Quantitative Proteomics of All 14 Renal Tubule Segments in Rat

Kavee Limbutara; Chung-Lin Chou; Mark A Knepper

doi:10.1681/ASN.2020010071

. 2020 May 1;31(6):1255–1266. doi: 10.1681/ASN.2020010071

Quantitative Proteomics of All 14 Renal Tubule Segments in Rat

Kavee Limbutara ¹, Chung-Lin Chou ¹, Mark A Knepper ^1,^✉

PMCID: PMC7269347 PMID: 32358040

Significance Statement

The renal tubule’s 14 distinct segments consist of epithelial cells with different transport and metabolic functions. Identifying the proteins mediating each function is crucial to gaining an overall understanding of kidney physiology and pathophysiology. New developments in protein mass spectrometry have resulted in a marked increase in sensitivity of protein detection and quantification. In this study, the authors manually microdissected kidney tubules from rat kidneys and leveraged the advances in protein mass spectrometry to identify and quantify the proteins expressed in each of the 14 tubule segments. They used these data to create an online information resource, the Kidney Tubule Expression Atlas, to allow researchers throughout the world to browse segment-specific protein expression data and download them for their own investigations.

Keywords: kidney tubule, Mass spectrometry, Database, Systems Biology

Abstract

Background

Previous research has used RNA sequencing in microdissected kidney tubules or single cells isolated from the kidney to profile gene expression in each type of kidney tubule epithelial cell. However, because proteins, not mRNA molecules, mediate most cellular functions, it is desirable to know the identity and amounts of each protein species to understand function. Recent improvements in the sensitivity of mass spectrometers offered us the ability to quantify the proteins expressed in each of 14 different renal tubule segments from rat.

Methods

We manually dissected kidney tubules from rat kidneys and subjected samples to protein mass spectrometry. We used the “proteomic ruler” technique to estimate the number of molecules of each protein per cell.

Results

Over the 44 samples analyzed, the average number of quantified proteins per segment was 4234, accounting for at least 99% of protein molecules in each cell. We have made the data publicly available online at the Kidney Tubule Expression Atlas website (https://esbl.nhlbi.nih.gov/KTEA/). Protein abundance along the renal tubule for many commonly studied water and solute transport proteins and metabolic enzymes matched expectations from prior localization studies, demonstrating the overall reliability of the data. The site features a “correlated protein” function, which we used to identify cell type–specific transcription factors expressed along the renal tubule.

Conclusions

We identified and quantified proteins expressed in each of the 14 segments of rat kidney tubules and used the proteomic data that we obtained to create an online information resource, the Kidney Tubule Expression Atlas. This resource will allow users throughout the world to browse segment-specific protein expression data and download them for their own research.

The introduction of RNA sequencing (RNA-Seq) has provided an infusion of new information about gene expression in the kidney. The method is extremely sensitive, allowing transcriptomic profiling of individual renal tubules¹ and single cells isolated from kidney.^2–5 RNA-Seq data are very valuable in helping researchers identify hypotheses for further study, supplying what amounts to “instant preliminary data” when provided as online resources. Yet, there is a major limitation in the sense that proteins, not mRNA molecules, are responsible for most biologic functions in the cell. Several studies have demonstrated that protein abundances are often not predictable from mRNA levels^6–11 because protein abundances can be independently regulated by processes that control protein stability and translation.^12,13

Consequently, there is a strong need for quantitative proteomic methods with sensitivity that rivals that of transcriptomics. The problem has been that, although RNA-Seq benefits from PCR amplification, similar amplification is not possible for proteins. Instead, increased sensitivity for mass spectrometry–based proteomics depends on improvements in mass spectrometer sensitivity. Largely because of such progress, Rinschen and colleagues¹⁴ have recently shown that it is possible to obtain deep proteomes from microdissected renal tubules. Here, we use similar techniques to identify proteomes of 14 distinct renal tubule segments, working with microdissected tubules from rats.

Methods

Microdissection

We followed the previously published standard protocol for rat kidney tubule segment microdissection.^1,15 Concisely, male Sprague–Dawley rats age 4–8 weeks (Animal Study Protocol No. H-0110R4; approved by the Animal Care and Use Committee, National Heart, Lung, and Blood Institute) were euthanized by decapitation, and kidneys were perfused via aorta with 10 ml of buffer solution (120 mM NaCl, 5 mM KCl, 2.5 mM Na₂HPO₄, 5 mM HEPES, 1.2 mM MgSO₄, 2 mM CaCl₂, 5 mM sodium acetate, 5.5 mM glucose, adjusted to pH 7.4 by NaOH, bubbled with 100% oxygen) to remove the blood. The kidneys were then further perfused with 10 ml digestion solution containing the same buffer plus either 1 mg/ml of collagenase B (Roche) for cortex or 3 mg/ml for medulla. (Separate rats were used for cortical and medullary dissections.) For medullary tissue, 1 mg/ml (outer medulla) or 3 mg/ml (inner medulla) of hyaluronidase (Worthington Biochemical Corporation) was also added to the digestion solution. Kidneys were removed and cut into thin slices, and then, they were incubated with the same digestion solution at 37°C for 30 minutes (cortex), 40 minutes (outer medulla), or 90 minutes (inner medulla). After the digestion, kidney tubule microdissection was performed under a Wild M8 stereomicroscope. Each tubule segment was distinguished by its characteristics as previously described.¹ A short description of the recognition criteria is given as Supplemental Table 1. Tubule length was measured.¹⁶ Several dissected tubules were pooled together and transferred with 2 μl to a clean petri dish containing ice-cold PBS. Tubules were washed several times with PBS and then lysed in 15 µl of 1.5% SDS/100 mM triethylammonium bicarbonate/1× Halt protease and phosphatase inhibitor by pipetting up and down under a stereomicroscope. Lysed samples were sonicated using a cup horn probe (Misonix Sonicator 3000) for 5 minutes and kept frozen at −80°C until further processed.

Mass Spectrometry–Based Proteomics

For each sample, several tubules were pooled together. Protein lysates were reduced with 10 mM dithiothreitol at 37°C for 30 minutes followed by alkylation using 10 mM iodoacetamide for 30 minutes. The modified single-pot, solid phase–enhanced sample preparation protocol^17,18 was used to clean and digest proteins into peptides for mass spectrometry analysis. The resulting peptide mixtures were then fractionated using microscale basic reverse-phase liquid chromatography¹⁹ into eight fractions (elution buffers: 5%, 7.5%, 10%, 13%, 16%, 20%, 25%, and 30% acetonitrile in 10 mM triethylammonium bicarbonate). Peptide mixture fractions were concatenated (fraction 1 with 5, 2 with 6, 3 with 7, and 4 with 8), resulting in four fractions per sample (except one cortical collecting duct sample that was not concatenated). Fractionated samples were then analyzed with LC-MS/MS using a Dionex UltiMate 3000 nano HPLC system coupled with Orbitrap Fusion Lumos (Thermo Scientific). Peptides were introduced into a nanotrap column at a flow rate of 300 nl/min and then separated on a reverse-phase EASY-Spray PepMap column (C18, 75 µm × 50 cm) using a nonlinear gradient from 2% to 28% acetonitrile in 0.1% formic acid (runtime 120 minutes). Data-dependent acquisition was performed with MS1 resolution of 120,000 and MS2 resolution of 15,000/30,000 at cycle time of 3 seconds.

All mass spectrum raw files were searched against a rat UniProt reference proteome (release 2019_10) using MaxQuant 1.6.10.43.²⁰ Parameters for amino acid modification including fixed carbamidomethyl (C) and variable Oxidation (M), Acetyl (Protein N-term). Match between run option was enabled. Trypsin/P was configured as digestion enzyme. Default settings were used for other parameters.

To estimate protein abundance in each sample, we applied the proteomic ruler approach²¹ using an in-house Python script. Briefly, protein intensities were summed for each sample and used to normalize differences in total protein amount. Molecular weight and number of theoretical peptides (tryptic digested peptides with length 7–30 amino acids) were used to correct for differences in signal intensity caused by protein size and sequence. On the basis of the assumption that total amount of histones is approximately equal to amount of DNA,²¹ total normalized signal intensity of histones in each sample was used to estimate copy number per cell for every protein. Plotting the total histone signal against the estimated number of cells from each sample gave a strong correlation (Supplemental Figure 1). The total cells per sample were estimated for segments other than thin descending limbs from data for cells per unit length curated by Clark et al.²² and multiplied by the total length of tubules in each sample.

Data Availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository²³ with the dataset identifier PXD016958.

Results

Fourteen renal tubule segments were profiled with at least three replicates each. Mass spectra have been archived at PRIDE (Project accession: PXD016958). The terminology used is on the basis of Chen et al.²⁴ and is summarized in Table 1. Over the 44 samples, pooled from 90 rats, the average number of quantified proteins was 4234. Average total tubule length per sample ranged from 16 mm in the S3 proximal tubule to 91 mm in the inner medullary thin descending limb (DTL3). Using measurements of total protein per unit length from Vandewalle et al.,¹⁶ the average protein mass per sample ranged from about 1.7 μg in the DTL2 to about 4.8 μg in the IMCD. Expression levels were quantified as copies per cell (cpc) using the Proteomic Ruler technique.²¹ Values were successfully quantified over six orders of magnitude, a feat unachievable with immunochemical methods. The full dataset can be interrogated at the publicly accessible Kidney Tubule Expression Atlas (KTEA) website (https://esbl.nhlbi.nih.gov/KTEA/), and cpc values for all replicates can be downloaded from a link at the bottom of the “Data Table” tab. The KTEA website has several options for data display and interrogation. In general, data are reproducible across all replicates with median coefficients of variation (SD/mean) for log₁₀-transformed cpc values of <0.14 in all segments (Table 1). Our calculations indicate that the proteome depth obtained in this study is likely to account for at least 99% of the total protein mass in the cell (Supplemental Table 2). Among all proteins quantified, about 25% are “housekeeping proteins,” defined here as being expressed in all 14 tubule segments.¹ Here, we focus on those with various degrees of renal tubule specificity.

Table 1.

Microdissected segments studied

Short Name	Full Name	Origin	No. of Replicates	Length per Sample (mm)^a	No. of Proteins Quantified^a	Median CV of log₁₀ (cpc)^b
S1	First segment of proximal tubule	Cortical labyrinth	3	16.4±2.47	2734±103	0.033
S2	Second segment of proximal tubule	Cortical medullary ray	3	18.3±4.78	4750±415	0.053
S3	Third segment of proximal tubule	Outer stripe of outer medulla	3	15.8±4.94	3508±287	0.033
DTL1	Descending thin limb type 1 (short-looped nephron)	Inner stripe of outer medulla	3	71.5±1.0	4228±19	0.02
DTL2	Descending thin limb type 2 (long-looped nephron)	Inner stripe of outer medulla	3	44.9±8.54	3362±312	0.067
DTL3	Descending thin limb type 3 (long-looped nephron)	Inner medulla	3	91.1±9.0	4426±50.9	0.024
ATL	Ascending thin limb	Inner medulla	3	69.7±10.1	3285±61.5	0.028
mTAL	Medullary thick ascending limb	Inner stripe of outer medulla	4	22.5±7.5	3494±413	0.093
cTAL	Cortical thick ascending limb	Cortical medullary ray	3	36.9±10.4	4762±1228	0.137
DCT	Distal convoluted tubule	Cortical labyrinth	3	27.2±3.6	4854±808	0.083
CNT	Connecting tubule	Cortical labyrinth	3	17.5±4.1	5120±286	0.035
CCD	Cortical collecting duct	Cortical medullary ray	4	32.7±10.2	5265±744	0.068
OMCD	Outer medullary collecting duct	Inner stripe of outer medulla	3	18.2±1.2	3977±216	0.039
IMCD	Inner medullary collecting duct	Inner medulla	3	38.0±8.3	5421±95.1	0.034

Open in a new tab

CV, coefficient of variation.

Values are mean ± SD.

Median CV (SD/mean) of the base 10 logarithm of cpc across all quantified proteins.

Transporters

The various renal tubule segments are distinguished in part functionally by differences in transport. Figure 1 shows the distribution of well studied water and solute transport proteins among the 14 renal tubule segments. In general, the locations and relative abundances of these transport proteins match knowledge from the literature, supporting the validity of the measurements. Examples include Slc5a2 (SGLT2) in S1 proximal tubule²⁵; Slc5a1 (SGLT1) in S2 and S3 proximal tubule²⁶; Clcnka (CLC-Ka chloride channel) in ATL²⁷; Clcnkb (CLC-Kb chloride channel in cTAL through OMCD)²⁸; Slc12a1 (bumetanide-sensitive Na-K-2Cl cotransporter) in mTAL and cTAL²⁹; Slc12a3 (thiazide-sensitive Na-Cl cotransporter) in DCT³⁰; and Aqp2 (aquaporin-2) in CNT through IMCD.³¹ Also, intercalated cell markers like Slc26a4 (pendrin)³² and Slc4a1 (bicarbonate-chloride exchanger 1)³³ were found only in the CNT, CCD, and OMCD (i.e., in the segments that contain intercalated cells).

Metabolic Enzymes

There are also important differences among renal tubule segments in terms of metabolic function. Figure 2 summarizes the distributions of important (generally rate-limiting) nonhousekeeping metabolic enzymes along the renal tubule. In general, the expression patterns shown match prior knowledge about the distribution of metabolic functions along the nephron,³⁴ including (1) absence of glucose utilization (glycolysis) by proximal tubule cells with ATP generation by fatty acid oxidation and amino acid oxidation; (2) selective roles of proximal tubule cells in gluconeogenesis, arginine production, fructose conversion to glucose, and uric acid production; (3) selective production of the osmoprotective enzyme aldose reductase in inner medullary segments; (4) selective expression of rate-limiting enzymes for ammoniagenesis (Pck1 and Glud1) in proximal tubule cells; and (5) maximum abundance of creatine kinase (important for high-energy phosphate buffering) in segments with the highest rates of sodium reabsorption (mTAL, cTAL, DCT) or transport against a large Na gradient (CNT, CCD). As with transporters, the fidelity of the match of metabolic enzyme distribution to prior knowledge further supports the validity of the measurements.

Transcription Factors

The high degree of fidelity to prior knowledge regarding the distribution of transport and metabolic functions demonstrated in Figures 1 and 2 suggests that the data can be used to identify novel hypotheses about other types of function. An example is mapping of tubule segment–specific transcription factors (Figure 3). Here, we used the Correlated Proteins function of our R Shiny-based KTEA website (https://esbl.nhlbi.nih.gov/KTEA/) to identify transcription factors with distributions similar to those of each of the transporters in Figure 1. This analysis identified some transcription factors that have been well characterized with respect to roles in renal tubule–selective gene expression (e.g., Elf5,³⁵ Gata3,³⁶ Hoxb7,^37,38 Foxi1,³⁹ Nfat5,⁴⁰ Pax2,^41,42 and Tfcp2l1⁴³) as well as some for which there is little or no prior knowledge. Many transcription factors were confined to specific regions of the renal tubule: proximal tubule (Hnf1a and Hnf4a), thin limbs of Henle’s loop (Foxc1), distal convoluted tubule (Vdr and Zfp503), general collecting duct (Gata3 and Stat2), non-IMCD collecting duct segments (Foxi1 and Tfcp2l1), and IMCD (Elf5, Hoxb6, Irf3, and Irf9). Others have broader distributions that may be related to specific functions. For example, two transcription factors associated with osmotic regulation (Nfat5⁴⁰ and Pax2⁴¹) were found selectively in inner medullary thin limbs and inner medullary collecting ducts where osmotic regulation is most important. In addition, this analysis identified two transcription factor proteins involved in innate immunity (Irf3 and Irf9) that are selectively expressed in the IMCD segment, providing novel hypotheses about the defense against retrograde introduction of viruses and bacteria into the renal tubules from the pelvic space. A full analysis of IMCD-specific proteins identified several additional proteins involved in innate immunity (Gene Ontology Biologic Process term “innate immune response”), namely Mx1, Mx2, Oasl, Oas1a, Sting1, and Trim21.

Figure 3. — Relative protein expression levels of differentially expressed transcription factors in 14 microdissected rat renal tubule segments. Copy number values are normalized to maximum value for each protein along the renal tubule. Yellow shading is included to provide a facile means of identifying patterns of expression along the renal tubule. See the Data Table tab of the KTEA website to download full data for all replicates.

The Hox family of transcription factors, associated with segmentation of structures in a variety of developmental model systems, is represented by Hoxb6, Hoxb7, Hoxd8, and Hoxd9 (Figure 3). Hoxd9 and Hoxb6 are confined to the early and late parts of the collecting duct system, respectively. In contrast, Hoxd8 spans the boundary between metanephric mesenchyme–derived structures and ureteric bud–derived structures. Hoxb7, generally considered a collecting duct marker, is most strongly expressed in the OMCD and IMCD but is not expressed in the CNT, consistent with the finding that the Hoxb7 promoter does not drive Cre recombinase expression in the CNT.⁴⁴

The Seven-Membrane Spanning Receptors

The analysis also provides a mapping of the most important seven-membrane spanning receptors, which include both G protein–coupled receptors and frizzled receptors (Supplemental Table 3). These proteins include some that are well studied, such as the prostaglandin E2 receptor EP3 subtype (Ptger3) and the calcium-activated receptor (Casr) in the thick ascending limb as well as the V2 vasopressin-receptor (Avpr2) in collecting duct segments. Others, such as the oxytocin receptor in the cortical thick ascending limb, have not been investigated.

Discovering Additional Segment-Specific Proteins

The potential use of the data described in this paper for hypothesis discovery extends beyond transcription factors. Figure 4 shows a general analysis of differentially expressed genes along the renal tubule independent of molecular function. Although many of the proteins reported in this figure are well known segment-specific markers (e.g., Slc34a1 [Na-phosphate cotransporter] in proximal tubule, Spp1 [osteopontin] in descending limb of Henle, Umod [uromodulin] and Casr [calcium receptor] in TAL, Slc12a3 [thiazide-sensitive Na-Cl cotransporter] in DCT, Calb1 [calbindin] in CNT, and Aqp4 in collecting ducts), many are not well characterized with regard to their segment-specific functional roles and may form the basis of future studies. A similar analysis reporting values selective for particular regions of the kidney is shown in Supplemental Figure 2.

Correlation with Transcriptomic Data

As noted in the Introduction, the rationale for proteomic profiling of the nephron versus transcriptomic profiling is that protein levels may not be predictable from transcript levels owing to post-transcriptional regulation. We addressed this issue by looking for the correlation between mRNA abundance profiles along the rat renal tubule¹ and protein abundance profiles from this study (Figure 5). There was a highly significant correlation in all 14 renal tubule segments. However, at any given transcript abundance level, there was a broad range of protein abundances, indicating that there are other determinants of protein abundance. One such determinant is protein half-life. Indeed, when we mapped protein half-life values from global half-life profiling studies in mpkCCD cells^12,13 to protein-mRNA abundance ratios in CCD measured in this study, there was a highly significant correlation (Figure 6). It is likely that variability in protein half-life is also in part due to variable translation efficiency, although we do not have data to test this hypothesis.

Figure 5. — Protein:transcript correlations for all proteins quantified in each renal tubule segment. Overall, the median correlation coefficient among all segments was 0.46. These values are consistent with the conclusion that mRNA level is an important determinant of protein level but that other factors, such as translational efficiency and protein half-life, also are important determinants of protein expression level. TPM, transcripts per million.

Figure 6. — Relationship between protein half-lives and protein-transcript ratios in cortical collecting duct. There is a significant positive correlation between protein half-life and protein-transcript ratio (Pearson r =0.30). Protein half-life data were derived from previously published data in mpkCCD cells (https://hpcwebapps.cit.nih.gov/ESBL/Database/ProteinHalfLives/). TPM, transcripts per million.

Another way to view correlation between mRNA levels and protein levels is in terms of congruence between mRNA and protein expression patterns along the renal tubule. This can be gauged by regression analysis for each gene product, comparing mRNA and protein levels across the 14 renal tubule segments. Figure 7 shows the distribution of correlation coefficients for all genes using Pearson regression analysis. Similar results were found for Spearman regression (Supplemental Figure 3). Here, high-magnitude positive values indicate a high degree of congruence, zero values indicate no congruence, and high-magnitude negative values indicate a mirror image relationship. Although there are many genes with a high degree of congruence, 50% had correlation coefficients <0.37 (median value), suggesting that determinants of protein abundance frequently vary along the renal tubule. Figure 8A shows examples of commonly studied gene products with high congruence, whereas Figure 8B shows examples with low congruence. Overall, the evidence supports the concept that protein abundance levels are not predictable from transcript levels alone.

Figure 7. — Distribution of genewise regression coefficients (Pearson) for correlations between protein (log₂ copy number) and transcript (log₂ TPM) abundance along the renal tubule. The median correlation coefficient was 0.37. Note that many genes had negative correlation coefficients. TPM, transcripts per million.

Figure 8. — Comparison of protein and mRNA abundance profiles along the renal tubule. Average protein copy numbers and average TPM values along the renal tubule are shown. (A) Examples of strongly correlated protein/mRNA. (B) Examples of proteins with different expression pattern from the mRNAs counterpart.

Discussion

This paper reports a new web resource (the KTEA) for support of investigations of renal physiology and pathophysiology on the basis of comprehensive proteomic analysis of microdissected tubules from rats. Overall, relatively deep proteomes were obtained in all 14 renal tubule segments, averaging 4234 quantified proteins per segment. The protein abundance profiles for transporter proteins and metabolic enzymes matched closely with prior data, supporting the validity of the measurements. This match supports the idea that the dataset can be used to reliably identify new hypotheses about other biologic processes in renal epithelial cells. For example, we used the data to identify differentially expressed transcription factors along the renal tubule, including many that have not yet been studied.

The KTEA website, built on a Shiny⁴⁵ platform, has several options for data display and analysis. One of them, the Correlated Proteins function, is particularly useful in identifying proteins whose abundance profiles along the renal tubule are similar. The Correlated Proteins function was used to generate Figure 3. Another function, Profile, used to generate Figure 8 allows users to compare the proteomic profile of a given protein with previously obtained profiles from RNA-Seq analysis of microdissected tubules,¹ useful in identifying sites of post-transcriptional regulation.

Success with this study was made possible by recent increases in sensitivity of commercial mass spectrometers.⁴⁶ It depended also on strategies to capture proteins and to scale down the biochemical procedures needed to prepare the samples for mass spectrometric analysis. The protocol here is similar to that developed for microdissected tubule and single-glomerulus analysis by Rinschen and colleagues.¹⁴ It is conceivable that further technical development will allow quantification of even more proteins. However, our calculations indicate that the proteome depth obtained in this study is likely to account for at least 99% of the total protein mass in each cell type.

The proteomic ruler method provided a means of estimating copy number per cell on the basis of the idea that DNA-cladding histones are expressed at relatively equal amounts in different cell types for diploid cells.²¹ The results, therefore, provide an apt means of determining relative protein abundance among renal tubule cell types. This assumption seems to be validated by the resemblance of expression profiles of many abundant proteins to corresponding profiles among transcripts (Figure 8A). The validity of total histones to provide an index of cell number is supported by Supplemental Figure 1, showing a strong correlation between cell number per sample and total histone signal.

Disclosures

None.

Funding

The work was primarily funded by the National Heart, Lung, and Blood Institute (NHLBI) Division of Intramural Research projects ZIA-HL001285 (to Dr. Knepper) and ZIA-HL006129 (to Dr. Knepper).

Supplementary Material

Supplemental Data

ASN.2020010071SupplementaryData.pdf^{(193.4KB, pdf)}

Acknowledgments

All mass spectrometry measurements were performed in the National Heart, Lung, and Blood Institute (NHLBI) Proteomics Core Facility (M. Gucek, Director). This work also used the computational resources of the National Institutes of Health High Performance Computing Biowulf cluster (https://hpc.nih.gov). We thank the Scientific Information Office of the NHLBI Division of Intramural Research for hosting the Kidney Tubule Expression Atlas website.

Dr. Knepper and Dr. Limbutara designed the study; Dr. Chou and Dr. Limbutara carried out experiments; Dr. Knepper and Dr. Limbutara analyzed the data; Dr. Knepper and Dr. Limbutara made the figures; Dr. Knepper and Dr. Limbutara drafted manuscript; Dr. Limbutara made the website; and Dr. Chou, Dr. Knepper, and Dr. Limbutara approved the final version of the manuscript.

Footnotes

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

Supplemental Material

This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2020010071/-/DCSupplemental.

Supplemental Figure 1. Scatter plot between estimated number of cells and mass spectrometry signal intensity of histone proteins.

Supplemental Figure 2. Heat map of the most differentially expressed proteins in selected regions of the kidney.

Supplemental Figure 3. Distribution of genewise Spearman correlations between protein and mRNA.

Supplemental Table 1. A short description of each renal tubule segment.

Supplemental Table 2. Estimation of percentage of total protein detected.

Supplemental Table 3. The seven-membrane spanning receptors expressed along the renal tubule.

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35.Grassmeyer J, Mukherjee M, deRiso J, Hettinger C, Bailey M, Sinha S, et al.: Elf5 is a principal cell lineage specific transcription factor in the kidney that contributes to Aqp2 and Avpr2 gene expression. Dev Biol 424: 77–89, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]
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39.Blomqvist SR, Vidarsson H, Fitzgerald S, Johansson BR, Ollerstam A, Brown R, et al.: Distal renal tubular acidosis in mice that lack the forkhead transcription factor Foxi1. J Clin Invest 113: 1560–1570, 2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Cai Q, Ferraris JD, Burg MB: High NaCl increases TonEBP/OREBP mRNA and protein by stabilizing its mRNA. Am J Physiol Renal Physiol 289: F803–F807, 2005 [DOI] [PubMed] [Google Scholar]
41.Cai Q, Dmitrieva NI, Ferraris JD, Brooks HL, van Balkom BW, Burg M: Pax2 expression occurs in renal medullary epithelial cells in vivo and in cell culture, is osmoregulated, and promotes osmotic tolerance. Proc Natl Acad Sci U S A 102: 503–508, 2005 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Narlis M, Grote D, Gaitan Y, Boualia SK, Bouchard M: Pax2 and pax8 regulate branching morphogenesis and nephron differentiation in the developing kidney. J Am Soc Nephrol 18: 1121–1129, 2007 [DOI] [PubMed] [Google Scholar]
43.Werth M, Schmidt-Ott KM, Leete T, Qiu A, Hinze C, Viltard M, et al.: Transcription factor TFCP2L1 patterns cells in the mouse kidney collecting ducts. eLife 6: e24265, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Rojek A, Füchtbauer EM, Kwon TH, Frøkiaer J, Nielsen S: Severe urinary concentrating defect in renal collecting duct-selective AQP2 conditional-knockout mice. Proc Natl Acad Sci U S A 103: 6037–6042, 2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Chang W, Cheng J, Allaire JJ, Xie Y, McPherson J: shiny: Web Application Framework for R, 2019. Available at: https://CRAN.R-project.org/package=shiny. Accessed October 10, 2019 [Google Scholar]
46.Rinschen MM, Limbutara K, Knepper MA, Payne DM, Pisitkun T: From molecules to mechanisms: Functional proteomics and its application to renal tubule physiology. Physiol Rev 98: 2571–2606, 2018 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Data

ASN.2020010071SupplementaryData.pdf^{(193.4KB, pdf)}

Data Availability Statement

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[B24] 24.Chen L, Clark JZ, Nelson JW, Kaissling B, Ellison DH, Knepper MA: Renal-tubule epithelial cell nomenclature for single-cell RNA-sequencing studies. J Am Soc Nephrol 30: 1358–1364, 2019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B25] 25.Vallon V, Platt KA, Cunard R, Schroth J, Whaley J, Thomson SC, et al.: SGLT2 mediates glucose reabsorption in the early proximal tubule. J Am Soc Nephrol 22: 104–112, 2011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B26] 26.Turner RJ, Moran A: Heterogeneity of sodium-dependent D-glucose transport sites along the proximal tubule: Evidence from vesicle studies. Am J Physiol 242: F406–F414, 1982 [DOI] [PubMed] [Google Scholar]

[B27] 27.Uchida S, Sasaki S, Nitta K, Uchida K, Horita S, Nihei H, et al.: Localization and functional characterization of rat kidney-specific chloride channel, ClC-K1. J Clin Invest 95: 104–113, 1995 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B28] 28.Kobayashi K, Uchida S, Mizutani S, Sasaki S, Marumo F: Intrarenal and cellular localization of CLC-K2 protein in the mouse kidney. J Am Soc Nephrol 12: 1327–1334, 2001 [DOI] [PubMed] [Google Scholar]

[B29] 29.Kaplan MR, Plotkin MD, Lee WS, Xu ZC, Lytton J, Hebert SC: Apical localization of the Na-K-Cl cotransporter, rBSC1, on rat thick ascending limbs. Kidney Int 49: 40–47, 1996 [DOI] [PubMed] [Google Scholar]

[B30] 30.Bachmann S, Velázquez H, Obermüller N, Reilly RF, Moser D, Ellison DH: Expression of the thiazide-sensitive Na-Cl cotransporter by rabbit distal convoluted tubule cells. J Clin Invest 96: 2510–2514, 1995 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B31] 31.Nielsen S, DiGiovanni SR, Christensen EI, Knepper MA, Harris HW: Cellular and subcellular immunolocalization of vasopressin-regulated water channel in rat kidney. Proc Natl Acad Sci U S A 90: 11663–11667, 1993 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B32] 32.Royaux IE, Wall SM, Karniski LP, Everett LA, Suzuki K, Knepper MA, et al.: Pendrin, encoded by the Pendred syndrome gene, resides in the apical region of renal intercalated cells and mediates bicarbonate secretion. Proc Natl Acad Sci U S A 98: 4221–4226, 2001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B33] 33.Ercolani L, Brown D, Stuart-Tilley A, Alper SL: Colocalization of GAPDH and band 3 (AE1) proteins in rat erythrocytes and kidney intercalated cell membranes. Am J Physiol 262: F892–F896, 1992 [DOI] [PubMed] [Google Scholar]

[B34] 34.McDonough A, Thomson S: Metabolic basis of solute transport. In: Brenner and Rector’s The Kidney, edited by Taal MW, Chertow GM, Marsden PA, Skorecki K, Yu AS, Brenner BM, Philadelphia, Elsevier Saunders, 2012, pp 138–157 [Google Scholar]

[B35] 35.Grassmeyer J, Mukherjee M, deRiso J, Hettinger C, Bailey M, Sinha S, et al.: Elf5 is a principal cell lineage specific transcription factor in the kidney that contributes to Aqp2 and Avpr2 gene expression. Dev Biol 424: 77–89, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B36] 36.Uchida S, Matsumura Y, Rai T, Sasaki S, Marumo F: Regulation of aquaporin-2 gene transcription by GATA-3. off. Biochem Biophys Res Commun 232: 65–68, 1997 [DOI] [PubMed] [Google Scholar]

[B37] 37.Srinivas S, Goldberg MR, Watanabe T, D’Agati V, al-Awqati Q, Costantini F: Expression of green fluorescent protein in the ureteric bud of transgenic mice: A new tool for the analysis of ureteric bud morphogenesis. Dev Genet 24: 241–251, 1999 [DOI] [PubMed] [Google Scholar]

[B38] 38.Plaisier E, Ribes D, Ronco P, Rossert J: Identification of two candidate collecting duct cell-specific cis-acting elements in the Hoxb-7 promoter region. Biochim Biophys Acta 1727: 106–115, 2005 [DOI] [PubMed] [Google Scholar]

[B39] 39.Blomqvist SR, Vidarsson H, Fitzgerald S, Johansson BR, Ollerstam A, Brown R, et al.: Distal renal tubular acidosis in mice that lack the forkhead transcription factor Foxi1. J Clin Invest 113: 1560–1570, 2004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B40] 40.Cai Q, Ferraris JD, Burg MB: High NaCl increases TonEBP/OREBP mRNA and protein by stabilizing its mRNA. Am J Physiol Renal Physiol 289: F803–F807, 2005 [DOI] [PubMed] [Google Scholar]

[B41] 41.Cai Q, Dmitrieva NI, Ferraris JD, Brooks HL, van Balkom BW, Burg M: Pax2 expression occurs in renal medullary epithelial cells in vivo and in cell culture, is osmoregulated, and promotes osmotic tolerance. Proc Natl Acad Sci U S A 102: 503–508, 2005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B42] 42.Narlis M, Grote D, Gaitan Y, Boualia SK, Bouchard M: Pax2 and pax8 regulate branching morphogenesis and nephron differentiation in the developing kidney. J Am Soc Nephrol 18: 1121–1129, 2007 [DOI] [PubMed] [Google Scholar]

[B43] 43.Werth M, Schmidt-Ott KM, Leete T, Qiu A, Hinze C, Viltard M, et al.: Transcription factor TFCP2L1 patterns cells in the mouse kidney collecting ducts. eLife 6: e24265, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B44] 44.Rojek A, Füchtbauer EM, Kwon TH, Frøkiaer J, Nielsen S: Severe urinary concentrating defect in renal collecting duct-selective AQP2 conditional-knockout mice. Proc Natl Acad Sci U S A 103: 6037–6042, 2006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B45] 45.Chang W, Cheng J, Allaire JJ, Xie Y, McPherson J: shiny: Web Application Framework for R, 2019. Available at: https://CRAN.R-project.org/package=shiny. Accessed October 10, 2019 [Google Scholar]

[B46] 46.Rinschen MM, Limbutara K, Knepper MA, Payne DM, Pisitkun T: From molecules to mechanisms: Functional proteomics and its application to renal tubule physiology. Physiol Rev 98: 2571–2606, 2018 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Quantitative Proteomics of All 14 Renal Tubule Segments in Rat

Kavee Limbutara

Chung-Lin Chou

Mark A Knepper

Significance Statement

Abstract

Background

Methods

Results

Conclusions

Methods

Microdissection

Mass Spectrometry–Based Proteomics

Data Availability

Results

Table 1.

Transporters

Figure 1.

Metabolic Enzymes

Figure 2.

Transcription Factors

Figure 3.

The Seven-Membrane Spanning Receptors

Discovering Additional Segment-Specific Proteins

Figure 4.

Correlation with Transcriptomic Data

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Discussion

Disclosures

Funding

Supplementary Material

Acknowledgments

Footnotes

Supplemental Material

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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