Cell and Transport Physiology

Combined Proteomics and Pathways Analysis of Collecting Duct Reveals a Protein Regulatory Network Activated in Vasopressin Escape

Ewout J. Hoorn, Jason D. Hoffert and Mark A. Knepper
JASN October 2005, 16 (10) 2852-2863; DOI: https://doi.org/10.1681/ASN.2005030322

Abstract

Low sensitivity is characteristic of many proteomics methods. Presented here is an approach that combines proteomics based on difference gel electrophoresis (DIGE) with bioinformatic pathways analysis to identify both abundant and relatively nonabundant proteins in inner medullary collecting duct (IMCD) altered in abundance during escape from vasopressin-induced antidiuresis. Rats received the vasopressin analog dDAVP by osmotic minipump plus either a daily water load (vasopressin escape) or only enough water to replace losses (control). Immunoblotting confirmed the hallmark of vasopressin escape, a decrease in aquaporin-2, and demonstrated a decrease in the abundance of the urea transporter UT-A3. DIGE identified 22 mostly high-abundance proteins regulated during vasopressin escape. These proteins were analyzed using pathways analysis software to reveal protein clusters incorporating the proteins identified by DIGE. A single dominant cluster emerged that included many relatively low-abundance proteins (abundances too low for DIGE identification), including several transcription factors. Immunoblotting confirmed a decrease in total and phosphorylated c-myc, a decrease in c-fos, and increases in c-jun and p53. Furthermore, immunoblotting confirmed hypothesized changes in other proteins in the proposed network: Increases in c-src, receptor for activated C kinase 1, calreticulin, and caspase 3 and decreases in steroid receptor co-activator 1, Grp78/BiP, and annexin A4. This combined approach proved capable of uncovering regulatory proteins that are altered in response to a specific physiologic perturbation without being detected directly by DIGE. The results demonstrate a dominant protein regulatory network in IMCD cells that is altered in association with vasopressin escape, providing a new framework for further studies of signaling in IMCD.

Escape from the antidiuretic action of vasopressin (“vasopressin escape”) is an important physiologic process that limits the severity of the syndrome of inappropriate antidiuresis (SIADH) and other hyponatremic disorders (1,2). During vasopressin escape, humans and experimental animals undergo a brisk water diuresis, despite high circulating levels of vasopressin (2–4). Vasopressin escape is of considerable clinical importance with regard to SIADH and other vasopressin-dependent dilutional hyponatremic states, because without escape, further water retention and hyponatremia could be fatal (4–6).

Studies in a rat model of vasopressin escape have demonstrated that the central feature of the vasopressin-independent increase in water excretion is a marked suppression of the expression of the water channel aquaporin-2 (AQP2) (2). The fall in AQP2 protein abundance is due in part to decreased levels of AQP2 mRNA in collecting duct (2). This response is associated with a decrease in the capacity of inner medullary collecting ducts (IMCD) to produce cAMP in response to vasopressin (7) in association with a fall in vasopressin-binding capacity of the V2R receptor (8). In contrast, there is upregulation of AQP-3 (2) and the α-subunit of the epithelial Na channel (ENaC) (9), suggesting that the process that is responsible for suppression of AQP2 expression is selective. With regard to possible mediators of escape, roles have been suggested for nitric oxide and prostaglandins (10) and aldosterone (11). However, little is known about which intracellular signaling processes orchestrate the escape and how the vasopressin escape phenomenon is triggered and maintained.

To address which proteins are co-regulated with AQP2 in IMCD during vasopressin escape, we used a relatively new differential proteomics method called difference gel electrophoresis (DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry to identify differentially expressed proteins. Recently, we demonstrated the feasibility of such an approach using DIGE-based proteomics to identify vasopressin-responsive proteins in the collecting duct of Brattleboro rats that were treated with the selective V2R agonist dDAVP (12).

To address further the signaling pathways that are activated during vasopressin escape, we analyzed the DIGE results using bioinformatic pathways analysis (13,14). The pathways analysis software generates hypothetical protein networks, based on large databases of protein interactions culled from the biologic literature, including physical binding reactions, cis-trans interactions in transcriptional regulation, and enzyme–substrate relationships. Such networks can be used to predict signaling pathways that are activated during vasopressin escape. The rationale for using the pathways analysis approach was not only to facilitate the interpretation of the relationships between the identified proteins but also to identify relatively low-abundance proteins (abundances too low for DIGE identification) that may be involved in vasopressin escape (15). The hypothetical changes in these low-abundance proteins then can be tested by immunoblotting. This integrated approach identified a single dominant network of proteins that includes several proteins that may play key regulatory roles in vasopressin escape.

Materials and Methods

Animals and Sample Preparation

Osmotic minipumps (model 2001; Alzet, Palo Alto, CA) that deliver 5 ng/h dDAVP (Peninsula Laboratories, San Carlos, CA; ACUC Protocol 2-KE-3) were implanted subcutaneously in Male Sprague-Dawley rats (Taconic Farms, Germantown, NY). After 3 d, rats were divided into two groups: “escape” and “control.” Escape rats were given excess daily water (0.4 ml/g body wt) via a gelled-agar diet (71% water, 28% finely ground rat food, 1% agar; BACTO-AGAR; Difco Laboratories, Detroit, MI). This diet forced the rats to take the water load to consume the food ration. Control rats were given the same amount of food but with only enough water (0.075 ml/g body wt) to compensate for insensible losses plus 0.015 ml/g body wt per d urine. Rats did not receive ad libitum water. This represents a small modification from our previous studies in which control rats received no water mixed with the food but were allowed to receive ad libitum water (2,7,9). The rats were maintained in metabolic cages in a temperature- and humidity-controlled room with a 12:12-h light-dark cycle, and urine was collected daily for measurement of volume and osmolality. Because the onset of escape is known to occur between 1 and 2 d after the start of water loading (2), three time points were analyzed (four control and four escape for each time point): After 1 and 2 d of water loading (early stages of escape) and after 4 d of water loading (late stages of escape). Thus, a total of 24 rats were separated into 12 control rats and 12 escape rats, and four versus four rats were selected arbitrarily for IMCD analysis at each of the three time points. The day 1 and day 4 time points were selected for DIGE analysis, whereas all three time points were analyzed by immunoblotting. IMCD suspensions were prepared using the method of Stokes et al. (16) with some modifications (17) (Supplemental Materials available online).

Semiquantitative Immunoblotting

Immunoblotting was carried out as described previously (18) (see Supplemental Materials). The IMCD pellet was solubilized in 5× Laemmli sample buffer (1 vol per 4 vol of sample) followed by heating to 60°C for 15 min before electrophoresis. Equal loading was confirmed by staining gels loaded for all three time points (24 samples) with Coomassie blue (18). This loading gel was scanned with a linear fluorescence scanner (Odyssey; Li-Cor Biosciences, Lincoln, NE) at an excitation wavelength of 700 nm (Supplemental Figure 1 available online).

DIGE

DIGE analysis was carried out (for day 1 and day 4 time points, each four versus four samples) as described previously (12,19) (see Supplemental Materials). Briefly, before two-dimensional (2-D) gel electrophoresis, IMCD proteins were solubilized in 2-D sample buffer (7 M urea, 2 M thiourea, 30 mM Tris Cl, and 4% CHAPS [pH 8.5]). The samples were labeled on lysine side chains with Cy3- (control), Cy5- (escape), or Cy2- (mixture of control + escape samples, internal standard) fluorophores using N-hydroxysuccinimide chemistry (Amersham). Isoelectric focusing was performed using an IPGphor apparatus (Amersham, Piscataway, NJ). Isoelectric focusing strips were loaded onto Ettan DALT-6 electrophoresis unit (Amersham) and further separated on a 10% SDS-PAGE gel (5 W/gel).

Fluorescence analytical gel images were obtained (Typhoon scanner; 100 μm resolution; Amersham) using the following emission filters: Cy2 (520 BP 40), Cy3 (580 BP 30), and Cy5 (670 BP 30). Spot matching, quantification, and statistical analyses were performed using DeCyder software (Version 5.0; Amersham). The corresponding Cy3 (control) and Cy5 (escape) images were normalized to the pooled internal standard (Cy2) for that gel using a Least-Means-Squared-Gradient-Descent algorithm. One gel was chosen as the “master,” and all remaining analytical gels were matched and normalized to the Cy2 master spot map. The resulting protein abundance ratios, now represented as standardized log abundance values, were compared using an unpaired t test. The inverse log of these values is presented in Table 1 as protein abundance ratio. A protein abundance ratio >1 corresponds to an increase in escape compared with control samples, whereas a ratio <1 corresponds to a decrease in escape (significance criterion, P ≤ 0.05). For picking, gels were fixed in 30% ethanol/7.5% acetic acid for 2 h followed by SYPRO Ruby (610 BP 30) staining overnight for total protein visualization.

View this table:
Table 1.

IMCD proteins regulated at early (day 1) or later (day 4) stages of vasopressin escapea

A robotic workstation (Ettan; Amersham) was used to excise protein spots, perform in-gel tryptic digestion, extract peptides from the gel, and transfer the extracts onto a MALDI substrate. Spectra were acquired with an ABI 4700 MALDI-TOF/TOF mass spectrometer, and proteins were identified by database matching using Mascot.

Bioinformatic Pathways Analysis

Regulated proteins identified by DIGE were analyzed further by bioinformatic pathways analysis (Ingenuity Pathway Analysis [IPA]; Ingenuity Systems, Mountain View, CA; www.ingenuity.com). IPA constructs hypothetical protein interaction clusters on the basis of a regularly updated “Ingenuity Pathways Knowledge Base.” The Ingenuity Pathways Knowledge Base is a very large curated database that consists of millions of individual relationships between proteins, culled from the biologic literature. These relationships involve direct protein interactions, including physical binding interactions, enzyme substrate relationships, and cis-trans relationships in transcriptional control. The networks are displayed graphically as nodes (individual proteins) and edges (the biologic relationships between the nodes).

In practice, a data set that contains the GenBank identifiers of differentially expressed proteins identified in the DIGE experiment is uploaded into IPA. IPA then builds hypothetical networks from these proteins, and other non–DIGE-identified proteins from the database that are needed fill out a protein cluster. Network generation is optimized for inclusion of as many proteins from the inputted expression profile as possible and aims for highly connected networks.

IPA computes a score for each possible network according to the fit of that network to the inputted proteins. The score is calculated as the negative base-10 logarithm of the P value that indicates the likelihood of the inputted proteins in a given network being found together as a result of random chance. Therefore, scores of 2 or higher have at least a 99% confidence of not being generated by random chance alone. For previous studies using IPA, see Siripurapu et al. (13) and Raponi et al. (14).

Results

Verifying Vasopressin Escape

In this model of vasopressin escape, both control and experimental rats received a continuous dDAVP infusion starting on day −3, but only the experimental rats received a daily water load, mixed with the food, starting on day 0. As previously noted (2,7,9), rats began to “escape” from dDAVP-induced antidiuresis on the second day, i.e., 24 to 48 h after initiation of water loading, as evidenced by a marked increase in urine excretion rate (Figure 1A). Urine osmolality (Figure 1B) was reduced significantly in escape on the second day of water loading. Plasma sodium levels (Figure 1C) showed an acute decrease between days 1 and 2 (from 137 ± 2 to 105 ± 3 mmol/L) and, subsequently, a partial recovery on day 4 (115 ± 3 mmol/L) in response to vasopressin escape. Plasma urea levels were significantly lower in escape animals both at early (day 1: 7.0 ± 0.7 versus 5.0 ± 0.4 mmol/L) and late (day 4: 7.0 ± 0.5 versus 5.0 ± 0.4 mmol/L) stages of vasopressin escape (Figure 1D).

Figure 1.
Figure 1.

Urine excretion rate, urine osmolality, plasma sodium concentration, and plasma urea concentration during vasopressin escape. (A) Urine excretion rate over the course of the experiment with water loading commencing on day 0. Urine excretion rate was increased significantly from day 2 in the escape group relative to control (four versus four rats per time point). Negative time points represent the equilibration period. (B) Urine osmolality over the course of the experiment. Osmolality was decreased significantly from day 1 to the end of the experiment in the escape group relative to control (four versus four rats per time point). (C) Plasma sodium concentrations at days 1, 2, and 4. Plasma sodium was significantly lower in the escape group relative to control on all 3 d (four versus four rats per time point). (D) Plasma urea concentrations at days 1 and 4. Plasma urea was significantly lower in the escape group relative to control on both days (four versus four rats per time point). *P < 0.05 for all.

Changes in Abundances of Transport Proteins in IMCD

Figure 2 shows immunoblots for AQP2, α-ENaC, and collecting duct urea transporters in IMCD on day 4 of vasopressin escape and densitometry values for all three time points. Downregulation of AQP2 and upregulation of α-ENaC is consistent with previous studies (7,9). A novel finding was that UT-A3 showed a 50% decrease in band density on day 2 of vasopressin escape and was decreased further on day 4. Conversely, UT-A1 did not show a significant decrease.

Figure 2.
Figure 2.

Immunoblots showing changes in abundances of aquaporin 2 (AQP2), α subunit of epithelial Na channel (α-ENaC), and collecting duct urea transporters in inner medullary collecting ducts (IMCD) from rats that underwent vasopressin escape versus control rats. Immunoblots are of IMCD cells purified from rat renal medullas at late stage of vasopressin escape (day 4 time point). Each lane is loaded with a sample from a different rat (n = 4 rats per treatment). Thirty micrograms of total protein was loaded in each lane, and the resulting immunoblots were probed with anti-AQP2 antibody (L127), anti–α-ENaC (Q3560-2), anti–UT-A3 (Q2695-2), or anti–UT-A1 (L403). To the right of each immunoblot is a bar graph showing densitometry values for all three time points studied. *P < 0.05.

Changes in Rat IMCD Proteome in Vasopressin Escape

Figure 3 shows an example of a DIGE gel with superimposition of Cy3 (control, red) and Cy5 (escape, green) images of the gel. Spots corresponding to proteins expressed at nearly equal levels in the two samples appear yellow, those upregulated in response to vasopressin escape appear green, and those downregulated in response to vasopressin escape appear red. Flanking the 2-D gel image are 3-D pixel density plots for nine selected proteins identified by MALDI-TOF/TOF mass spectrometry, including heat-shock protein 70 (HSP70), ATP synthase, calreticulin, prohibitin, mitochondrial aconitase, Sec23B, annexin A2, malate dehydrogenase, and protein disulfide isomerase (PDI). PDI had an apparent shift in isoelectric point, suggestive of a posttranslational modification. Only protein spots with statistically significant abundance ratios (P ≤ 0.05 for four pairs of samples) were selected for MALDI-TOF/TOF identification. Moreover, only those identifications with expectation values (i.e., an indicator of the degree of certainty of an identification) larger than 95% were accepted. A total of 22 protein spots were identified by MALDI-TOF/TOF mass spectrometry (Table 1). More proteins with altered abundance levels were identified at day 1 than at day 4. Those identified included proteins that were downregulated and upregulated and one protein that shifted position in the gel (Table 1; Figure 3) in response to vasopressin escape. Also listed in Table 1 are the theoretical molecular weights and theoretical isoelectric points. The molecular weights and isoelectric points for all of these proteins matched those derived from the spot position on the gel, providing additional verification of the identifications.

Figure 3.
Figure 3.

Two-dimensional (2-D) gel showing changes in the IMCD proteome in vasopressin escape. Superimposed images from samples labeled with Cy3 (control, red pseudocolor), and Cy5 (escape, green pseudocolor) and 3-D representation of spot intensities. Spots that appear red or green represent proteins that are respectively down- or upregulated in vasopressin escape, whereas proteins that are equally abundant in both samples appear yellow. Full range of horizontal axis is from 3 pH units (left) to 10 pH units (right). Full range of vertical axis is 15 kD (bottom) to 120 kD (top). pI, isoelectric point; MW, molecular weight.

Pathways Analysis of Vasopressin Escape

Figure 4 shows the largest protein cluster that was generated by the pathways analysis of the proteins listed in Table 1. This “vasopressin escape cluster” consists of a network of 33 proteins, including eight of the 22 proteins that were identified by DIGE-based proteomics and 25 additional proteins that were recognized as being related because of their reported interactions with the proteins identified by DIGE. The nodes represent individual proteins listed by gene name (see Table 2 for glossary), whereas the edges represent the interactions, which include direct physical binding, substrate–enzyme interactions, and/or cis-/trans relationship in transcriptional regulation. In Figure 4, nodes are displayed using different gray levels that represent how the protein was identified and studied. Proteins that were identified by DIGE only are represented as light gray nodes, whereas proteins that were identified by DIGE and studied further by immunoblotting are represented as black nodes. Proteins that were identified by IPA only are represented as white nodes, whereas proteins that were identified by IPA and studied further by immunoblotting are represented as dark gray nodes. Finally, the index protein AQP2 is shown. Because we were chiefly interested in identifying candidate proteins for follow-up by immunoblotting, we did not discriminate between the early and late time points for the pathways analysis. Annotation of the interactions in Figure 4 is provided as Supplemental Materials.

Figure 4.
Figure 4.

Protein regulatory network associated with vasopressin escape. Protein regulatory network was generated by bioinformatic pathways analysis through the use of the Ingenuity Pathways Analysis (IPA) software. Proteins that are listed in Table 1 were analyzed. Individual proteins are displayed as nodes, using different shades of gray to represent how the protein was identified and studied. In addition, different shapes are used to represent the functional class of the gene product (see figure insert). The edges describe the nature of the relationship between the nodes: An edge with arrowhead means that protein A acts on protein B, whereas an edge without an arrowhead represents binding only between two proteins. P indicates phosphorylation as a special case of the former. The gene names associated with the proteins are shown; see Table 2 for glossary. See Supplemental Materials for detailed descriptions of individual protein–protein interactions, including literature references. The overall score for the depicted network was 34, indicating that the probability of matching the indicated proteins by a purely random event was 10−34.

View this table:
Table 2.

Glossary: Protein regulatory network associated with vasopressin escape

Confirmation by Semiquantitative Immunoblotting

Four of the 22 proteins that were identified by DIGE were selected for semiquantitative immunoblotting on the basis of the availability of high-quality antibodies. These were PDI, calreticulin, β-tubulin, and HSP70 (Figure 5). Although DIGE and immunoblotting gave percentage changes that differed somewhat for these four proteins, in general, immunoblotting confirmed the qualitative responses detected with the DIGE technique, as found previously (12,19). Of the 25 proteins identified by pathways analysis as potentially involved in vasopressin escape (Figure 4), 10 were selected for semiquantitative immunoblotting on the basis of the central positions in the protein cluster and of antibody availability. One of these is c-myc, a transcription factor that is regulated in part by phosphorylation. At early stages of escape, total c-myc abundance was decreased, whereas at later stages, the abundance of the phosphorylated form of c-myc was decreased (Figure 6). The abundances of four additional proteins that were identified by pathways analysis were decreased (annexin A4, GRP78/BiP, steroid receptor co-activator 1 [SRC-1], and c-fos; Figure 7A), whereas the abundances of five other proteins were increased (caspase 3, p53, receptor for activated C kinase 1 [RACK1], c-src, and c-jun; Figure 7B). The abundances of the proteins shown in Figures 5, 6, and 7 were changed only at the reported time point and were unchanged at the other time points (data not shown). To address whether the protein abundance changes are specific for the IMCD or occur in other tissues, we immunoblotted renal cortex and brain samples from the same experiments and probed with selected antibodies (Figure 8). Among the responding proteins in IMCD, only c-src showed a similar response in renal cortex, and only HSP70 showed a similar response in brain.

Figure 5.
Figure 5.

Immunoblots confirming selected protein abundance changes identified by DIGE-based proteomics. IMCD suspensions, prepared at the day 1 and day 4 time points. Each lane is loaded with a sample from a different rat (n = 4 rats/treatment). Thirty micrograms of total protein were loaded in each lane, and the resulting immunoblots were probed with (for day 1) anti–protein disulfide isomerase (PDI), anti-calreticulin, anti–β-tubulin, and (for day 4) anti–heat shock protein 70 (HSP70). The apparent change in mobility of β-tubulin may represent an unidentified posttranslational modification. *P < 0.05.

Figure 6.
Figure 6.

Immunoblots of IMCD proteins using antibodies to total and phosphorylated c-myc at early and late stages of vasopressin escape. Each lane is loaded with a sample from a different rat (n = 4 rats/treatment). Thirty micrograms of total protein were loaded in each lane, and the resulting immunoblots were probed with anti–c-myc and anti-phosphorylated c-myc. *P < 0.05.

Figure 7.
Figure 7.

Immunoblots for selected proteins that were identified by pathways analysis. IMCD proteins that were identified by pathways analysis were immunoblotted (30 μg of total protein per lane). Immunoblots show the regulation of proteins at late stages of vasopressin escape (day 4 time point); all proteins were unchanged at early stages of vasopressin escape (day 1; data not shown). Each lane is loaded with a sample from a different rat (n = 4 rats/treatment). Immunoblots were probed with antibodies to proteins indicated at left. *P < 0.05.

Figure 8.
Figure 8.

Immunoblots for selected proteins in renal cortex and brain tissue during vasopressin escape (day 4). Each lane is loaded with a sample from a different rat (n = 4 rats/treatment), using renal cortex (A) and whole brain (B) homogenates. Thirty micrograms of total protein were loaded in each lane, and the resulting immunoblots were probed with antibodies to proteins indicated at left. *P < 0.05.

Discussion

Proteomics analysis is seeing increasing use as a means of identifying new mechanistic hypotheses in physiology (20). An important disadvantage of all 2-D gel-based proteomics approaches is that low-abundance proteins, including many cell-signaling proteins, are unlikely to be identified (15,21). Hence, alternative approaches are needed to discover transcription factors and other regulatory proteins that are involved in the responses to physiologic perturbations. In this study, we used a combination of DIGE-based proteomics and pathways analysis to identify a protein regulatory network whose state is altered in association with the vasopressin escape process and the associated downregulation of AQP2 expression. Proteins that were identified by DIGE were used as input data for bioinformatic pathways analysis, which pointed to several additional proteins that hypothetically could be involved in the escape process. These additional proteins corresponded individually to “hypotheses” that could be tested by immunoblotting asking whether each protein was up- or downregulated as part of the escape process. Indeed, 10 of 10 proteins that were tested in this way displayed the hypothesized regulation (Figures 6 and 7). The outcome of this integrated approach was a cluster of proteins, discussed below, that could participate in the onset and maintenance of escape or alternatively in secondary responses after vasopressin escape. Most of the demonstrated protein abundance changes occurred only in IMCD, not in renal cortex or brain, indicative of a selective response in the IMCD.

UT-A3 but Not UT-A1 Is Downregulated during Vasopressin Escape

As a preliminary step in these experiments, we carried out immunoblotting to verify the downregulation of AQP2 seen in previous studies (2,9). This study confirmed the decrease in AQP2 expression and showed that the α-subunit of ENaC is upregulated in IMCD in the same manner as in more proximal parts of the collecting duct (9). In addition, we extended our studies to collecting duct urea transporter abundances and demonstrated that UT-A3 but not UT-A1 is markedly decreased in abundance during vasopressin escape (Figure 2). The decrease occurred in parallel with the reduction in AQP2. Because both UT-A1 (unchanged) and UT-A3 (downregulated) share the same transcription start site and upstream regulatory regions (22), it is unlikely that transcriptional regulation is the basis of the decrease in UT-A3 expression. Instead, recognizing that the 3′ end of UT-A1 and UT-A3 transcripts differ (22), it seems possible that the differential regulation of these two proteins is based on the different 3′ ends of the mRNA. Both mRNA stability regulation and translational regulation are based on specialized processes involving the 3′ end of mRNA molecules (23), raising the hypothesis that either of these mechanisms may be involved in UT-A3 regulation.

The observed downregulation of UT-A3 could contribute to the increase in water excretion seen during the escape process by a mechanism similar to that demonstrated in knockout mice that lack both UT-A1 and UT-A3 (24). The knockout mice developed a urinary concentrating defect because urea failed to accumulate in the inner medulla, resulting in a urea-dependent osmotic diuresis. Vasopressin escape is associated with modest extracellular fluid volume (ECF) expansion and hypertension (10). This ECF volume expansion could play a role in the decreased expression of collecting duct urea transporters, as suggested in a previous study that implicated aldosterone/salt-induced extracellular volume expansion in regulation of collecting duct urea transporter expression (25). In our study, serum urea levels were markedly decreased as seen previously (11), a presumed consequence of UT-A3 downregulation.

Abundances of Several Transcription Factors Are Altered during Vasopressin Escape Process

Amid the protein regulatory network identified in this study are several transcription factors, whose cellular abundances are presumably too low to be detected via the DIGE technique. Several of these were demonstrated to undergo changes in abundance by immunoblotting. The earliest transcription factor to exhibit abundance changes was c-myc, whose abundance was found to be decreased just before the increase in water excretion (day 1). c-Myc is a basic helix-loop-helix leucine-zipper protein that binds as a heterodimer to so-called E-box cis-elements. The presence of three such E-box elements in the 5′-flanking region of the AQP2 gene (26) raises the possibility that c-myc abundance changes could contribute to the fall in AQP2 expression during the escape response. Although c-myc is best known as a tumor promotor oncogene, it also has physiologic functions in all cells that seem to be related to the state of differentiation and proliferation through general regulation of transcription and translation (27). Its role in translational regulation was revealed in DNA array studies, which showed that changes in c-myc levels are associated with parallel changes in the expression of a host of ribosomal proteins (28). The demonstrated fall in c-myc expression therefore may be expected to be associated with a decrease in total cellular transcription and translation, at least at early stages of escape.

At later stages of vasopressin escape, we found a decrease in phosphorylated c-myc. The antibody recognizes c-myc phosphorylated at threonine-58 and serine-62. These modifications result in a decrease in half-life of the c-myc protein, thereby increasing the abundance of total c-myc. The phosphorylation is mediated largely by two kinases: c-jun N-terminal kinase (29) and glycogen synthase kinase-3 (30). The decrease in phosphorylation may play a role in the restoration of total c-myc toward control levels on day 4 of the vasopressin escape protocol.

In addition to c-myc, several other transcription factors were identified whose abundances were altered at later stages of vasopressin escape, viz. c-fos (decreased), c-jun (increased), and p53 (increased). In addition, the abundance of a transcriptional co-factor, the SRC-1, was decreased.

c-Fos and c-jun together form the transcription factor called “activated protein 1” (AP-1), for which there is an enhancer site in the 5′-flanking region of the human AQP2 gene (31–33). Previously, we demonstrated that c-fos and c-jun are upregulated in the rat renal inner medulla in response to long-term vasopressin infusion (34). Previous studies have demonstrated that AP-1 and a cAMP response element are necessary for maximal transcriptional activation of the AQP2 gene in response to increased intracellular cAMP (31). Conceivably, the demonstrated decrease in c-fos expression contributes to the fall in AQP2 expression in vasopressin escape. c-Fos is itself regulated in part via a cAMP binding element in its 5′-flanking region (33). SRC-1 is a transcriptional co-regulator that interacts with AP-1 and other transcription factors to mediate transcriptional regulation (35,36).

Abundances of Several Other Regulatory Proteins Are Altered during Vasopressin Escape

Aside from transcription factors, the protein regulatory network that was identified by pathways analysis included several other regulatory proteins that potentially could play a role in vasopressin escape. One of these was c-src, a nonreceptor tyrosine kinase whose abundance was increased three-fold during late stages of vasopressin escape (Figure 7). c-Src is a critical protein in the coupling between G-protein–coupled receptors and mitogen-activated protein kinase pathways (37). c-Src as well as some of its substrates binds to β-arrestin 2 (38), a key protein in the internalization of the V2R. Note that c-src was also increased in renal cortical samples (Figure 8), suggesting that c-src upregulation may have been due to a systemic factor. Similarly, HSP70 was decreased in abundance not only in IMCD but also in brain, perhaps related to the fall in systemic tonicity (39). Another protein whose abundance was upregulated was RACK1. Its function seems to be broader than its name suggests, because it constitutes a component of the ribosome and thus may play a role in translational regulation (40). RACK1 is also antiapoptotic and a binding partner for c-src (41). Finally, many of the identified regulated proteins also play a role in the endoplasmic reticulum (ER) stress response, including calreticulin, GRP78/BiP, PDI, and caspase 3. ER stress results from situations in which the protein folding capacity of the ER is exceeded such as generalized acceleration of translation (42).

In conclusion, combined proteomics and pathways analysis served to identify a protein network that is associated with vasopressin escape and contains both high- and low-abundance proteins. The network included several transcription factors that may be involved in vasopressin escape as well as other relatively low-abundance regulatory proteins. These findings provide a new framework for the study of AQP2 regulation in the collecting duct, which is critical to the understanding of SIADH and other forms of hyponatremia.

Acknowledgments

This work was supported by the intramural budget of the National Heart, Lung, and Blood Institute (Z01-HL-01282-KE to M.A.K). E.J.H. was supported by the Dutch Kidney Foundation.

We thank Dr. M. Michalak (University of Alberta, Canada) for kindly providing a calreticulin antibody, Angel Aponte for expert help with 2-D electrophoresis, Ellis Johns for assistance with immunoblotting, David Caden for expert help with blood chemistry, and Dr. R. Zietse for helpful discussions.

Footnotes

  • Published online ahead of print. Publication date available at www.jasn.org.

References

  1. ↵
    Adrogue HJ, Madias NE: Hyponatremia. N Engl J Med 342 : 1581 –1589, 2000
    OpenUrlCrossRefPubMed
  2. ↵
    Ecelbarger CA, Nielsen S, Olson BR, Murase T, Baker EA, Knepper MA, Verbalis JG: Role of renal aquaporins in escape from vasopressin-induced antidiuresis in rat. J Clin Invest 99 : 1852 –1863, 1997
    OpenUrlCrossRefPubMed
  3. Jaenike JR, Waterhouse C: The renal response to sustained administration of vasopressin and water in man. J Clin Endocrinol Metab 21 : 231 –242, 1961
    OpenUrlCrossRefPubMed
  4. ↵
    Schwartz WB, Bennett W, Curelop S, Bartter FC: A syndrome of renal sodium loss and hyponatremia probably resulting from inappropriate secretion of antidiuretic hormone. 1957. J Am Soc Nephrol 12 : 2860 –2870, 2001
    OpenUrlFREE Full Text
  5. Murase T, Ecelbarger CA, Baker EA, Tian Y, Knepper MA, Verbalis JG: Kidney aquaporin-2 expression during escape from antidiuresis is not related to plasma or tissue osmolality. J Am Soc Nephrol 10 : 2067 –2075, 1999
    OpenUrlAbstract/FREE Full Text
  6. ↵
    Verbalis JG: Escape from antidiuresis: A good story. Kidney Int 60 : 1608 –1610, 2001
    OpenUrlCrossRefPubMed
  7. ↵
    Ecelbarger CA, Chou CL, Lee AJ, DiGiovanni SR, Verbalis JG, Knepper MA: Escape from vasopressin-induced antidiuresis: Role of vasopressin resistance of the collecting duct. Am J Physiol 274 : F1161 –F1166, 1998
    OpenUrlPubMed
  8. ↵
    Tian Y, Sandberg K, Murase T, Baker EA, Speth RC, Verbalis JG: Vasopressin V2 receptor binding is down-regulated during renal escape from vasopressin-induced antidiuresis. Endocrinology 141 : 307 –314, 2000
    OpenUrlCrossRefPubMed
  9. ↵
    Ecelbarger CA, Knepper MA, Verbalis JG: Increased abundance of distal sodium transporters in rat kidney during vasopressin escape. J Am Soc Nephrol 12 : 207 –217, 2001
    OpenUrlAbstract/FREE Full Text
  10. ↵
    Song J, Hu X, Khan O, Tian Y, Verbalis JG, Ecelbarger CA: Increased blood pressure, aldosterone activity, and regional differences in renal ENaC protein during vasopressin escape. Am J Physiol Renal Physiol 287 : F1076 –F1083, 2004
    OpenUrlCrossRefPubMed
  11. ↵
    Murase T, Tian Y, Fang XY, Verbalis JG: Synergistic effects of nitric oxide and prostaglandins on renal escape from vasopressin-induced antidiuresis. Am J Physiol Regul Integr Comp Physiol 284 : R354 –R362, 2003
    OpenUrlCrossRefPubMed
  12. ↵
    van Balkom BW, Hoffert JD, Chou CL, Knepper MA: Proteomic analysis of long-term vasopressin action in the inner medullary collecting duct of the Brattleboro rat. Am J Physiol Renal Physiol 286 : F216 –F224, 2004
    OpenUrlCrossRefPubMed
  13. ↵
    Siripurapu V, Meth J, Kobayashi N, Hamaguchi M: DBC2 significantly influences cell-cycle, apoptosis, cytoskeleton and membrane-trafficking pathways. J Mol Biol 346 : 83 –89, 2005
    OpenUrlCrossRefPubMed
  14. ↵
    Raponi M, Belly RT, Karp JE, Lancet JE, Atkins D, Wang Y: Microarray analysis reveals genetic pathways modulated by tipifarnib in acute myeloid leukemia. BMC Cancer 4 : 56 , 2004
    OpenUrlCrossRefPubMed
  15. ↵
    Knepper MA: Proteomics and the kidney. J Am Soc Nephrol 13 : 1398 –1408, 2002
    OpenUrlAbstract/FREE Full Text
  16. ↵
    Stokes JB, Grupp C, Kinne RK: Purification of rat papillary collecting duct cells: functional and metabolic assessment. Am J Physiol 253 : F251 –F262, 1987
    OpenUrlPubMed
  17. ↵
    Chou CL, DiGiovanni SR, Luther A, Lolait SJ, Knepper MA: Oxytocin as an antidiuretic hormone. II. Role of V2 vasopressin receptor. Am J Physiol 269 : F78 –F85, 1995
    OpenUrlPubMed
  18. ↵
    Kim GH, Masilamani S, Turner R, Mitchell C, Wade JB, Knepper MA: The thiazide-sensitive Na-Cl cotransporter is an aldosterone-induced protein. Proc Natl Acad Sci U S A 95 : 14552 –14557, 1998
    OpenUrlAbstract/FREE Full Text
  19. ↵
    Hoffert JD, van Balkom BW, Chou CL, Knepper MA: Application of difference gel electrophoresis to the identification of inner medullary collecting duct proteins. Am J Physiol Renal Physiol 286 : F170 –F179, 2004
    OpenUrlCrossRefPubMed
  20. ↵
    Thongboonkerd V, Barati MT, McLeish KR, Benarafa C, Remold-O’Donnell E, Zheng S, Rovin BH, Pierce WM, Epstein PN, Klein JB: Alterations in the renal elastin-elastase system in type 1 diabetic nephropathy identified by proteomic analysis. J Am Soc Nephrol 15 : 650 –662, 2004
    OpenUrlAbstract/FREE Full Text
  21. ↵
    Thongboonkerd V: Proteomics in nephrology: Current status and future directions. Am J Nephrol 24 : 360 –378, 2004
    OpenUrlCrossRefPubMed
  22. ↵
    Fenton RA, Cottingham CA, Stewart GS, Howorth A, Hewitt JA, Smith CP: Structure and characterization of the mouse UT-A gene (Slc14a2). Am J Physiol Renal Physiol 282 : F630 –F638, 2002
    OpenUrlCrossRefPubMed
  23. ↵
    Audic Y, Hartley RS: Post-transcriptional regulation in cancer. Biol Cell 96 : 479 –498, 2004
    OpenUrlCrossRefPubMed
  24. ↵
    Fenton RA, Chou CL, Stewart GS, Smith CP, Knepper MA: Urinary concentrating defect in mice with selective deletion of phloretin-sensitive urea transporters in the renal collecting duct. Proc Natl Acad Sci U S A 101 : 7469 –7474, 2004
    OpenUrlAbstract/FREE Full Text
  25. ↵
    Wang XY, Beutler K, Nielsen J, Nielsen S, Knepper MA, Masilamani S: Decreased abundance of collecting duct urea transporters UT-A1 and UT-A3 with ECF volume expansion. Am J Physiol Renal Physiol 282 : F577 –F584, 2002
    OpenUrlCrossRefPubMed
  26. ↵
    Ma T, Yang B, Kuo WL, Verkman AS: cDNA cloning and gene structure of a novel water channel expressed exclusively in human kidney: Evidence for a gene cluster of aquaporins at chromosome locus 12q13. Genomics 35 : 543 –550, 1996
    OpenUrlCrossRefPubMed
  27. ↵
    Schmidt EV: The role of c-myc in regulation of translation initiation. Oncogene 23 : 3217 –3221, 2004
    OpenUrlCrossRefPubMed
  28. ↵
    Louro ID, Bailey EC, Li X, South LS, McKie-Bell PR, Yoder BK, Huang CC, Johnson MR, Hill AE, Johnson RL, Ruppert JM: Comparative gene expression profile analysis of GLI and c-MYC in an epithelial model of malignant transformation. Cancer Res 62 : 5867 –5873, 2002
    OpenUrlAbstract/FREE Full Text
  29. ↵
    Noguchi K, Kitanaka C, Yamana H, Kokubu A, Mochizuki T, Kuchino Y: Regulation of c-Myc through phosphorylation at Ser-62 and Ser-71 by c-Jun N-terminal kinase. J Biol Chem 274 : 32580 –32587, 1999
    OpenUrlAbstract/FREE Full Text
  30. ↵
    Gregory MA, Qi Y, Hann SR: Phosphorylation by glycogen synthase kinase-3 controls c-myc proteolysis and subnuclear localization. J Biol Chem 278 : 51606 –51612, 2003
    OpenUrlAbstract/FREE Full Text
  31. ↵
    Yasui M, Zelenin SM, Celsi G, Aperia A: Adenylate cyclase-coupled vasopressin receptor activates AQP2 promoter via a dual effect on CRE and AP1 elements. Am J Physiol 272 : F443 –F450, 1997
    OpenUrlPubMed
  32. Hozawa S, Holtzman EJ, Ausiello DA: cAMP motifs regulating transcription in the aquaporin 2 gene. Am J Physiol 270 : C1695 –C1702, 1996
    OpenUrlPubMed
  33. ↵
    Uchida S, Sasaki S, Fushimi K, Marumo F: Isolation of human aquaporin-CD gene. J Biol Chem 269 : 23451 –23455, 1994
    OpenUrlAbstract/FREE Full Text
  34. ↵
    Brooks HL, Ageloff S, Kwon TH, Brandt W, Terris JM, Seth A, Michea L, Nielsen S, Fenton R, Knepper MA: cDNA array identification of genes regulated in rat renal medulla in response to vasopressin infusion. Am J Physiol Renal Physiol 284 : F218 –F228, 2003
    OpenUrlCrossRefPubMed
  35. ↵
    Spencer TE, Jenster G, Burcin MM, Allis CD, Zhou J, Mizzen CA, McKenna NJ, Onate SA, Tsai SY, Tsai MJ, O’Malley BW: Steroid receptor coactivator-1 is a histone acetyltransferase. Nature 389 : 194 –198, 1997
    OpenUrlCrossRefPubMed
  36. ↵
    Lee SK, Kim HJ, Na SY, Kim TS, Choi HS, Im SY, Lee JW: Steroid receptor coactivator-1 coactivates activating protein-1-mediated transactivations through interaction with the c-Jun and c-Fos subunits. J Biol Chem 273 : 16651 –16654, 1998
    OpenUrlAbstract/FREE Full Text
  37. ↵
    Luttrell LM: ‘Location, location, location:’ Activation and targeting of MAP kinases by G protein-coupled receptors. J Mol Endocrinol 30 : 117 –126, 2003
    OpenUrlAbstract
  38. ↵
    Miller WE, Maudsley S, Ahn S, Khan KD, Luttrell LM, Lefkowitz RJ: Beta-arrestin1 interacts with the catalytic domain of the tyrosine kinase c-SRC. Role of beta-arrestin1-dependent targeting of c-SRC in receptor endocytosis. J Biol Chem 275 : 11312 –11319, 2000
    OpenUrlAbstract/FREE Full Text
  39. ↵
    Zhang Z, Ferraris JD, Brooks HL, Brisc I, Burg MB: Expression of osmotic stress-related genes in tissues of normal and hyposmotic rats. Am J Physiol Renal Physiol 285 : F688 –F693, 2003
    OpenUrlCrossRefPubMed
  40. ↵
    Sengupta J, Nilsson J, Gursky R, Spahn CM, Nissen P, Frank J: Identification of the versatile scaffold protein RACK1 on the eukaryotic ribosome by cryo-EM. Nat Struct Mol Biol 11 : 957 –962, 2004
    OpenUrlCrossRefPubMed
  41. ↵
    Chang BY, Harte RA, Cartwright CA: RACK1: A novel substrate for the Src protein-tyrosine kinase. Oncogene 21 : 7619 –7629, 2002
    OpenUrlCrossRefPubMed
  42. ↵
    Schroder M, Kaufman RJ: ER stress and the unfolded protein response. Mutat Res 569 : 29 –63, 2005
    OpenUrlCrossRefPubMed
Back to top

In this issue

View Selected Citations (0)
Print
Download PDF
Email Article
Citation Tools
Request Permissions
Combined Proteomics and Pathways Analysis of Collecting Duct Reveals a Protein Regulatory Network Activated in Vasopressin Escape
Ewout J. Hoorn, Jason D. Hoffert, Mark A. Knepper
JASN Oct 2005, 16 (10) 2852-2863; DOI: 10.1681/ASN.2005030322
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo

Jump to section