab family and SNARE proteins. This finding demonstrated that, in the basal state, AQP2 resides mainly in endosomes, the trans-Golgi network, and rough ER. In a targeted analysis of Rab proteins following treatment with vasopressin, some evidence was obtained, suggesting redistribution of AQP2 from late endosomes into recycling endosomes. To fully elucidate the dynamic intracellular trafficking of AQP2 in response to vasopressin, further studies are needed using this immune-isolation approach, coupled with unbiased quantitative LC-MS/MS analysis.

The ultimate result of vasopressin-stimulated trafficking is the increase in insertion of AQP2 into the apical plasma membrane. Using biotinylation of apical surface proteins, Loo et al. (101) studied changes in the apical membrane proteome following dDAVP stimulation in SILAC-labeled mpkCCD cells. Membrane abundance of 100 proteins was found to be altered by vasopressin. Among these proteins, AQP2 exhibited the highest increase in abundance, followed by Mal2. Importantly, Mal2 protein is a component of lipid rafts and functions in transcytosis (an intracellular transport pathway used to deliver membrane-bound proteins from the basolateral to the apical surface). Gene ontology analysis of these 100 proteins revealed “negative regulation of cellular component organization,” “actin filament capping,” “regulation of actin filament polymerization,” and “regulation of cytoskeleton organization” to be the predominantly enriched processes. Using live-cell imaging, apical F-actin relocalization to basolateral regions (peripheralization) was demonstrated in response to dDAVP, highlighting the major role of F-actin dynamics in AQP2 translocation.

The effect of vasopressin on protein-protein interactions in the collecting duct was also recently studied. By focusing on the UT-A1, a known urea transporter regulated by vasopressin, Chou et al. (30) used chemical cross-linking, followed by immunoprecipitation of UT-A1 in native rat IMCD cells, incubated with or without dDAVP for 15 min to study its interacting partner proteins. LC-MS/MS analysis revealed 25 partner proteins significantly changed in its binding with UT-A1 in response to dDAVP. These included two upregulated proteins, Nae1 and Uba3, which are involved in the activation of Nedd8, a ubiquitin-like protein. Among 23 significantly decreased proteins were several actin cytoskeleton-related proteins, Na-K-ATPase subunits, and phosphorylase b kinase regulatory subunit-β. The decreased interaction between UT-A1 and Na-K-ATPase subunits, which reside mainly on basolateral membrane, also support a notion that UT-A1 basolateral localization is physiologically regulated, at least in part, by vasopressin.

b) long-term effects.

A major well-established, long-term effect of vasopressin stimulation on collecting ducts is an increase in AQP2 protein abundance (40). This could be due to increased AQP2 synthesis, decreased AQP2 degradation, or a combination of these processes. Application of various proteomic techniques to study long-term vasopressin stimulation has contributed to a more comprehensive understanding of the mechanisms involved in regulation of AQP2 abundance.

In the first such study, van Balkom et al. (167) examined long-term vasopressin effects on the rat IMCD proteome using 2D-DIGE coupled with MALDI-TOF MS. Another early study employed the complementary technique of one-dimensional electrophoresis prefractionation, followed by LC-MS/MS with isotope-coded affinity tagging to study the effect of long-term vasopressin stimulation in rat IMCD (132). In these two studies, partially overlapping subsets of 43 and 33 proteins, respectively, were identified that increased, decreased, or shifted position. Among these proteins, cytoskeletal proteins and proteins involved in energy metabolism, response to stress, intracellular transport, vesicular trafficking, and translational elongation were predominant groups of proteins identified. From these studies, it is clear that AQP2 is not the only protein that exhibits changes in abundance resulting from long-term vasopressin treatment. Further proteomic analyses in mpkCCD cells have allowed even deeper profiling of global changes in protein abundance (83).

To study the potential role of long-term vasopressin stimulation on regulation of protein abundance by transcriptional mechanisms, Schenk et al. (149) quantified protein abundance changes in fractions of cytoplasmic extract, nuclear extract, and nuclear pellet from mpkCCD cells following 30 and 60 min of dDAVP treatment. The aim of this study was to identify proteins that translocated between these compartments (e.g., transcription factors and their regulators) in response to vasopressin treatment. Using SILAC labeling, a total of 3,987 proteins were identified in the nuclear fractions, 65 of which were significantly changed in abundance, indicating altered translocation of these proteins. Among the transcription factors identified, JunB, Elf3, and Gatad2b were significantly increased, while Hmbox1 was decreased. Nedd4 was the only posttranslational modifier found to be increased. The Mediator complex (a multiprotein complex that acts as a transcriptional coactivator of RNA polymerase II) was also increased, suggesting that the complex was assembled in cytoplasm and translocated together to the nucleus in response to vasopressin stimulation. Both total and phosphorylated forms of β-catenin were also increased in the nuclear fractions. Similarly, Bolger et al. (19) studied the nuclear phosphoproteome of mpkCCD cells in response to 30 min of dDAVP treatment. A total of 1,251 phosphorylation sites were identified in the nuclear fractions, 39 of which were significantly changed. Bioinformatics analysis of these regulated phosphorylation sites showed cell-cell junction and transcriptional regulation proteins to be the most common functional clusters. Phosphorylation at S552 of β-catenin was increased (known to increase β-catenin transcriptional activity), whereas phosphorylation at S73 of c-Jun was decreased after dDAVP treatment (suggesting decreased transcriptional activity of c-Jun). Follow-up studies used chromatin immunoprecipitation employing a β-catenin antibody coupled to LC-MS/MS to identify β-catenin interacting proteins in the nuclei of native IMCD cells from rats (77). Among the interacting proteins was Taf1, which plays a central role in transcriptional initiation and is part of the transcriptional preinitiation complex that is regulated by various transcription factors through the action of the mediator complex. Finally, in a very recent study using a higher-sensitivity mass spectrometer, Pickering et al. (130) reported a total of 5,048 proteins from nuclear fractions of rat native IMCD following vasopressin treatment, of which 369 were transcription factors. dDAVP treatment resulted in altered abundance of 99 proteins in nuclear pellets and 88 proteins in nuclear extract fractions, including 7 downregulated histone proteins in the nuclear pellets. These changes in histone proteins possibly reflect vasopressin-mediated chromatin remodeling.

While it is clear from the preceding review that long-term effects of vasopressin are mediated by changes in transcription, other post-transcriptional mechanisms such as selective translational regulation and/or selective protein degradation could also play a role. To address these possibilities, Khositseth et al. performed a proteome-transcriptome comparison for mpkCCD cells, using SILAC labeling and microarray analysis to evaluate the long-term effects in response to dDAVP (83). After the quantification of 2,990 proteins, 188 were found to be altered in abundance. Importantly, less than one-third of these proteins (notably including AQP2) were found to exhibit corresponding changes in both protein abundances and mRNA levels. These results demonstrated a significant role for posttranscriptional regulation of protein abundance as part of the long-term effects of vasopressin regulation.

To further study the potential role of long-term vasopressin stimulation on posttranscriptional regulation of protein abundance, specifically focusing on protein turnover mechanisms, another proteomics technique was used to determine vasopressin effects on protein half-lives. Sandoval et al. (146) applied dynamic SILAC labeling to mpkCCD cells to determine the half-lives for all cellular proteins. dDAVP treatment significantly affected half-lives of only 5 out of 2,172 proteins identified, including AQP2, which increased nearly 50% (from 9.8 to 14.2 h). Therefore, vasopressin not only increases AQP2 transcription (83, 182), but it also decreases AQP2 degradation as well. Mathematical modeling of these data also provided information about translation rates for 1,620 of the identified cellular proteins. Among 151 proteins found to have altered translation rates after vasopressin treatment, AQP2 exhibited the largest increase (~10-fold). Collectively, these results, combined with those from studies described above, demonstrated that the long-term effects of vasopressin on AQP2 abundance are mediated by multiple regulatory mechanisms at the levels of transcription, translation, and degradation. A further interesting finding from the Sandoval et al. study (146) was that the translation rates of seven glutathione S-transferase proteins (all from the cytoplasmic GST superfamily) were also increased by vasopressin treatment, resulting in an overall increase in glutathionylated proteins. The role of vasopressin in the increased activity of this antioxidation pathway remains to be elucidated.

One interesting aspect in vasopressin physiology is the “vasopressin escape” phenomenon: despite the presence of continuous high vasopressin level, the tubule cells exhibit a diminishing response to the hormone (i.e., water diuresis occurs). Hoorn et al. (69) employed a proteomic approach to study this phenomenon by first infusing rats with dDAVP for 72 h, followed by dividing the rats into two groups (water-loading and water-restricting groups). IMCDs were isolated and analyzed with 2D-DIGE/MALDI-TOF and Western blotting. A total of 22 proteins with significant changes were identified. After subjecting these identified regulated proteins to bioinformatics pathway analysis (Ingenuity Pathway Analysis), additional proteins representing key nodes in signaling networks were predicted to be involved in vasopressin escape. For example, c-Myc, c-Fos, and c-Jun were transcription factors predicted to exhibit changes in abundance regulated by vasopressin escape, along with additional regulatory proteins, including c-src, heat shock protein 70, and RACK1. Altered abundance for all of these proteins were validated by Western blotting in this study, suggesting that a complex signaling network mediated by these proteins is involved in the vasopressin escape phenomenon.

IV. BIOINFORMATICS TECHNIQUES IN SYSTEMS BIOLOGY STUDIES

A. Drawing Physiological Conclusions From Proteomics Data

Successful collection of proteomic data is only the first half of a proteomics study. The second half is the extraction of physiological insights from the data. Here we provide some guidelines on how to do this. The starting point is an expression matrix (often in the form of a spreadsheet) with a list of proteins and/or peptides mapped to quantitative data. Such matrices are typically derived from data analysis by different search algorithms. An important step is data filtering. A hypothetical data set may contain 9,000 proteins, each with a ratio representing protein abundance under the experimental condition, divided by protein abundance under the control condition. Such a data set will include many low signal-to-noise proteins whose quantifications are imprecise. For a valid downstream analysis of these data, the low signal-to-noise proteins must be filtered out. For this, a measure of relative protein abundance or signal-to-noise ratio is needed in addition to the experimental-to-control ratio. A cutoff threshold for the abundance variable can be chosen to optimize the balance between false positives and false negatives. This can be done in two ways. First, if enough control observations are made or specific control vs. control experiments have been performed, the variability among controls can be calculated for different abundance threshold cutoffs. This gives the intrinsic variability of the method, which can be used to calculate a 95% or 99% confidence interval for control vs. control observations at different thresholds. These confidence intervals then can be used to estimate whether experimental vs. control ratios are likely to be due to the intrinsic variability of the method or to the experimental variable. In this approach, a global false discovery rate will be 1% if the 99% confidence interval is chosen and 5% if the 95% confidence interval is chosen. Second, the empirical Bayes method can be employed if it is expected that relatively few proteins will be changed as a result of the experimental perturbation. With this method, the distributions of experimental-to-control ratios are used in lieu of the control-to-control ratios described in the first method. Regardless of the method chosen, our experience is that data set filtering is crucial to being able to extract mechanistic conclusions from proteomics data.

Another way to assess whether measured ratios are likely to be reflective of real responses is to use P values derived from statistical testing for each protein over multiple replicates. If the P value is evaluated relative to a threshold (e.g., P < 0.05), a problem arises, i.e., that multiple applications of the test will result in multiple false positives. For example, if abundance ratios for 5,000 proteins are measured in 4 experimental/control pairs and a t-test is applied to each protein with a P < 0.05 threshold, there will be on the order of 0.05 × 5,000 = 250 additional false positives simply as a consequence of multiple applications of the statistical test itself. Many investigators create a correction to the P value to account for multiple tested, using for example, the Benjamini-Hochberg correction. However, this correction amounts to little more than “window dressing.” A more realistic way to deal with the problem is to recognize the P value is simply a continuously distributed descriptive statistic reflecting signal-to-noise (59) and not as a measure of truth. In practice, the use of P values or false discovery rate corrected P values plotted against the logarithm of experimental-to-control ratios (a so-called volcano plot) gives the user a global view of which measured parameters exhibited reproducible changes and which parameters remained unchanged in proteomic experiments (FIGURE 11). However, we can never establish absolute certainty of such a change.

FIGURE 11. — A volcano plot as a visual tool for globally evaluating results from quantitative proteomic experiments. A special kind of scatterplot called “volcano plot” can help visualize the result of a high-throughput study. The x-axis represents the effect size in terms of logarithm (typically base 2) of experimental-to-control expression ratio. The y-axis represents the negative logarithm of P values, which indicate statistical significance. In this example plot, appropriate cutoff values for expression ratios (vertical blue dotted lines; e.g., >50% change) and false discovery rate (FDR) corrected P values (horizontal red dotted line) are shown and used to highlight possible significant and biologically relevant changes. For this example, three proteins (pink data points) are identified as being downregulated in response to the experimental treatment, suggesting a possible molecular mechanism and/or hypothesis for additional studies.

If we cannot prove that an observed change in the abundance of any particular protein is not a false positive, then why do proteomics at all? This limitation may be alarming to biochemists who are interested only in a particular protein, but should not be problematic to physiologists who are chiefly interested in gaining a mechanistic understanding of biological processes. Biological processes depend on coordinated changes in multiple proteins. In general, for such a process, a change in a single protein associated with the process does not demonstrate much about the process, but if all or most of the protein changes involved in a process are found, a high level of confidence for the process can be inferred. An example is from a study by Yang et al. (179) quantifying protein abundance changes in response to the hormone vasopressin in several protein fractions derived from differential centrifugation of cultured collecting duct cells. It was observed that 64 separate ribosomal subunits underwent a small nominal shift from the 200,000 g pellet (containing free ribosomes) to the 17,000 g pellet (containing the rough ER) in response to vasopressin. The increase in the 17,000 g pellet amounted to only ~15–20% for each ribosomal protein subunit. If analyzed individually, the small change in each subunit may have been dismissed. Analyzed together, the same small change in all but 2 of 66 ribosomal proteins leads to a calculated P value of 10⁻³⁴, supporting the hypothesis that the entire ribosome translocated from the cytoplasm to the rough ER in response to vasopressin. This modest translocation of the cellular ribosomal pool is responsible for the massive vasopressin-stimulated increase in translation of the integral membrane protein AQP2 in the rough ER (146). Thus analysis of proteomic data sets to study physiological processes depends on detection of multiple coordinated changes in an independently curated group of proteins that together would be unlikely to be due to random variation.

Consequently, successful application of proteomics to physiological investigations depends on the availability of prior data to identify sets of proteins that are coordinately involved in particular physiological processes, (e.g., processes defined by gene ontology terms). These data sets can be mapped to the new proteomics data and the question can be asked: Are (increases/decreases/changes) in proteins in the functional dataset more frequent than (increases/decreases/changes) among all proteins expressed in the cell?” This is a question that can be answered through use of standard statistical tests such as χ² testing or Fishers’ exact testing.

1. The folly of validation

In the old days, when DNA microarrays were first introduced in biology, the arrays consisted of cDNAs immobilized on glass slides. The relative abundances of various transcripts were estimated based on their ability to hybridize with individual cDNA features. False positives were frequent, and it was well recognized that individual observations needed to be verified in individual experiments, a process that came to be known as validation. As effective ways of eliminating false positives became available for oligonucleotide arrays (e.g., commercial Affymetrix expression arrays), the reliability of microarrays dramatically increased. However, the tradition of validation remained attached to microarray procedures and eventually to RNAseq and to proteomics. In our opinion, the concept of validation should be abandoned in favor of the concept of reproducibility. With regard to proteomics, there appears to be a propensity to validate LC-MS/MS results with Western blotting, a technique that is decidedly inferior, both for specificity and for quantification. It has even been convincingly argued that, given the limitations of Western blotting, all results from Western blotting experiments should be “validated” with protein MS. Indeed, many investigators now recognize that a better way to assure reproducibility in proteomics studies is to use targeted proteomics to increase the number of times a particular quantification is observed (2).

2. The folly of fold change

A large number of software programs that carry out differential expression analysis used the concept of fold change as a means of quantification. Unfortunately, fold change is an ambiguous term and should be avoided. Originally, fold change was calculated as

[(experimental value) - (control value)] / (control value)

However, the writers of many differential expression analysis programs have used a different definition, namely,

(experimental value) / (control value)

For very large changes, these two measures are very nearly equal. However, a problem arises when changes are small. Logically, if there is no change, the fold change should be 0, not 1. A reduction to one-half is given as a fold change of 0.5, which the uninitiated might expect to represent an increase, not a decrease. Because of the ambiguities that have arisen with the term fold change, we recommend that, instead, users should report the logarithm of the ratio of the experimental value to the control value. Most investigators choose the base 2 logarithm for this purpose.

3. Making your data available to other investigators

Most journals that publish studies based on protein MS require that the data be deposited in a publicly accessible repository such as the PRIDE (PRoteomics IDEntifications) Database at https://www.ebi.ac.uk/pride/archive/ maintained by the European Molecular Biology Laboratory. In general, such databases include important metadata describing experiments as well as the raw spectral files, but not curated information. Since a large part of the added value of a data set is in the advanced bioinformatics, authors should consider including their analytical methodologies, scripts, and algorithms in the deposited data. However, similar government-supported repositories have lost support and disappeared in the past, and it would be wise to deposit the curated data elsewhere to ensure its continued availability (104). Many journals allow processed, curated data to be included with the supporting information included with published papers. Beyond this, increased visibility and accessibility can be obtained if investigators place curated data on the internet in the form of user-friendly web pages (see for example, https://hpcwebapps.cit.nih.gov/ESBL/Database/). Such pages can be constructed fairly simply without web programming skills by saving the relevant tab of a well annotated spreadsheet as an .html document and posting on an institutional website [see for example, https://hpcwebapps.cit.nih.gov/ESBL/Database/PodocytePhosphoproteome/index.htm, a database referring to a phosphoproteomic study in podocytes (140a)]. More sophisticated sites can be built with fairly fundamental web programming skills learnable in a few days, or through use of special open-source tools (see for example, https://hpcwebapps.cit.nih.gov/ESBL/Database/IMCD_Proteome_Database/).

V. CONCLUSIONS

Proteomic profiling of the main renal tubule segments of the kidney has provided unique insights into the tight regulation of solute and electrolyte transport processes and metabolism. With the rapidly expanding amount of data from these high-throughput studies, finding comprehensive information for individual proteins or genes of interest can be quite tedious. As a result, Zhao et al. (189) developed a tool called BIG (Biological Information Gatherer; https://big.nhlbi.nih.gov/index.jsp), which helps summarize all data about each individual protein or gene in the comprehensive “Renal Epithelial Transcriptome and Proteome Databases” (Epithelial Systems Biology Laboratory, NHLBI) and reports the output in readable sentences.

Despite the significant progress presented in the preceding review, these studies represent an oversimplification of renal tubule physiology, since only the four main renal tubule segments of the kidney (FIGURE 6) can be practically isolated for proteomic studies. In actuality, 14 discrete renal tubule segments have been manually dissected under the microscope for transcriptomic analysis (96), undoubtedly including many differentiated epithelial cell types not previously characterized by proteomic analysis. Therefore, further technical developments are needed to facilitate higher resolution isolation of distinct renal tubule cell types for proteomic analysis. One promising approach would be the use of a tubule segment-specific GFP expression system, based on transcriptomics-derived knowledge of gene-specific expression in the 14 discrete renal tubule segments, to isolate specific fluorescently tagged tubule segments by FACS. In addition, deeper profiling of these distinct renal tubule cell types will provide even more detailed understanding of renal physiology. Such profiling could be further enhanced through the use of orthogonal fractionation techniques, such as capillary electrophoresis/isoelectric focusing (73) or high-pH RPLC (14) upstream of standard (i.e., low-pH) RPLC-MS/MS.

The “classical” strategies for studying kidney physiology have been augmented by high-throughput proteomic techniques, which have vastly increased the amount of new information available. However, many such studies simply conclude with a list of protein changes in response to experimental manipulations, along with bioinformatics analyses intended to reveal important new physiological insights into kidney function. Unfortunately, the results from these studies have often not led to follow-up investigations involving new critical testable hypotheses. In addition to increased knowledge of normal kidney physiology, more studies are also needed to advance understanding of renal pathophysiology. For example, two recent proteomic-driven studies revealed a prominent role for selective autophagy in the downregulation of specific proteins during development of nephrogenic diabetes insipidus due to electrolyte imbalance disorders [hypokalemia (84) and hypercalcemia (82)]. Another proof-of-concept study recently demonstrated an application of proteomic analysis in exploring the molecular pathophysiology in an animal model of chronic tubulointerstitial fibrosis (137). In a final illustrative example, Merkulova et al. (109) used proteomic analysis plus genetic knock-down experiments to elucidate the interactome of kidney V-ATPase, a protein that plays a key role in renal acid excretion and blood pH homeostasis, identifying several novel interacting proteins regulating V-ATPase functions. The findings help to explain the pathophysiological roles of V-ATPase in various diseases.

Finally, complete understanding of renal physiology will require the study of the entire nephron, wherein studies of both glomerular and renal tubule physiology are complementary. Another layer of complexity in renal physiology and pathophysiology is in the heterogeneity of kidney tissues at the single-cell/tubule/nephron level. The field of renal single-cell biology has been pioneered by single-cell and single-tubule transcriptomic analyses (24, 95). Although more technically challenging, a recent study has successfully investigated kidney proteomes from mice with proteinuric kidney disease at the single-nephron level (68). In contrast to the situation with renal tubule physiology, proteomic studies involving glomerular biology are often disease focused (139), and phosphoproteomic studies have only been performed very recently (140, 141), 9 yr after the first phosphoproteomic study in renal tubules by Hoffert et al. (66). In addition, the complete understanding of renal physiology will also require more comprehensive, multidimensional investigations of the cellular proteome for each of the more than 26 different kidney cell types (5). Such multidimensional analyses would include the dynamic changes in phosphorylation and other PTMs [ubiquitination (166) and acetylation (176)], together with explorations of protein-protein interactions and protein translocations by advanced proteomic and biochemical techniques (93). The advanced experimental approaches suggested here would generate a detailed multidimensional protein atlas of the human kidney, serving as a key resource for precision medicine and novel targeted strategies for treating kidney diseases. It is highly expected that, when combined with multi-omics and molecular microscope approaches, the proteomic study from renal biopsy(s) of an individual with kidney disease will help deliver accurate diagnosis and personalized treatment to the patient, the concept that is initiated by the Kidney Precision Medicine Project (https://www.niddk.nih.gov/research-funding/research-programs/kidney-precision-medicine-project-kpmp). Last but not least, FIGURE 12 summarizes steps toward new knowledge, knowledge gaps, and potential advances and solutions for proteomic studies, ranging from the creation of a proteomic list to bioinformatic interrogation, inferential interpretation, and formulation of hypotheses and new experiments. The authors anticipate that further partnership between renal physiology and proteome science will continue to be fruitful and productive.

FIGURE 12. — Steps toward new knowledge, knowledge gaps, and potential advances and solutions. The creation of a proteomic list can be improved regarding both qualitative (multidimensional analyses and single-cell/tubule/nephron studies) and quantitative (fractionation techniques and instrumentation) aspects. Bioinformatic interrogation of biological databases cannot be taken as a static process, since these databases are continuously evolving and not always accurate. Thus standardization and quality control of data curation in a dynamic manner are required (191). Functional enrichment analysis is a popular method to identify over- or underrepresented protein groups. This kind of analysis is prone to several sources of bias that can confound the data interpretation, especially in the selection of background comparison list and in the statistical interpretation of high-throughput data (163). Inferential interpretation leading to the formulation of hypotheses and new experiments is a highly complex process. Machine-learning techniques, such as deep learning, have gained much attention over the last few years. Even though these techniques cannot yield an explicit hypothesis or understanding of the underlying mechanisms, they help us draw biologically relevant conclusions from the omics data (23). Lastly, new experiments to validate the previous findings and test new hypotheses are essential, the task that always requires high effort. Automated systems and cloud-based biology are undertaken as a future direction (167a).

GRANTS

M. M. Rinschen was supported by the Deutsche Forschungsgemeinschaft (RI 2811/1 and 2811/2). This work was supported by the Chulalongkorn Academic Advancement into Its 2nd Century Project (CUAASC) and a Kidney Foundation of Thailand research grant.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

ACKNOWLEDGMENTS

Address for reprint requests and other correspondence: T. Pisitkun, National Heart, Lung and Blood Institute, NHLBI 10/6N312, 31 Center Dr., Bethesda, MD 20892 (e-mail: pisitkut@nhlbi.nih.gov) and M. M. Rinschen, University Hospital Cologne, Joseph-Stelzmann-Str. 26, 50931 Cologne, Germany (e-mail: markus.rinschen@uk-koeln.de).

M. M. Rinschen and K. Limbutara contributed equally to this work.

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PERMALINK

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

Markus M Rinschen

Kavee Limbutara

Mark A Knepper

D Michael Payne

Trairak Pisitkun

Abstract

I. FROM PHYSIOLOGY TO PROTEOMICS TO MECHANISMS

A. Physiological Stimuli and Complex Dynamic Biological Responses

FIGURE 1.

1. Protein abundance

2. Posttranslational modifications

3. Protein intermolecular interactions

4. Protein localization

B. Development of Modern High-Throughput Proteomics Techniques for Elucidating Physiological Mechanisms

FIGURE 2.

II. ADDITIONAL CONCEPTS FOR UNDERSTANDING MS-BASED PROTEOMICS

A. Essential Workflow for Typical High-Throughput MS-Based Proteomics

FIGURE 3.

B. Data Acquisition Techniques for MS-Based Proteomics

FIGURE 4.

C. An Overview of Protein/Peptide Quantification Methods for MS-Based Proteomics

FIGURE 5.

III. APPLICATION OF FUNCTIONAL PROTEOMICS TO RENAL TUBULE PHYSIOLOGY

FIGURE 6.

FIGURE 7.

A. Response to Metabolic Acidosis and Angiotensin in the Proximal Tubule

1. Isolation

2. Profiling

Table 1.

3. Proximal tubule response to metabolic acidosis

4. Angiotensin and the proximal tubule

B. Response to Osmotic Stress and Hormonal Activation of cAMP in the Thick Ascending Limb of the Loop of Henle

1. Isolation

2. Profiling

3. Response to osmotic stress

4. Regulation of solute transport by phosphorylation-driven networks

FIGURE 8.

C. Response to Aldosterone and Vasopressin in the Distal Convoluted Tubule and Connecting Tubule

1. Isolation

2. Profiling

3. Response to aldosterone

4. Response to vasopressin

FIGURE 9.

D. Response to Vasopressin in the Collecting Duct

1. Isolation

2. Profiling

3. Response to vasopressin

FIGURE 10.

a) short-term effects.

b) long-term effects.

IV. BIOINFORMATICS TECHNIQUES IN SYSTEMS BIOLOGY STUDIES

A. Drawing Physiological Conclusions From Proteomics Data

FIGURE 11.

1. The folly of validation

2. The folly of fold change

3. Making your data available to other investigators

V. CONCLUSIONS

FIGURE 12.

GRANTS

DISCLOSURES

ACKNOWLEDGMENTS

REFERENCES

ACTIONS

PERMALINK

RESOURCES

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