Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Aug 8.
Published in final edited form as: Kidney Int. 2025 Oct 10;109(2):323–336. doi: 10.1016/j.kint.2025.08.039

Inactivation of Pkd2 in adult mice results in delayed cyst formation and identifies sex as a major modifier of disease severity

Patricia Outeda 1, Perry Summers 1, Denis Basquin 1, Ruizhen Xu 1, Jessica Hunt 1, Luis F Menezes 2, Terry Watnick 1
PMCID: PMC13449052  NIHMSID: NIHMS2184642  PMID: 41077129

Abstract

Introduction:

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is caused by mutations in PKD1 or PKD2 and is the most common single-gene disorder resulting in kidney failure. Since deletion of either Pkd1 or Pkd2 in mice is embryonically lethal, conditional alleles have become indispensable tools for studying the pathobiology of ADPKD and for preclinical testing. The rapidity of cyst formation in Pkd1 conditional mice depends on the timing of gene inactivation, but this has never been systematically studied for Pkd2. Here, we characterize the kinetics of cystogenesis in a Pkd2 conditional mouse model and identify associated alterations in gene expression.

Methods:

Using a conditional mouse model, we inactivated Pkd2 at different postnatal stages and characterized the severity of cyst formation. Additionally, we performed RNA-sequencing analysis to identify gene expression changes associated with Pkd2 loss in males and females.

Results:

There is a similar developmental switch in Pkd2 conditional mice with delayed cyst formation after Pkd2 inactivation at or beyond postnatal day 14. Like Pkd1 models, cystogenesis is associated with differential expression of genes involved in metabolism, cell proliferation, and immune response. We confirm that sex is a key modifier of ADPKD progression with differences in disease severity occurring in the context of significant transcriptional differences between males and females that are independent of the Pkd2 genotype.

Conclusions:

Pkd2 conditional mice are a useful model for understanding ADPKD pathogenesis and for testing therapeutics. Our data highlight the importance of sex as a biological variable that should be considered in any study design using orthologous mouse models with late Pkd2 inactivation.

Keywords: ADPKD, animal model, gene expression

Autosomal dominant polycystic kidney disease (ADPKD) is a common genetic disorder that results in end-stage kidney disease because of the replacement of renal tissue with fluid-filled cysts.1 ADPKD is caused by mutations in PKD1 or PKD2, encoding polycystin-1 and polycystin-2, respectively.2-4 The functions of polycystin-1 and polycystin-2 are incompletely understood. One model posits that polycystin-1 and polycystin-2 interact to form a receptor-channel complex that traffics to the primary cilia of renal epithelial cells, where it plays a critical role in maintaining tubular architecture.5-10 ADPKD progression is variable, and several factors have been recognized to affect disease severity. Population studies demonstrate that individuals with PKD1 mutations develop end-stage kidney disease nearly 20 years earlier than those with PKD2.11,12 In addition, among those with PKD1 mutations, protein truncating variants are associated with severe forms of PKD, whereas missense alterations are milder.13 However, even within these groups, there is significant variability, suggesting that there are other factors that impact disease progression.14-16

Mouse models are indispensable for studying the pathobiology of cystic disease and for testing preclinical therapies. Inactivation of Pkd1 in mice during embryonic development or within the first 12 days after birth results in rapid cyst formation (early-onset model).17-19 In contrast, inactivation of Pkd1 at or beyond postnatal day (P) 14 results in delayed cyst development (late-onset model) with a lag phase of $4 months, at which point there is rapid cyst formation and expansion.17-19 Further characterization suggested that this shift in the tempo of cyst initiation takes place on a background of diminishing proliferation and altered gene expression, marking a normal developmental switch that occurs between P11/12 versus P14/15 in wild-type kidneys.18,20 Further studies in the adult-onset Pkd1 model revealed that males had a more severe phenotype compared with females and that sex was correlated with differential expression of genes involved in metabolic pathways and lipid metabolism.21

Although it is presumed that these findings can be extrapolated to Pkd2 mutant mice, this has never been systematically tested.22 The objective of the present study was to characterize the kinetics of cystogenesis and disease progression in a Pkd2 conditional mouse model and to identify associated alterations in gene expression.

METHODS

Animal care and use

All animal experimental procedures were conducted in accordance with the University of Maryland Animal Care and Use Committee guidelines and procedures. Animal models are described in the Supplementary Methods.

RNA-sequencing analysis

RNA sequencing (RNA-seq) was performed at the Institute for Genome Sciences at the University of Maryland School of Medicine. Sequence files were preprocessed and trimmed using fastp 0.23.223 and aligned to mm10 mouse genome using STAR 2.7.10b.24 Count matrices were imported into R 4.3.025 and Rstudio 2023.03.0.38626 and analyzed with DESeq227 with design = ~ group + comparison, where “group” controlled for litter/sex and comparison was either genotype or sex, depending on the analysis. Genes with Benjamini-Hochberg–adjusted P < 0.01 in the corresponding comparisons were considered differentially expressed. Pathway and transcription factor enrichment analyses were performed with clusterProfiler28 and databases in ChEA3.29 Protein-protein physical interaction was performed using STRINGdb30 and STRING mouse database version 12.0, with score threshold of 750 and “physical” network type. Plots were generated using ggplot2,31 ggvenn, UpSetR, enrichplot, and igraph packages in R.

Detailed methods can be found in the Supplementary Methods.

RESULTS

Timing of Pkd2 inactivation determines the rate of PKD progression in mice

To characterize the kinetics of cyst formation, we treated Pkd2fl/fl; Pax8rtTA; TetOCre mice with doxycycline (DOX), resulting in activation of Cre recombinase and Pkd2 deletion throughout the nephron, including proximal and distal tubules and collecting ducts.32,33 To begin, we treated Pkd2fl/fl; Pax8rtTA; TetOCre pups with a single i.p. injection of DOX at time points between P10 and P15. Kidneys were harvested 10 days after inactivation. Control mice (Pkd2fl/fl; Pax8rtTA or Pkd2fl/fl; TetOCre) were treated with DOX and harvested at P20 or P25. As shown in Figure 1, kidneys from pups induced between P10 and P13 were grossly enlarged after 10 days, whereas kidneys induced at P14 and P15 had nearly absent cystic disease (Figure 1a and Supplementary Figure S1A). We quantified the kidney weight–to–body weight ratio (KBW) and found that pups induced at P10 to P13 (and harvested at P20 to P23) had a significant increase in KBW compared with controls. In contrast, the KBW of kidneys harvested at P24 to P25 after induction at P14 to P15 did not differ significantly from control kidneys harvested at P25 (Figure 1b). Hematoxylin and eosin staining confirmed an increased cystic index in pups induced at P10 to P13 (Figure 1c), whereas P24 and P25 kidneys from pups induced at P14 and P15, respectively, had no change in cystic index compared with controls (Figure 1c). Blood urea nitrogen (BUN) levels remained within the normal range in P24 and P25 pups that were induced at P14 and P15. In comparison, BUN levels at P20 and P22 in mice induced at P10 and P12 were significantly elevated (Figure 1d). The median survival for mice that were induced at P10 was 43 days, compared with a median survival of 79 days for males induced at P15 (P < 0.0001) (Figure 1e). All female mice were alive at P116. To determine whether differences in cyst progression were due to Cre-recombinase activity, we evaluated the efficiency of Pkd2 deletion. We induced Cre recombination in pups at P10 and P15 and harvested kidneys 3 days later—at P13 and P18, respectively—before cyst formation began. We isolated renal tubules to enrich for epithelial cells, which are the primary targets of Cre-mediated recombination. Quantitative polymerase chain reaction analysis from renal tubules confirmed efficient deletion of Pkd2 at both time points, with recombination levels of ≈82% to 85% (Supplementary Figure S1B and C).

Figure 1 ∣. Timing of Pkd2 inactivation in mice determines the rate of polycystic kidney disease (PKD) progression.

Figure 1 ∣

Open in a new tab

(a) Representative histology of hematoxylin and eosin–stained kidney sections from Pkd2fl/fl; Pax8rtTA; TetOCre mice treated with a single i.p. dose of doxycycline (50 mg/kg) at postnatal day (P) 10 (N = 8), P11 (N = 6), P12 (N = 9), P13 (N = 11), P14 (N = 11), and P15 (N = 10) and harvested 10 days after Pkd2 inactivation. Control mice lacked either the Pax8rtTA or the TetOCre module and were treated with doxycycline at P10 or P15. Black bar = 2 mm; yellow bar = 250 μm. (b) Comparison of 1 kidney weight–to–body weight ratio (KBW). (c) Comparison of cystic index. (d) Comparison of blood urea nitrogen (BUN) levels. Overall, the phenotype was more variable with doxycycline induction at P13 and P14, which may be due to slight differences in age or stage of renal maturation. Colored bars represent timing of inactivation (red, P10; green, P11; purple, P12; orange, P13; blue, P14; and pink, P15). Solid circles indicate mutants; and diamonds, controls. Statistical significance was determined using multiple group comparisons by ordinary 1-way analysis of variance, followed by Tukey multiple comparisons test. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. (e) Kaplan-Meier survival curves for mice induced at P10 (N = 8) (solid green line) compared with males (dashed blue line) and females (red dashed line) induced at P15 (N = 8 and N = 6, respectively), P < 0.0001. Statistical significance was calculated using the log-rank (Mantel-Cox) test to compare survival between the 3 groups. P < 0.001. Statistical significance between P15 males and P15 female survival curves was calculated using a pairwise log-rank (Mantel-Cox) test. NS, not significant (P > 0.05). To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.

Inactivation of Pkd2 at P15 results in delayed cyst formation with sexual dimorphism

To determine the time course of cyst development after induction at P15, we collected kidneys at P60 (45 days after Pkd2 inactivation). By this time, all mice had evidence of cyst formation (Figure 2a and Supplementary Figure S2A). However, at P60, the KBW and cystic index were significantly reduced in females versus males (Figure 2b and c and Supplementary Figure S2A). Although BUN levels were slightly higher in males compared with females, the difference was not statistically significant (Figure 2d). Analysis of the kidney phenotype in females induced at P15 and harvested at P116 confirmed progression to a severe phenotype, with elevated KBW ratios, cystic index, and BUN levels (Figure 2a-d and Supplementary Figure S2B). Taken together, these findings suggest delayed disease progression in females versus males, with cyst burden ultimately reaching a similar degree of severity. In contrast, we did not observe any difference in KBW or cystic index between males and females in mice induced before P14 or in mice induced at P15 but harvested before the onset of cystogenesis (Supplementary Figure S3).

Figure 2 ∣. Inactivation of Pkd2 at postnatal day 15 (P15) results in delayed cyst formation with sexual dimorphism.

Figure 2 ∣

Open in a new tab

(a) Representative histology of hematoxylin and eosin–stained kidney sections from Pkd2fl/fl; Pax8rtTA;TetOCre mice induced with a single i.p. dose of doxycycline (50 mg/kg) at P15 and harvested at P60, 45 days after Pkd2 inactivation, from males (N = 14) (blue squares) and females (N = 8) (red circles) and from females harvested at P116, 101 days after Pkd2 inactivation (N = 3) (green circles). Bar = 2 mm (left) and 200 μm (right). Quantitative data for (b) 1 kidney weight–to–body weight ratio (KBW), (c) cystic index, and (d) blood urea nitrogen (BUN) levels from males (N = 12) (blue squares), females (N = 6) (red circles), and from females harvested at P116 (N = 3) (green circles). Statistical significance was determined using multiple group comparisons by ordinary 1-way analysis of variance followed by Tukey multiple comparisons test. Data are presented as mean ± SEM. **P < 0.01, ***P < 0.001, ****P < 0.0001. NS, not significant (P > 0.05). To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.

To exclude the possibility of sex differences in Cre activity as a cause of sexual dimorphism in disease progression, we generated Pkd2fl/fl; Pax8rtTA; TetOCre mice with the double-fluorescent mT/mG reporter (Pkd2fl/fl; Pax8rtTA; TetOCre; mT/mG).34 Following induction at P15 and analysis at P20, we observed comparable green fluorescent protein expression in the cortex and the medulla between sexes, supporting the idea that differences in cyst formation are not due to Cre recombinase activity (Supplementary Figure S4A). Additionally, we performed Western blot analysis on kidneys from pups induced at P15 and collected at P25, before cyst formation and observed a ~75% reduction in polycystin-2 expression in both sexes compared with controls (Supplementary Figure S4B and C).

We stained P60 kidney sections with Masson trichrome and anti–α-smooth muscle actin, both markers of fibrosis. Although there was no difference in collagen deposition between males and females, there was an increase in myofibroblasts around developing cysts that was more pronounced in males (Supplementary Figure S5).

Kidney cysts develop asynchronously after Pkd2 inactivation

To determine the origin of cysts after Pkd2 inactivation, we performed immunofluorescence staining of kidney sections with Lotus lectin (LTL) and Dolichos biflorus agglutinin (DBA), marking proximal and distal tubules, respectively. We found that kidney sections collected at P20 after induction at P10 exhibited large DBA-positive cysts, whereas LTL-positive proximal tubules were discreetly dilated or not dilated (Figure 3a). Examination of renal sections from kidneys harvested at P30 (after Pkd2 inactivation at P10) showed that cystic disease had progressed, as evidenced by an increase in KBW and cystic index between P20 and P30 (Figure 3b-d). By this time, cysts clearly displayed proximal and distal tubule markers, with areas of some larger cysts appearing to lose segment-specific markers (Figure 3e).

Figure 3 ∣. Kidney cysts develop asynchronously after Pkd2 inactivation.

Figure 3 ∣

Open in a new tab

(a) Representative kidney image from a Pkd2fl/fl; Pax8rtTA; TetOCre mouse induced at postnatal day 10 (P10) with i.p. doxycycline (50 mg/kg) and harvested at P20 (N = 8). Sections were stained with markers for proximal tubules (Lotus lectin [LTL]; green), distal tubules (Dolichos biflorus agglutinin [DBA]; red), and nuclei (4′ ,6-diamidino-2-phenylindole [DAPI]; blue) and show the presence of large distal cysts and modestly dilated proximal tubules. (b) Representative histology of a hematoxylin and eosin (H&E)–stained kidney section from a mouse treated with doxycycline at P10 as in (a) and harvested 20 days after Pkd2 inactivation at P30 (N = 11). (c,d) Comparison of (c) 1 kidney weight (KW)/body weight (BW) ratio or (d) cystic index for kidneys treated with i.p. doxycycline at P10 and harvested at P20 (red circles; N = 8) versus P30 (purple circles; N = 11). Statistical significance was determined using unpaired 2-tailed t-test. Data are presented as mean ± SEM. ***P < 0.001 and ****P < 0.0001. (e) Representative kidney section from mouse induced at P10 (as in [a]) and harvested at P30, stained with markers for proximal tubules (LTL; green), distal tubules (DBA; red), and nuclei (DAPI; blue). (f) Schematic timeline showing experimental events. Mice were induced with doxycycline (50 mg/kg; i.p.) at P15. Kidneys were harvested at P25 (black dot), P60 (red dot), and P116 (green dot). (g) Representative kidney section collected from a mouse induced with doxycycline at P15 and harvested at P25 and stained with markers for proximal tubules (LTL; green), distal tubules (DBA; red), and nuclei (DAPI; blue) showing the absence of cysts. (h) Representative kidney sections collected from a male (red square; left panel) and a female (red circle; right panel) mouse induced with doxycycline at P15 and harvested at P60. (i) Representative kidney section collected from a female (green dot) induced with doxycycline at P15 and harvested at P116. Sections were stained with markers for proximal tubules (LTL; green) and distal tubules (DBA; red). In the male, cysts stained with LTL and DBA, indicating both proximal and distal tubule origin. Females (right panel; red circle) had distal cysts with only modestly dilated proximal tubule. Larger cysts in both sexes often lacked staining for either LTL or DBA. Females at P116 (green dot) exhibited a more severe phenotype compared with earlier time points (P60), indicating a delayed disease progression. Bars = 2 mm (black); 250 μm (white). To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.

Next, we investigated cyst origin in Pkd2fl/fl; Pax8rtTA; TetOCre mice induced at P15 (Figure 3f). As expected, there were no kidney cysts at P25 (Figure 3g). By P60, however, we observed cysts arising from both proximal and distal tubules in males (Figure 3h, left panel), whereas in females, cysts arose mostly from distal tubules (Figure 3h, right panel). By P116, females exhibited cysts staining for both proximal and distal tubule markers, similar to males at P60 (Figure 3i). To determine whether there were regional differences in Cre activity, we induced Pkd2 deletion in Pkd2fl/fl; Pax8rtTA; TetOCre; mT/mG mice at P10 or P15 and collected tissues at P15 or P20, respectively. We observed comparable green fluorescent protein expression in both the medulla and the cortex, suggesting robust Cre activity in both proximal and distal tubules (Supplementary Figure S4A and Supplementary Figure S6).

To explore the role of proliferation in cyst formation, we costained kidney sections from Pkd2fl/fl; Pax8rtTA; TetOCre mice induced at P15 and harvested at P60 with Ki67, along with LTL and DBA (Supplementary Figure S7). We observed a significantly higher proportion of proliferative (Ki-67+) cells in DBA+ collecting duct segments from males (~25%) compared with females (~15%). In proximal tubules (LTL+ segments), there was a trend toward increased proliferation in males (Supplementary Figure S7). These findings suggest that enhanced proliferation may contribute to the severe cystic phenotype observed in males.

Transcriptomic analysis of Pkd2 mutant kidneys reveals sex as a major determinant of gene expression

We used RNA-seq to investigate the transcriptional underpinnings of cyst formation in Pkd2 mutant kidneys. We decided to focus on the late induction model because this would also allow us to correlate the observed phenotypic differences between males and females with gene expression profiles. We induced Pkd2 deletion in Pkd2fl/fl; Pax8rtTA; TetOCre mice with i.p. DOX at P27 to P29 to avoid the critical window around P14 that determines the kinetics of cyst formation. In addition, this strategy minimizes small differences in the timing of parturition or gestational age. We harvested kidneys at P85 and P125 from 10 males and 10 females for each time point. For each sex, there were N = 5 Cre-positive and N = 5 Cre-negative controls.

At P85, both males and females had early-stage PKD, but cyst formation was more advanced in males, with a statistically significant increase in KBW and cystic index compared with same-sex controls (Supplementary Figures S8 and S9). Females had scattered cysts with a minimal increase in cystic index and no change in KBW. By P125, both male and female kidneys showed more advanced cystic disease with KBW, and cystic index significantly increased compared with controls of the same sex (Figure 4a-c and Supplementary Figure S8). Consistent with observations at P60 and P85, males at P125 continued to have more pronounced cystic disease with higher BUNs compared with females (Figure 4d).

Figure 4 ∣. Transcriptomic analysis of Pkd2 mutant kidneys reveals sex as a major determinant of gene expression.

Figure 4 ∣

Open in a new tab

(a) Representative hematoxylin and eosin–stained kidney sections from Pkd2fl/fl; Pax8rtTA; TetOCre male (M) and female (F) mice and control littermates treated with i.p. doxycycline (50 mg/kg) at postnatal day (P) 27 to P29 and harvested at P125. Bars = 2 mm and 200 μm. Comparison of (b) 1 kidney weight–to–body weight ratio (KBW), (c) cystic index, and (d) blood urea nitrogen (BUN) levels from P125 mutant (red) and control (blue) males (squares) and female (circles) kidneys (N = 5/group/sex) used for transcriptomic analysis. Data are presented as mean ± SEM. Statistical significance was determined using multiple group comparisons by ordinary 1-way analysis of variance followed by Tukey multiple comparisons test. *P < 0.05, **P < 0.01, and ****P < 0.0001. (e) Principal component (PC) plot representing global transcriptional similarities between samples. Note that the second PC mostly separates sex (triangles: males; circles: females). Within sexes, mutants (red) and controls (blue) are also clustered. Circle and triangle sizes represent KBW. (f) Plot showing log2 fold change of male to female comparisons in either wild-type controls (wt.; x axis) or mutants (knockout [ko]; y axis) for all the genes in the panel. Genes where the fold-change difference between mutant and control groups was >3 (off the diagonal) are labeled. Circle size correlates with the number of reads. NS, not significant (P > 0.05). To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.

We conducted bulk RNA-seq analysis of the kidneys harvested at P125. In Figure 4e, the principal component analysis, representing transcriptional similarities between samples, shows that sex is a more significant determinant of sample clustering than Pkd2 mutation status. To determine whether sex-dependent gene expression differences were genotype independent, we plotted log2 fold change of male-to-female comparisons for controls (x axis) and mutants (y axis). We found that the fold-change alterations of differentially expressed genes (DEGs) between sexes in both control and mutant groups show a strong correlation (Pearson correlation = 0.94), suggesting that sexual dimorphism in gene expression is independent of genotype (Figure 4f and Supplementary Figure S10A). Transcription factor enrichment analyses linked estrogen receptor α (ESR1), a hormone receptor that mediates the pleiotropic effects of steroid hormones in males and females, with DEGs in both controls and mutants (Supplementary Figure S10B and C).35-37 Western blot analysis confirmed that the expression level of ESR1 was higher in both male and female mutant P125 kidneys compared with controls (Supplementary Figure S10D and E).

Because males tend to have more severe PKD in our mouse model and in previously published Pkd1 models,21,38 we hypothesized that male-specific, genotype-dependent differences in gene expression might provide clues about disease-modifying pathways. To address this, we compared transcriptional differences between mutants and controls by sex and identified genotype-specific transcriptional differences unique to males or females (Figure 5a and b). As shown in Figure 5a, ~59% of the DEGs were specific to males, whereas ~11% were specific to females, and only ~30% of the DEGs were shared. Despite this, we found a high overall correlation in fold-change expression between mutants versus controls when compared by sex (Pearson correlation = 0.91) (Figure 5b). In fact, even genes statistically significant in only 1 sex showed correlated fold changes between males and females (Pearson correlation = 0.86), suggesting shared cystogenic pathways in males and females. Pathway analysis of DEGs specific to mutant versus control males demonstrated that the most highly enriched biological processes were associated with metabolic pathways (including fatty acid and carboxylic acid metabolism), chemotaxis, and cell migration (Figure 5c). We conducted a similar analysis in females but failed to identify pathways with comparably low P values and gene enrichment, likely because the number of DEGs unique to females was too small for reliable pathway identification.

Figure 5 ∣. Genotype-specific transcriptional differences by gender.

Figure 5 ∣

Open in a new tab

(a) Venn diagram showing differentially expressed genes (DEGs) (adjusted P < 0.01) comparing mutants with controls in males (blue), females (red), and both orange). (b) Plot showing log2 fold change of mutant to control comparisons in either females (x axis) or males (y axis) for genes in panel (a). Genes where the fold-change difference between mutant and control groups was >3 (off the diagonal) are labeled. Circle size correlates with the number of reads. Genes differentially expressed only in males are colored in blue; in females, colored in red; and in both sexes, colored in orange. (c) Dot plot showing top enriched biological process pathways in genes that were differentially expressed only in males.

Gene expression patterns in Pkd2 mutant kidneys correlate with KBW

Our analysis of differential gene expression in male and female mutants and controls suggested that the transcriptional landscape underlying cyst formation is similar in both sexes albeit delayed in females. In this scenario, there could be a core set of DEGs associated with cystogenesis that might have been missed in sex-stratified analyses but reach significance in a combined dataset. To test this, we compared all Pkd2 mutant kidneys with controls, controlling for litter and sex. This analysis identified 3081 DEGs (adjusted P < 0.01, controlled by litter and sex), exceeding the number found in same-sex comparisons (Figure 6a). The observation that some genes reached statistical significance only in the combined male/female analysis suggests that certain gene expression changes are subtle but consistent across sexes.

Figure 6 ∣. Joint analysis of transcriptional differences in Pkd2 mutant kidneys and correlation with kidney weight–to–body weight ratio (KBW).

Figure 6 ∣

Open in a new tab

(a) Venn diagram of differentially expressed genes (adjusted P < 0.01) in mutants versus controls only in males, only in females, or in a combined male and female analysis controlled by sex. (b,c) Volcano plots with genes differentially expressed in the combined male and female analysis (joint male-female), colored on the basis of their absolute correlation with KBW (high correlation: dark red and dark green; low correlation: light red and light green). (b) Genes with an absolute (abs) correlation to KBW of >0.5. (c) Genes with an absolute correlation to KBW of ≤0.5. Genes with adjusted P < 10−20 are labeled. (d,e) Dot plot showing ratio of genes in the differentially expressed group to genes in the category (x axis) for enriched biological process pathways in genes that were differentially expressed and had (e) low (≤0.5) or (d) high (>0.5) absolute correlation with KBW.

To ascertain DEGs that might be relevant for cyst formation or cyst growth, we correlated DEGs with KBW in mutant samples (Figure 6b and c). Pathway analysis was conducted separately for those genes that had a high absolute correlation with KBW (high, >0.5 or <−0.05) (Figure 6b and d) or had little absolute correlation with disease progression as measured by KBW (low, <0.05 or >−0.05) (Figure 6c and e). We found that genes that were highly correlated with KBW ratios (either >0.5 or <−0.05) were enriched in pathways related to organelle fission and cell proliferation (Figure 6d). Among these was cyclin-dependent kinase-1 (Cdk1), which was previously reported to be dysregulated in Pkd1 ADPKD mouse models.39 Genes with relatively low or no correlation with KBW ratios were enriched in pathways involved in the immune response (Figure 6e).

Meta-analysis of the Pkd2 transcriptome

To date, several transcriptomic studies have reported changes in animal and cell models after Pkd2 inactivation.39-43 We reasoned that a joint analysis of our dataset with previously published datasets might identify a core set of transcriptional changes related to Pkd2 loss. Among publicly available RNA-seq datasets, we selected 2 studies for further analysis. The first study (denoted as Somlo) included 3 mutants and 3 control kidneys from a mouse line like ours, Pkd2fl/fl; Pax8rtTA; TetOCre (GSE149739).39 To obtain Pkd2 knockout, the mice were administered oral DOX from P28 to P42, and kidneys were harvested at P70. At this point, mice were reported to have mild tubule dilation but no overt cysts. Both males and females were combined in this dataset. The second study (denoted as Welling) used an inducible immortalized mouse inner medullary collecting duct cell line derived from Pkd2fl/fl; Pax8rtTA; TetOCre; SV40 LTAts mice (GSE171574; N = 8 mutants and N = 8 controls).41 The immortalized conditional cell lines were treated with DOX to induce Pkd2 loss or were vehicle treated. These datasets were chosen because they showed a consistent reduction in Pkd2 transcript in mutant samples , had at least 3 samples per genotype, and because <30% of the genes were differentially expressed.

We reanalyzed the 2 previously published datasets for consistency and then conducted a meta-analysis. Principal component plots show that most of the transcriptional differences between samples can be accounted for by the tissue of origin (Figure 7a), and ≈2% of the variance can be explained by genotype (Figure 7b). The joint analysis of all 3 datasets (“joint all”) had a higher number of DEGs (N = 3196) than any individual dataset because statistical significance was achieved only when the data were combined. The upset plot (Figure 7c) and Venn diagram (Supplementary Figure S11A) show the DEGs that were unique to each dataset. There were only 86 DEGs that were shared across all the analyses (Figure 7c, Supplementary Figure S11A, and Supplementary Table S1). In Figure 7d, we color coded genes based on the number of analyses in which they were differentially expressed (each dataset separately and the joint all analysis). Some of the top DEGs in each individual dataset were not identified as differentially expressed in the “joint all” analysis, likely reflecting differences in background, tissue type, and/or disease state.

Figure 7 ∣. Meta-analysis of the Pkd2 transcriptome.

Figure 7 ∣

Open in a new tab

(a,b) Principal component (PC) plots representing transcriptional profile differences in 3 independent Pkd2 knockout studies (circles: mutant samples; triangles: controls). Blue: inner medullary collecting duct (IMCD) cells (denoted as Welling)41; green: P70 kidneys (denoted as Somlo)40; red: P125 kidneys in the current study (denoted as Outeda). (a) Plot showing PC1 (x axis) and PC2 (y axis). (b) Plot showing PC4 (x axis) and PC8 (y axis). (c) Upset plot showing the size of overlap between the different analyses. A set of 86 genes was differentially expressed in all datasets and when the datasets were analyzed jointly (“joint all”). A set of 383 (297 + 86) were differentially expressed in the joint and kidney datasets (with or without differential expression in the IMCD set). The kidney datasets shared 439 differentially expressed genes (DEGs). (d) Volcano plots showing log2(fold change) of mutant to control (x axis) and corresponding –log10(adjusted P value) (y axis) of genes in each individual dataset. Dots representing genes are colored on the basis of the number of analyses in which they were found to be differentially expressed. Light gray: indicates genes that were not differentially expressed. Dark gray: indicates DEGs in 1 analysis. Green: indicates DEGs in 2 analyses. Orange: indicates DEGs in 3 analyses. Blue: indicates DEGs in 4 analyses (the joint analysis and each individual dataset). The top 5 DEGs in each dataset are labeled, and the boxes are colored on the basis of the number of analyses in which they were found to be differentially expressed (see above). (e) Venn diagram showing DEGs (with adjusted P < 0.01) in mutant versus controls in the kidney datasets (Somlo and Outeda) and in a joint analysis of the kidney datasets (joint kidney). (f) Pathway analysis of the 439 genes differentially expressed in both kidney datasets, showing dot plot of the ratio (x axis) of differentially expressed genes in the category to total number of genes in the category for each enriched biological process (y axis). Dots are colored on the basis of adjusted P value of pathway enrichment.

Because many of the differences in gene expression in the “joint all” analysis were driven by the large number DEGs in the cell line study, we performed a further evaluation of only the kidney datasets. Despite the difference in the number of DEGs (based on P value), the fold change in gene expression between mutants and controls in the 2 datasets was highly correlated (Pearson correlation = 0.95) (Supplementary Figure S11B). We then focused on a set of 439 genes that were differentially expressed in the kidney datasets (Figure 7e and Supplementary Table S2). Pathway analysis of the 439 DEGs in the kidney datasets identified enrichment for processes in cell division and proliferation (Figure 7f). Additional network analysis of 383 DEGs shared between the kidney datasets and the joint analysis showed associations with transcription factors Nanog homeobox (NANOG) and tumor protein P63 (TRP63), linked to proliferation and apoptosis pathways, respectively (Supplementary Figure S12A and B). STRING analysis of protein-protein interactions suggests that several DEGs in the joint all and kidney studies encode physically interacting proteins (Supplementary Figure S13), forming 2 subnetworks with 9 and 21 nodes (Supplementary Figure S14). These subnetworks are enriched in biological processes related to proliferation (Supplementary Figure S14A and B) and immune response (Supplementary Figure S14C and D).

DISCUSSION

Several investigators have demonstrated that Pkd1 inactivation in conditional mouse models before P14 leads to rapid cyst formation within weeks. In contrast, inactivation of Pkd1 after weaning results in severe cystic kidney disease after a lag phase of several months.17-19 These findings have been consistent regardless of the Cre recombinase or Pkd1 conditional mouse line. Careful mapping of this “breakpoint” identified a critical window between P12 and P14 that determines the disease kinetics.18 This switch corresponds to the end of kidney development and is characterized by reduced cell proliferation and by metabolic reprogramming, suggesting that the response to Pkd1 mutation is determined by the renal context in which polycystin signaling is diminished.18,20

Here, we show for the first time that a similar time-dependent switch occurs with Pkd2 deletion. Using a Pkd2 conditional model combined with a DOX-inducible Cre driven by the Pax8 promoter, we found that deletion of Pkd2 at or before P12 triggers rapid cystogenesis, whereas deletion at or after P15 yields a delay of months before the onset of cystogenesis. This difference is unrelated to the degree of Cre recombinase activity in early versus late induction models. As has been described for Pkd1, we observed variability in the severity of cyst formation when Pkd2 loss was induced at P13 to P14, likely because of subtle differences in age (timing of parturition) or in the stage of renal development.18 These findings differ from a previous study where embryonic Pkd2 inactivation using Aqp2-Cre caused severe PKD, but postnatal inactivation at P1 produced no cysts.22 Because early postnatal inactivation of Pkd1 in distal tubules is sufficient to drive cystogenesis, this discrepancy may reflect differences in Cre drivers and induction strategies.17,22,44 Further comparative studies are needed to determine how segment-specific inactivation affects cyst formation kinetics after Pkd2 inactivation.

We also show that cysts in this Pkd2 model arise from both proximal and distal tubules, consistent with Pax8-driven Cre activity. However, we found that cysts develop asynchronously despite little difference observed in Cre activity. We looked at the origin of cysts shortly after Pkd2 deletion in both the early-onset and delayed-onset models and found that distal tubule (DBA-positive) cysts appear earlier than proximal tubule (LTL-positive) cysts, suggesting that the distal tubule/collecting duct may be more sensitive to the loss of polycystin signaling.

Another notable finding of our study is that although the severity of cystic disease was similar between males and females in the early-onset model, there was sexual dimorphism in the setting of late Pkd2 inactivation. After Pkd2 deletion at or beyond P15, males progressed faster with a higher KBW and cystic index in comparison with females. In contrast, females exhibit delayed cystogenesis and survive longer. This is similar to what has been described for Pkd1 conditional models and for human ADPKD and is unrelated to Cre recombinase activity.21,38,45 One possible explanation for the lack of sexual dimorphism in the early-onset model is the absence of hormonal effects before the onset of sexual maturity, which occurs between 6 to 8 weeks of age in mice.46,47

In keeping with the importance of “renal context” as a factor in PKD progression, RNA-seq analysis of kidneys at P125 revealed major sex-specific gene expression differences between male and female kidneys independent of Pkd2 status. When late Pkd2 deletion was superimposed on these sex-related alterations in gene expression, the transcriptional response was similar, albeit reduced, in females, suggesting shared cystogenic pathways in both sexes. In males with more advanced cystic disease, the most highly enriched biological processes were associated with metabolism (including fatty acid and carboxylic acid metabolism), chemotaxis, and cell migration, which is similar to what has been reported in other mouse models.20,21,48-50 Although our study was not designed to address this, we speculate that gene expression analysis of female mice at a later stage would have yielded similar results. Further emphasizing the importance of sex, we found that the transcription factor ESR1 was highly correlated with DEGs in both sexes. In addition, ESR1 protein levels were increased in Pkd2 mutant male and female kidneys compared with controls. ESR1 regulates estrogen-responsive genes involved in sexual development, energy homeostasis, and glucose metabolism in both sexes.51 Interestingly, previous studies using non-orthologous PKD models observed that androgens were a disease progression factor, whereas estrogens had a protective role.52,53 It is possible that the estrogen-mediated signaling may act as a brake on cystogenic pathways, particularly in female mice.

In this report, we sought to define a transcriptional signature that correlates with PKD severity. Not unexpectedly, we found that DEGs related to proliferation pathways were enriched in kidneys with the largest KBW ratios, whereas pathways related to the immune response had little to no correlation with KBW ratios. These findings are consistent with a model in which inflammatory processes play a role in the initial steps of cystogenesis, whereas cell proliferation is associated with disease progression.

We conducted a meta-analysis of our data with 2 publicly available RNA-seq datasets selected using stringent criteria to identify core genes associated with Pkd2 loss.39,41 These datasets were derived from a Pkd2 mutant cell line and a Pkd2 conditional model similar to ours, but at an earlier cystic stage. After reanalyzing the data to ensure consistency, we found that the transcriptional differences were driven more by the tissue of origin rather than genotype. The Pkd2 mutant cell line exhibited the largest number of unique DEGs, and only 86 genes were differentially expressed across all 3 datasets. The 2 kidney datasets, which segregated more closely in 1 principal component analysis, shared 439 DEGs, 383 of which overlapped in the joint analysis of all the datasets (“joint all”). These genes were enriched for cell division and proliferation pathways and consensus sequences related to transcription factors Trp63 and NANOG. Trp63 regulates renal epithelial morphogenesis postinjury, whereas NANOG maintains pluripotency by activating repressors and suppressing activators of cell differentiation in embryonic stem cells and various cancers, including kidney.54-60 The role of NANOG and Trp63 in cystic progression deserves further investigation.

Our analyses also highlight the limitations of combining data across studies. Significant differences detected in a meta-analysis may reflect dataset-specific features to 1 of the datasets rather than a general feature of the disease. Likewise, when combining data from sources that vary in tissue type, genotype, sex, model, or degree of PKD progression, it is important to consider whether the meta-analysis is uncovering common pathways or potentially obscuring true differences. Ultimately, deciding which is true will require experimental validation. On the basis of our meta-analysis, cell-based models lacking the complexity of whole organisms may not fully recapitulate the molecular programs observed in cystic kidney, thereby limiting their utility for predicting therapeutic responses.

In conclusion, our results show that the kinetics of cyst formation in a Pkd2 conditional mouse model are nearly identical to what has been described for Pkd1 and that the models have similar transcriptional profiles. Pkd2 conditional mice can therefore serve as a complementary model to Pkd1 for understanding ADPKD pathogenesis and for testing therapeutics. Additionally, we show that an inducible system, such as the Cre Pax8-rtTA; TetO-Cre, which allows for temporal control of Pkd gene inactivation, permits modeling of adult-onset disease. Our findings confirm that sex is an important modifier of ADPKD progression in the kidney and implicate estrogen-related pathways in this process. Our data highlight the importance of sex as a biological variable that should be considered in any study design using orthologous mouse models with late Pkd2 inactivation.

Supplementary Material

MMC1

Supplementary material is available online at www.kidney-international.org.

Translational Statement.

Mouse models are indispensable tools for studying autosomal dominant polycystic kidney disease (ADPKD), which is caused by mutations in PKD1 or PKD2. We assayed the kinetics of cyst formation in a Pkd2 mouse model that allowed us to control the timing of Pkd2 inactivation. We found that similar to Pkd1, inactivation of Pkd2 after postnatal day 14 resulted in delayed cyst formation that was more severe in males versus females. ADPKD progression in this model was associated with alterations in genes involved in metabolism, cell proliferation, and the immune response. There were remarkable differences in gene expression between male and female kidneys that were independent of the Pkd2 genotype. Taken together, these findings suggest that Pkd2 conditional mice are robust models for exploring ADPKD treatment and pathogenesis. Our results are important because they inform the design of preclinical studies using Pkd2 mouse models.

ACKNOWLEDGMENTS

This work was supported by funding from The National Kidney Foundation of Maryland and The Constance and Carl Ferris Kidney Research Award (University of Maryland, School of Medicine, Department of Nephrology) to PO, the intramural program of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (ZIA DK075042) to LM, and National Institutes of Health (NIH) grant R01DK095036 to TW. These studies used resources provided by the NIDDK-sponsored Polycystic Kidney Disease Research Resource Consortium (1U54DK126114). The authors thank members of the Qian and Cebotaru laboratory for their helpful advice and Dr. Mauban at the University of Maryland School of Medicine’s Confocal Microscopy Core (Baltimore, MD) (S10 OD026698) for imaging guidance. This work used the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov).

Footnotes

DISCLOSURE

All the authors declared no competing interests.

DATA STATEMENT

The RNA-sequencing datasets generated for this study can be found in the Gene Expression Omnibus archive (https://www.ncbi.nlm.nih.gov/gds/) with accession number GSE261420. Access to the data was restricted while the manuscript was reviewed; however, data will be made available to reviewers on request to the Editor. Supplementary Table S1 and Supplementary Table S2 can be accessed via the following link: https://figshare.com/s/20c86e78a7d961547875.

REFERENCES

  • 1.Igarashi P, Somlo S. Genetics and pathogenesis of polycystic kidney disease. J Am Soc Nephrol. 2002;13:2384–2398. [DOI] [PubMed] [Google Scholar]
  • 2.Chapin HC, Caplan MJ. The cell biology of polycystic kidney disease. J Cell Biol. 2010;191:701–710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Harris PC, Torres VE. Polycystic kidney disease. Annu Rev Med. 2009;60: 321–337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Wu G, Somlo S. Molecular genetics and mechanism of autosomal dominant polycystic kidney disease. Mol Genet Metab. 2000;69:1–15. [DOI] [PubMed] [Google Scholar]
  • 5.Hopp K, Hommerding CJ, Wang X, et al. Tolvaptan plus pasireotide shows enhanced efficacy in a PKD1 model. J Am Soc Nephrol. 2015;26: 39–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Hughes J, Ward CJ, Aspinwall R, et al. The polycystic kidney disease 1 (PKD1) gene encodes a novel protein with multiple cell recognition domains. Nat Genet. 1995;10:151–160. [DOI] [PubMed] [Google Scholar]
  • 7.Kim H, Xu H, Yao Q, et al. Ciliary membrane proteins traffic through the Golgi via a Rabep1/GGA1/Arl3-dependent mechanism. Nat Commun. 2014;5:5482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Mochizuki T, Wu G, Hayashi T, et al. PKD2, a gene for polycystic kidney disease that encodes an integral membrane protein. Science. 1996;272: 1339–1342. [DOI] [PubMed] [Google Scholar]
  • 9.Wu G, D’Agati V, Cai Y, et al. Somatic inactivation of Pkd2 results in polycystic kidney disease. Cell. 1998;93:177–188. [DOI] [PubMed] [Google Scholar]
  • 10.Yu S, Hackmann K, Gao J, et al. Essential role of cleavage of polycystin-1 at G protein-coupled receptor proteolytic site for kidney tubular structure. Proc Natl Acad Sci U S A. 2007;104:18688–18693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Hateboer N, Dijk MAV, Bogdanova N, et al. Comparison of phenotypes of polycystic kidney disease types 1 and 2: European PKD1-PKD2 Study Group. Lancet. 1999;353:103–107. [DOI] [PubMed] [Google Scholar]
  • 12.Torra R, Badenas C, Darnell A, et al. Linkage, clinical features, and prognosis of autosomal dominant polycystic kidney disease types 1 and 2. J Am Soc Nephrol. 1996;7:2142–2151. [DOI] [PubMed] [Google Scholar]
  • 13.Hwang Y-H, Conklin J, Chan W, et al. Refining genotype-phenotype correlation in autosomal dominant polycystic kidney disease. J Am Soc Nephrol. 2016;27:1861–1868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Gabow PA, Johnson AM, Kaehny WD, et al. Factors affecting the progression of renal disease in autosomal-dominant polycystic kidney disease. Kidney Int. 1992;41:1311–1319. [DOI] [PubMed] [Google Scholar]
  • 15.Nowak KL, You Z, Gitomer B, et al. Overweight and obesity are predictors of progression in early autosomal dominant polycystic kidney disease. J Am Soc Nephrol. 2018;29:571–578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Torres VE, Grantham JJ, Chapman AB, et al. Potentially modifiable factors affecting the progression of autosomal dominant polycystic kidney disease. Clin J Am Soc Nephrol. 2011;6:640–647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Lantinga-van Leeuwen IS, Leonhard WN, van der Waal A, et al. Kidney-specific inactivation of the Pkd1 gene induces rapid cyst formation in developing kidneys and a slow onset of disease in adult mice. Hum Mol Genet. 2007;16:3188–3196. [DOI] [PubMed] [Google Scholar]
  • 18.Piontek K, Menezes LF, Garcia-Gonzalez MA, et al. A critical developmental switch defines the kinetics of kidney cyst formation after loss of Pkd1. Nat Med. 2007;13:1490–1495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Takakura A, Contrino L, Beck AW, Zhou J. Pkd1 inactivation induced in adulthood produces focal cystic disease. J Am Soc Nephrol. 2008;19: 2351–2363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Menezes LF, Zhou F, Patterson AD, et al. Network analysis of a Pkd1-mouse model of autosomal dominant polycystic kidney disease identifies HNF4α as a disease modifier. PLoS Genet. 2012;8:e1003053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Menezes LF, Lin C-C, Zhou F, Germino GG. Fatty acid oxidation is impaired in an orthologous mouse model of autosomal dominant polycystic kidney disease. EBioMedicine. 2016;5:183–192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Tsilosani A, Gao C, Chen E, et al. Pkd2 deficiency in embryonic Aqp2 + progenitor cells is sufficient to cause severe polycystic kidney disease. J Am Soc Nephrol. 2024;35:398–409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Chen S, Zhou Y, Chen Y, Gu J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–i890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2023. https://www.R-project.org/ [Google Scholar]
  • 26.RStudio. R version 4.1.2. Posit Team, R. 2023. [Google Scholar]
  • 27.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Keenan AB, Torre D, Lachmann A, et al. ChEA3: transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Res. 2019;47:W212–W224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49: D605–D612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Wickham H, Sievert C. ggplot2: Elegant Graphics for Data Analysis. New York: Springer; 2009:10. [Google Scholar]
  • 32.Garcia-Gonzalez MA, Outeda P, Zhou Q, et al. Pkd1 and Pkd2 are required for normal placental development. PLoS One. 2010;5:e12821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Traykova-Brauch M, Schönig K, Greiner O, et al. An efficient and versatile system for acute and chronic modulation of renal tubular function in transgenic mice. Nat Med. 2008;14:979–984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Muzumdar MD, Tasic B, Miyamichi K, et al. A global double-fluorescent Cre reporter mouse. Genesis. 2007;45:593–605. [DOI] [PubMed] [Google Scholar]
  • 35.Acconcia F, Fiocchetti M, Busonero C, et al. The extra-nuclear interactome of the estrogen receptors: implications for physiological functions. Mol Cell Endocrinol. 2021;538:111452. [DOI] [PubMed] [Google Scholar]
  • 36.Aryan L, Younessi D, Zargari M, et al. The role of estrogen receptors in cardiovascular disease. Int J Mol Sci. 2020;21:4314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Moggs JG, Orphanides G. Estrogen receptors: orchestrators of pleiotropic cellular responses. EMBO Rep. 2001;2:775–781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Talbi K, Cabrita I, Schreiber R, Kunzelmann K. Gender-dependent phenotype in polycystic kidney disease is determined by differential intracellular Ca(2+) signals. Int J Mol Sci. 2021;22:6019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Zhang C, Balbo B, Ma M, et al. Cyclin-dependent kinase 1 activity is a driver of cyst growth in polycystic kidney disease. J Am Soc Nephrol. 2021;32:41–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Dixon EE, Maxim DS, Halperin Kuhns VL, et al. GDNF drives rapid tubule morphogenesis in a novel 3D in vitro model for ADPKD. J Cell Sci. 2020;133:jcs249557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Jung HJ, Dixon EE, Coleman R, et al. Polycystin-2-dependent transcriptome reveals early response of autosomal dominant polycystic kidney disease. Physiol Genomics. 2023;55:565–577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Lee EC, Valencia T, Allerson C, et al. Discovery and preclinical evaluation of anti-miR-17 oligonucleotide RGLS4326 for the treatment of polycystic kidney disease. Nat Commun. 2019;10:4148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Woo YM, Kim DY, Koo NJ, et al. Profiling of miRNAs and target genes related to cystogenesis in ADPKD mouse models. Sci Rep. 2017;7: 14151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Leonhard WN, Zandbergen M, Veraar K, et al. Scattered deletion of PKD1 in kidneys causes a cystic snowball effect and recapitulates polycystic kidney disease. J Am Soc Nephrol. 2015;26:1322–1333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Stewart JH. End-stage renal failure appears earlier in men than in women with polycystic kidney disease. Am J Kidney Dis. 1994;24:181–183. [DOI] [PubMed] [Google Scholar]
  • 46.Gore AC, Roberts JL, Gibson MJ. Mechanisms for the regulation of gonadotropin-releasing hormone gene expression in the developing mouse. Endocrinology. 1999;140:2280–2287. [DOI] [PubMed] [Google Scholar]
  • 47.Bell MR. Comparing postnatal development of gonadal hormones and associated social behaviors in rats, mice, and humans. Endocrinology. 2018;159:2596–2613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Chen WC, Tzeng YS, Li H. Gene expression in early and progression phases of autosomal dominant polycystic kidney disease. BMC Res Notes. 2008;1:131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Malas TB, Leonhard WN, Bange H, et al. Prioritization of novel ADPKD drug candidates from disease-stage specific gene expression profiles. EBioMedicine. 2020;51:102585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Sun Y, Liu Z, Cao X, et al. Activation of P-TEFb by cAMP-PKA signaling in autosomal dominant polycystic kidney disease. Sci Adv. 2019;5: eaaw3593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Nilsson S, Gustafsson J. Estrogen receptors: therapies targeted to receptor subtypes. Clin Pharmacol Ther. 2011;89:44–55. [DOI] [PubMed] [Google Scholar]
  • 52.Cowley BD Jr, Rupp JC, Muessel MJ, Gattone VH 2nd. Gender and the effect of gonadal hormones on the progression of inherited polycystic kidney disease in rats. Am J Kidney Dis. 1997;29:265–272. [DOI] [PubMed] [Google Scholar]
  • 53.Stringer KD, Komers R, Osman SA, et al. Gender hormones and the progression of experimental polycystic kidney disease. Kidney Int. 2005;68:1729–1739. [DOI] [PubMed] [Google Scholar]
  • 54.Bussolati B, Bruno S, Grange C, et al. Identification of a tumor-initiating stem cell population in human renal carcinomas. FASEB J. 2008;22:3696–3705. [DOI] [PubMed] [Google Scholar]
  • 55.Chambers I, Colby D, Roberston M, et al. Functional expression cloning of Nanog, a pluripotency sustaining factor in embryonic stem cells. Cell. 2003;113:643–655. [DOI] [PubMed] [Google Scholar]
  • 56.Ezeh UI, Turek PJ, Reijo RA, Clark AT. Human embryonic stem cell genes OCT4, NANOG, STELLAR, and GDF3 are expressed in both seminoma and breast carcinoma. Cancer. 2005;104:2255–2265. [DOI] [PubMed] [Google Scholar]
  • 57.Merrick BA, Martin NP, Brooks AM, et al. Insights into repeated renal injury using RNA-Seq with two new RPTEC cell lines. Int J Mol Sci. 2023;24:14228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Mitsui K, Tokuzawa Y, Itoh H, et al. The homeoprotein Nanog is required for maintenance of pluripotency in mouse epiblast and ES cells. Cell. 2003;113:631–642. [DOI] [PubMed] [Google Scholar]
  • 59.Ye F, Zhou C, Cheng Q, et al. Stem-cell-abundant proteins Nanog, Nucleostemin and Musashi1 are highly expressed in malignant cervical epithelial cells. BMC Cancer. 2008;8:108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Zhang Y, Yan W, Chen X. P63 regulates tubular formation via epithelial-to-mesenchymal transition. Oncogene. 2014;33:1548–1557. [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

MMC1
NIHMS2184642-supplement-MMC1.docx (5.2MB, docx)

Data Availability Statement

The RNA-sequencing datasets generated for this study can be found in the Gene Expression Omnibus archive (https://www.ncbi.nlm.nih.gov/gds/) with accession number GSE261420. Access to the data was restricted while the manuscript was reviewed; however, data will be made available to reviewers on request to the Editor. Supplementary Table S1 and Supplementary Table S2 can be accessed via the following link: https://figshare.com/s/20c86e78a7d961547875.

RESOURCES