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Article

Transcriptomics of Congenital Hepatic Fibrosis in Autosomal Recessive Polycystic Kidney Disease Using PCK Rats

by
Satyajeet Khare
1,
Lu Jiang
2,
Diego Paine-Cabrera
2,
Udayan Apte
2,3,* and
Michele T. Pritchard
2,3,*
1
Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune 412115, India
2
Department of Pharmacology, Toxicology and Therapeutics, University of Kansas Medical Center, Kansas City, KS 66160, USA
3
The Liver Center, University of Kansas Medical Center, Kansas City, KS 66160, USA
*
Authors to whom correspondence should be addressed.
Livers 2023, 3(3), 331-346; https://doi.org/10.3390/livers3030025
Submission received: 13 April 2023 / Revised: 3 July 2023 / Accepted: 17 July 2023 / Published: 21 July 2023
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Abstract

:
Congenital hepatic fibrosis/Autosomal recessive polycystic kidney disease (CHF/ARPKD) is an inherited neonatal disease induced by mutations in the PKHD1 gene and characterized by cysts and robust pericystic fibrosis in the liver and kidneys. The PCK rat is an excellent animal model that carries a Pkhd1 mutation and exhibits similar pathophysiology. We performed RNA-Seq analysis on liver samples from PCK rats over a time course of postnatal day (PND) 15, 20, 30, and 90 using age-matched Sprague Dawley (SD) rats as controls to characterize molecular mechanisms of CHF/ARPKD pathogenesis. A comprehensive gene expression analysis identified 1298 differentially expressed genes (DEGs) between PCK and SD rats. The genes overexpressed in the PCK rats at PND30 and 90 were involved cell migration (e.g., Lamc2, Tgfb2, and Plet1), cell adhesion (e.g., Spp1, Adgrg1, and Cd44), and wound healing (e.g., Plat, Celsr1, Tpm1). Connective tissue growth factor (Ctgf) and platelet-derived growth factor (Pdgfb), two genes associated with fibrosis, were upregulated in PCK rats at all time points. Genes associated with MHC class I molecules (e.g., RT1-A2) or involved in ribosome assembly (e.g., Pes1) were significantly downregulated in PCK rats. Upstream regulator analysis showed activation of proteins involved tissue growth (MTPN) inflammation (STAT family members), chromatin remodeling (BRG1), reduction in fibrosis (SMAD7), and inhibition of proteins involved in hepatic differentiation (HNF4α). Immunofluorescence staining revealed that cyst wall epithelium cells also express hepatic progenitor cell markers. The increase in mRNAs of four top upregulated genes, including Reg3b, Aoc1, Tm4sf20, and Cdx2, was confirmed at the protein level using immunohistochemistry. In conclusion, these studies indicate that a combination of increased inflammation, cell migration, wound healing, decreased antifibrotic gene expression, and inhibition of hepatic function are the major underlying pathogenic mechanisms in CHF/ARPKD.

1. Introduction

Autosomal recessive polycystic kidney disease (ARPKD) is a hereditary disorder caused by mutations in PKHD1 [1]. It is a ciliopathy where the growth and function of the primary cilium is affected on several types of epithelial cells, including renal tubule cells and biliary tree cholangiocytes, leading to cystogenesis. PKHD1 codes for the protein fibrocystin, which is expressed on the primary cilium in the epithelial cells, and mutations in PKHD1 disrupt primary cilium function [2,3]. The clinical spectrum is widely variable, with multiple organs affected, but primarily the kidneys and the liver. Kidney disease is characterized by tubule dilation and focal cyst development in collecting ducts [4,5]. Liver disease in ARPKD is characterized by congenital hepatic fibrosis (CHF), which involves development of segmental dilations of bile ducts accompanied by periportal fibrosis [6]. CHF causes significant morbidity and mortality in ARPKD patients who develop portal hypertension [7]. Although the mechanisms driving hepatic cyst expansion are not well characterized in human CHF/ARPKD, different factors, such as cholangiocyte hyperproliferation and fluid secretion, and perhaps altered cell–cell and cell–matrix biomechanics [8], may contribute to the progression of cysts [9]. One of the best-characterized animal models of CHF/ARPKD is the PCK rat, derived from a colony of Sprague Dawley (SD) rats at Charles River Laboratories, Wilmington, MA, USA [10]. The PCK rat carries a gene mutation that is orthologous to human PKHD1 [11]. As a spontaneously occurring, inherited model of ARPKD, PCK rat hepatorenal fibrocystic disease bears a striking resemblance to human ARPKD [12].
The mechanisms underlying hepatic cystogenesis and progression of CHF/ARPKD are not completely known. We previously proposed a ‘pathogenic triumvirate’ to describe the three pathogenic components in CHF/ARPKD, which include cyst growth, inflammation, and fibrosis [13]. We focused specifically on hepatic cystogenesis because CHF/ARPKD manifests itself during perinatal period. Babies with CHF/ARPKD suffer from hepatic insufficiency and pulmonary hypertension secondary to cyst growth and hepatic fibrosis. Molecular mechanisms that drive the pathogenesis of CHF/ARPKD and underlying transcriptomic changes are not known. Using RNA-seq analysis to analyze liver samples from SD rats and PCK rats at different time points after birth, we found that differentially expressed genes, transcription factors, and canonical pathways in PCK rats could be categorized into one of these three components. In addition, we identified several genes and transcription factors related to liver development and metabolism in PCK rats that may contribute to the pathogenesis of CHF/ARPKD and confirmed a subset of the top upregulated genes in PCK rat tissue. Overall, our study provides a comprehensive characterization of gene expression profile in CHF/ARPKD. By identifying potential regulators of the ‘pathogenic triumvirate’ and other possible contributors of CHF/ARPKD, we propose several molecular targets of the disease that can be explored in further mechanistic studies and with the overall aim of developing novel therapeutic strategies.

2. Materials and Methods

2.1. Animals

Male and female PCK rats and Sprague Dawley (SD) rats (control strain for PCK) were purchased from Charles River and housed in an AAALAC-accredited vivarium at the University of Kansas Medical Center (KUMC) and used to generate rat pups for these studies. All animal studies were approved by the KUMC Institutional Animal Care and Use Committee (IACUC) and were performed in accordance with The Guide for the Care and Use of Laboratory Animals (8th Edition).

2.2. Liver Tissue Collection and Storage

Rat pups were aged to postnatal days (PNDs) 15, 20, 30, or 90 and then euthanized by isoflurane inhalation overdose. Once euthanized, the livers were excised, dissected into separate lobes, and further cut into smaller pieces. Some liver pieces were snap frozen in liquid nitrogen and stored at −80 °C for RNA sequencing analysis, whereas other liver pieces were placed into histological cassettes and submerged in 10% neutral buffered formalin for histological analysis. Paraffin-embedded liver sections were used for H&E and Mason’s Trichrome staining.

2.3. Sample Preparation and RNA Isolation

Liver samples were collected from male and female SD and PCK rats at postnatal days (PNDs) 15, 20, 30, and 90 (n = 3 each time point for each genotype, 2 males and one female). Previously snap-frozen liver pieces (20–30 mg) were transferred to 1.5 mL of RNAlater-ICE (Ambion Inc., Austin, TX, USA) and stored at −80 °C overnight before RNA isolation. Total RNA was isolated with the RNeasy Mini Kit (Qiagen, Valencia, CA, USA) after homogenization using a bead homogenizer with Lysing Matrix D homogenization tubes (MP Biomedicals Fast Prep 24, Solon, OH, USA). The total RNA concentration and purity were determined using a NanoDrop microvolume spectrophotometer (Thermo Fisher Scientific, Lenexa, KS, USA) apparatus.

2.4. RNA Library Preparation and Data Collection

Total RNA (500 ng) was used to construct the Stranded mRNA-Seq library. Briefly, the total RNA fraction was fragmented, reverse transcribed into cDNA, and ligated with the appropriate indexed adaptors using the TruSeq Stranded mRNA LT Sample Preparation Kit (Illumina, San Diego, CA, USA). The quality of library was validated by an Agilent Bioanalyzer. Library quantification was measured using the Roche Lightcycler96 with FastStart Essential DNA Green Master Mix. Library concentrations were adjusted to 4 nM and pooled for multiplexed sequencing. Libraries were denatured and diluted to the appropriate pM concentration, followed by clonal clustering onto the sequencing flow cell, using the TruSeq Paired-End (PE) Cluster Kit v3-cBot-HS. The clonal clustering procedure was automated using the Illumina cBOT Cluster Station. The clustered flow cell was sequenced on the Illumina HiSeq 2500 Sequencing System using the TruSeq SBS Kit v3-HS. Sequence data were converted to Fastq format from Bcl files and de-multiplexed into individual sequences for further downstream analysis.

2.5. Bioinformatic Analysis for RNA-Seq

The quality of sequence reads was confirmed using FASTQC. Reads were aligned to the reference genome Rnor 6.0 from Ensembl [14] using HiSAT2 [15]. The bam files generated were used to create an expression matrix using feature Counts [16]. Differential expression analysis was performed on the count matrix using DESeq2 [17]. Differentially expressed genes identified were subjected to enrichment analysis using clusterProfiler [18]. Expression plots were generated using normalized CPM values in RStudio [19].
Pathway analysis was performed using Ingenuity Pathway Analysis (IPA, Ingenuity Systems, www.ingenuity.com, accessed on 1 May 2017). Differential expression was determined by absolute fold change >1.5 and a q-value (the p-value adjusted for multiple hypothesis correction using the Benjamini-Hochberg procedure) less than or equal to 0.05. IPA upstream analysis was performed to predict the activated and inhibited upstream regulators using activation of z-score algorithm. The activation Z-score was applied to quantify the significance of the concordance between the direction of change in genes (up-/down-regulated) in a gene dataset and the established direction of change (derived from literature) of genes associated with a transcriptional regulator. A z-score ≥2 or ≤−2 was considered significantly activated or inhibited, respectively. p value of overlap reflected that the overlap between the experimental dataset and a given transcriptional regulator was due to a random chance. An overlap p-value ≤0.05 was considered significant.

2.6. Immunohistochemistry

Immunohistochemical staining was performed on paraffin-embedded liver sections 5 μm thick. Antigen retrievals were performed in boiling citrate buffer solution for 30 min. The liver sections were blocked with 5% normal goat serum for 30 min at room temperature (RT). Primary antibodies: Reg3b dilution 1:100 (MAB1996, R & D Systems, Minneapolis, MN, USA), CDX2 dilution 1:300 (ab76541, abcam, Waltham, MA, USA), AOC1 dilution 1:250 (NBP1-58006, Novus Biologicals, Centennial, CO), and TM4SF20 dilution 1:300 (ARP49875_P050, Aviva Systems Biology, San Diego, CA, USA) were incubated at 4 °C overnight. The biotinylated secondary antibody was incubated for 30 min at RT, followed by avidin–biotin–peroxidase complex ABC Vectastain kit Peroxidase HRP (PK-4000, Vector Laboratories, Newark, CA, USA). Using the chromogen diaminobenzidine ImmPACT DAB Substrate kit (SK-4105, Vector Laboratories, Newark, CA, USA) as a substrate for horseradish peroxidase. Slides were counterstained with hematoxylin (GHS316, Sigma Aldrich, Lenexa, KS, USA). Photomicrographs were captured with an Olympus DP74 color camera mounted on an Olympus BX51 microscope with CellSens (Version 2.3) software.

2.7. Statistical Analysis of Non-RNAseq Data

All non-RNA-seq data were expressed as the mean ± standard error of the mean. Student’s t-test or one-way ANOVA with Tukey’s post hoc were used for analyses with a p-value <0.05.

3. Results

3.1. Progressive Pericystic Fibrosis in PCK Rats

PCK rats are a variant of the Sprague Dawley (SD) rat strain, which harbors mutations in the Pkhd1 gene, the gene orthologous to that which causes ARPKD in humans. Previous studies extensively characterized this model and demonstrated that the PCK rat mimics the pathophysiological characteristics of human CHF/ARPKD. These rats develop extensive hepatic cysts, which are present at birth and progressively increase in number and size with age [11]. This results in extensive hepatomegaly in PCK rats as compared to SD rats demonstrated by significantly higher liver weight to body weight ratio, which is evident by PND10 and continues to increase until PND90 (Figure 1A). Additionally, extensive pericystic fibrosis is evident in PCK rats, as demonstrated using Masson’s trichrome staining (Figure 1B). The kidney and liver disease in the PCK rats becomes progressively worse, necessitating euthanasia around 6 months of age. These characteristics make PCK rats an ideal model to investigate mechanisms of hepatic cystogenesis and fibrosis in CHF/ARPKD.

3.2. RNAseq Analysis Correlated with Progressive Worsening of CHF/ARPKD

We performed global whole liver gene expression analysis using three individual SD and PCK rats at three time points (PND15, 20, 30 and 90, total n = 24). Differential gene expression analysis showed a progressive increase in the number of genes significantly differentially expressed between SD and PCK rats with highest difference in PND90 (Table 1). Principle component analysis (PCA), distance matrix, and correlation plots revealed that the biological replicates at each time point clustered together (Figure 2A and Supplementary Figure S1). PCA further showed a time dependent change in hepatic gene expression between control SD livers and PCK livers. SD-PND15 and SD-PND20 samples clustered together with PCK-PND15 and PCK-PND20 samples, suggesting relatively less overall difference in hepatic gene expression during these early time points. A significant shift in clustering was observed at PND30, which continued further at PND90. PCA showed that SD-PND30 samples clustered separately from SD-PND90, an expected result due to hepatic maturation between 1 and 3 months of age. Interestingly, the PCK-PND30 and PCK-PND90 samples clustered separately from each other and from SD rat samples at the same time points, indicating both age-related changes and changes due to disease progression.

3.3. Gene Ontology (GO) Analysis Reveals Major Categories of DEGs

We performed a differential gene expression analysis with adjusted p < 0.01, which revealed that 1298 genes were differentially expressed between SD and PCK rats when all time points were considered together. We used this strategy to capture major changes due to both disease progression (time) and genotype. Gene ontology analysis revealed a significant activation of genes involved in wound healing, cell migration, and cell adhesion and a significant downregulation of genes involved in ribosomal biogenesis and antigen presentation in PCK rats as compared to SD rats (Figure 2B).
The genes associated with wound healing could be further clustered in two subclusters, which changed expression pattern over the time course (Figure 3A). The first cluster contained (outlined by a red box) genes such as Plat, Wnt7a, Tpm1, and Pdgfb and showed higher expression in PCK rats at PND15 and PND20 with a moderate reduction at PND30 and PND90 but were still higher than SD rats at the respective time points. The second cluster (outlined by a blue box) genes such as Crp, P2ry2, Tgfbr2, and Mmp12 showed an overall lower expression as compared to the first cluster in PCK rats. However, expression of these genes was moderately higher in PCK rats when compared to SD rats at all time points except PND30.
The genes involved in cell migration also segregated in two subclusters and illustrated complete segregation of genes between genotypes, suggesting disease-specific regulation (Figure 3B). The first subcluster (outlined by a red box) containing genes such as Lamc2, Tgfb2, Cxcl16, and Acp5 was significantly higher in PCK rats at all time points, whereas the second gene cluster (outlined by a blue box), which included Diaph1, Prkca, Rufy3, and Bmp2, showed an overall lower expression in PCK rats at most of the time points as compared to SD rats.
A significant number of DEGs were categorized as involved in cell adhesion by GO analysis, which formed three approximate subclusters (Figure 4). The first cluster (outlined by a red box), containing genes such as Spp1, Cd44, Pkp1, and Mybpc3, showed significantly higher expression in PCK rats at all time points as compared to SD rats. A second cluster (outlined by a blue box), containing genes such as F11r, Cd47, Itgae, and Cadm1, showed a temporal change between the groups. Most of the genes in this group showed significantly higher expression in PCK rats at PND15 and 20 as compared to SD rats. A moderate increase in expression of these genes was observed at PND30 and 90 in SD rats, but it was still significantly lower than the expression in PCK rats at PND30 and especially at PND90. A third cluster (outlined by a black box) containing genes such as Lrfn3, Cd99l2, Pnn, and Cd4 showed temporal variations during the PND15–20 and PND30–90 time points. In SD rats, these genes were higher at PND15 and 20 but showed decreased expression at PND30 and 90. In PCK rats, expression of these genes was higher at PND15 and 20 as compared to PND30 and 90 but was significantly lower than SD rats at the same time points.
Two categories of genes showed significant downregulation in PCK rats relative to SD rats including those involved in antigen presentation (Figure 5A; RT1-A2, RT1-CE10, etc.) and those involved in ribosomal biogenesis (Figure 5B; Pes1, Rsp28, etc.). The genes involved in antigen presentation showed a significantly lower in expression at PND15 and 20 in PCK rats than the SD rats and a moderate increase in expression at PND30 and 90, as compared to PCK-PND 15/20, but were still lower in expression than the SD-PND30/90 groups (Figure 5A). The genes involved in ribosomal biogenesis showed extensive variations in their expression patterns between the time points and genotypes but were overall lower in PCK rats as compared to SD rats (Figure 5B).

3.4. Identification of Upstream Regulators and Canonical Pathways in PCK Rats

To further investigate the global gene expression data and identify key players, we performed an upstream regulator analysis using Ingenuity Pathway Analysis (IPA) software. The upstream regulators were predicted based on the IPA database. The activated upstream regulators (Table 2) showed some consistently upregulated molecules such as MTPN (myotrophin), which was predicted to be activated at all four time points in PCK rats. SMARCA4, which codes for the protein BRG1 was activated at PND20 and 30. Interestingly, various members of the STAT family of transcription factors were activated at all four time points. (PND15- STAT5B, PND20- STAT4, PND30- STAT3 and STAT1, and PND90- STAT3 and STAT4).
The upstream regulators predicted to be inhibited in PCK rats (Table 2) included CBX5, also called HP1α, a gene silencer that primarily works via H3K9 methylation and was predicted to be inhibited at PND15, 20, and 30. HNF4α, the master regulator of hepatocyte differentiation, was also predicted to be downregulated at PND30 and PND 90. Finally, inhibition of SMAD7, an inhibitory SMAD that attenuates TGFβ and activin mediated signaling, was inhibited at PND30 and 90 in PCK rats. We hypothesized that inhibition of HNF4α may promote a more dedifferentiated phenotype in the PCK rat livers and may show alteration in hepatic stem cell niche. We performed immunofluorescence staining of OV6, a marker of hepatic progenitor (oval) cells commonly used in rat studies, which revealed that almost all cyst wall epithelial cells expressed this HPC marker (Figure 6). In SD livers, OV6 staining was restricted to biliary ducts as expected.

3.5. Immunohistochemical Confirmation of Top Upregulated Genes

We performed a differential gene expression analysis to identify genes that were up or down regulated at all time points in PCK rats as compared to SD rats. The top five up and down regulated genes are listed in Table 3. We chose the top four genes including Reg3b, Aoc1, Tm4sf20, and Cdx2 that were upregulated in all time points for further analysis. Expression data extracted from RNAseq analysis (log normalized counts, n = 3 per group) shown in Figure 7 indicates that all genes were expressed significantly higher in PCK rat livers as compared to SD rat livers. Next, we performed immunohistochemical analysis to confirm protein expression changes in these top four upregulated genes at PND30 (Figure 8) and PND90 (Figure 9) using paraffin-embedded liver sections from SD and PCK rats. AOC1, CDX2, and Reg3b showed higher expressions both in cyst wall epithelium and hepatocytes in PCK rats at both PND30 and PND90. TM2SF40 showed mainly nuclear staining pattern and was expressed at equal levels in SD and PCK hepatocytes. However, PCK rats also showed significant TM4SF20 expression in cyst wall epithelium. Furthermore, at both PND30 and PND90, PCK rat hepatocytes also showed cytoplasmic expression of TM4SF20 in addition to nuclear expression, which was absent in SD rats. Interestingly, there was very low to no staining of any of these proteins in the extracellular matrix-rich pericystic region, suggesting a preferential localization of proteins to the hepatic epithelial cell compartment.

4. Discussion

CHF/ARPKD is a progressive genetic disease affecting mainly the kidneys and the liver. Although the phenotypes of CHF/ARPKD are well-characterized, the mechanisms driving these phenotypes in CHF/ARPKD are still incompletely understood. Unlike previously thought, ARPKD patients have significant liver issues. ARPKD manifests itself in perinatal period with significant pericystic fibrosis, portal hypertension, pulmonary insufficiency, and loss of liver function. These patients often need dual kidney and liver transplantation to survive. Previous studies indicate that a pathogenic triumvirate of inflammation, proliferation, and fibrosis is at the center of progressive cyst development [13]. To reveal possible mechanisms driving this profound disease phenotype, we performed RNA-Seq analysis in SD and PCK rat liver at various time points to compare the gene expression alterations associated with Pkhd1 mutation. Our analysis focused on determining differential gene expression in developing (PND15, 20, 30) and mature (PND90) rats in both genotypes. This strategy was effective because it captured gene expression changes that occurred as a function of hepatic maturation with time and those that occurred due to disease development and progression. A total of 1298 genes were significantly different between PCK livers and SD control livers with approximately 50% downregulated and the other 50% upregulated. The principal component analysis indicated an age-related change in gene expression. This was interesting because it suggested that the normal hepatic maturation and differentiation process was disrupted in CHF/ARPKD. Rodent livers underwent a massive transcriptomic transformation during the first 6 weeks (42 days) of life, during which the liver repressed the embryonic gene expression program and induced the adult gene expression program [20]. This differentiation process was combined with bursts of cell proliferation that aided in increasing the liver size to achieve the adult liver to body weight ratio. By 6 weeks of age, rodent livers looked like adult livers both in terms of size and transcriptome [21,22]. Our analysis indicated that this normal process of size increase and hepatic maturation was severely dysregulated in PCK rats, and a growing number of additional transcriptomic changes occurred, leading to severe and rapid disease progression. PCA showed that there was a relatively small difference in gene expression between SD and PCK rats up to PND20. By PND30, the two genotypes significantly separated, indicating that the ‘window of pathogenesis’ laid between PND15 and PND30. Extrapolating these findings to human conditions will be both challenging and rewarding, as the process of hepatic maturation is significantly more delayed, requiring up to 2 years in humans [23]. However, important points of therapeutic intervention may lie in this critical pathogenic window.
The gene ontology analysis of these data revealed a clear picture of disease progression involving disruption in cell migration and cell adhesion processes that ultimately produced a prolonged wound healing response. This has been observed in other fibrotic disorders in the liver and other tissues [24,25,26]. Loss of functional PKHD1 in PCK rat livers triggers an injury response that is not typical in the sense of structural damage or functional damage but seems to be associated with disruption of normal bile duct development. It is known that bile duct development continues during the early postnatal period where the biliary tree is expanding along with increasing liver size. It is plausible that loss of PKHD1 function in cholangiocytes along with other mechanisms induces cystogenesis, which is treated by the liver as a massive structural damage, perhaps due to micromechanical changes induced by expanding cysts, promoting a wound healing response and fibrosis. The gene expression profiles identified by our study support this hypothesis, as they show increased expression of genes involved in cell migration and cell adhesion, both possible components of cyst formation and budding off from the biliary tree. The wound healing response, which seems to drive the fibrosis, is likely a result of increased micromechanical stiffness induced by these growing number of cysts. While the exact signaling mechanisms remain to be investigated, our data provide several novel leads that were previously unknown.
The data on upstream regulators provides further evidence that the pathogenic triumvirate of proliferation, inflammation, and fibrosis is at the center of the CHF/ARPKD disease processes [13]. One of the most highly and consistently predicted to be upregulated transcription factors as members of the signal transducers and activators of transcription (STAT) family, including STAT1, STAT3, STAT4, and STAT5B, which are involved in growth and inflammatory processes. Previous studies have shown strong activation of STAT3, regulated by polycystin-1 (PC-1), the gene product of PKD1 (mutated in ADPKD), in cyst-lining epithelial cells in mouse and human ADPKD [27]. Furthermore, treatment with STAT3 inhibitors decreased cell proliferation in human ADPKD cells, blocked renal cyst formation in PKD mouse models, and reduced cyst formation and growth in a neonatal PKD mouse model [28]. Although not identified in our analysis, STAT6, another member of STAT family, exhibits increased activity in two murine ADPKD models, mediated by interleukins IL4 and IL13 [29]. Deletion of STAT6 in bpk/bpk mice, a model for ADPKD, resulted in less severe kidney disease and an improvement of kidney function. Pharmacological inhibition of STAT6 led to suppression of renal cystic growth, lower kidney weight, and cystic index [29]. Given our observations and those previously reported for STAT6, we hypothesize that other STAT family members could also be involved in CHF/ARPDK pathogenesis.
IPA analysis predicted inhibition of SMAD7, a negative regulator of transforming growth factor β (TGF-β) signaling, in PCK rats at PND30 and PND90. Inhibition of SMAD7 suggests an increased activation of the TGF-β signaling pathway, which may further contribute to myofibroblast activation and fibrosis. Indeed, the anti-fibrotic role of SMAD7 was illustrated in multiple liver fibrosis models, including the CCl4-induced liver fibrosis [30] and bile duct ligation models (ref), as well as in isolated primary hepatic stellate cells, [31] and also in the unilateral ureteral obstructed (UUO)-induced renal fibrosis model [32]. All these studies suggest that inhibiting TGF-β signaling through activating Smad7 can be a potential therapeutic tool in ARPKD.
In addition to the upstream regulators that were already established in ADPKD, such as the STATs and SMAD-TGFβ signaling, we also found some upstream regulators unique in ARPKD. MTPN (myotrophin) is a novel target predicted by IPA in ARPKD. Upstream analysis revealed that MTPN was activated from PND15 to PND90, suggesting its role is critical in ARPKD pathogenesis. Interestingly, MTPN stimulates insulin secretion, and its function can be suppressed by microRNA-375 (miR-375) [33], which is critical in maintaining normal glucose homeostasis, regulating insulin secretion, and pancreatic α- and β-cell turnover in response to increasing insulin demand in insulin resistance [33,34]. Interestingly, enhanced glycolysis, defective glucose metabolism, insulin resistance, and hyperinsulinemia have been reported in ADPKD patients [35,36]. Thus, our studies have identified MTPN as a novel therapeutic target in CHF/ARPKD, which may correct the metabolic disorder inherent in this disease.
Finally, our studies revealed the inhibition of HNF4α, the master regulator of hepatic differentiation in CHF/ARPKD. HNF4α plays a critical role in liver development and hepatocyte differentiation. In fact, HNF4α is absolutely essential in differentiation of hepatocytes from hepatoblasts, and its downregulation results in increased cell proliferation and cancer in the liver [37,38]. A detailed analysis of HNF4α target genes showed decline in several cytochrome P450 family members (Cyp3a5, Cyp2c9, and Cyp2b6), transporters (Abcc2, Abcg5, Abcg8, Aqp8, and Slco1a1), and lipid metabolism (Acox2, Acsl1, Lipa, Pla2g4a, and Srebf1) genes. These data, for the first time, reveal that CHF/ARPDK is not only associated with increased cystogenesis, fibrosis, and inflammation but also with significant dysregulation in hepatocyte differentiation and subsequent metabolic function, which may further increase disease-associated morbidity. Further studies are needed to investigate the role of HNF4α in CHF/ARPKD and how it regulates CHF/ARPKD pathogenesis.
Certain limitations of this study should be noted. First, we used whole liver for bulk RNAseq analysis. The observed gene expression changes, while novel and useful, includes gene expression in all liver cell types (i.e., hepatocytes, cholangiocytes, liver sinusoidal endothelial cells, myofibroblasts, and any resident or infiltrating hepatic leukocytes). Future studies using single cell RNAseq are needed to determine cell-type-specific gene expression changes in SD and PCK rats and in ARPKD patients compared to controls. Furthermore, a detailed proteomic analysis would be extremely useful. While we confirmed upregulation of the top four genes increased throughout the time course, this candidate gene approach has its limitations that can be overcome by bulk or single-cell proteomics.
In summary, this is the first comprehensive global hepatic gene expression analysis in a rat model of CHF/ARPKD over a prolonged pathogenic period. Our analysis provides strong evidence to support the ‘pathogenic triumvirate’ of proliferation–inflammation–fibrosis as underlying mechanisms driving cystogenesis and pericystic fibrosis in CHF/ARPKD. Furthermore, our studies have revealed a critical therapeutic window between PND20 and PND30 prior to rapid disease progression that represents a previously unrecognized therapeutic opportunity. Beyond ARPKD, it is possible that these data may apply to other hepatic cystic diseases. Future studies should determine if a similar therapeutic window exists in human CHF/ARPKD children. Moreover, we identified several novel therapeutic targets that could prevent disease progression within this therapeutic window and attenuate or halt progression of this devastating disease. Thus, these studies provide a solid foundation for launching further mechanistic investigations and drug development efforts to pharmacologically manage CHF/ARPKD in humans.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/livers3030025/s1, Figure S1: Data quality analysis of RNAseq data; Table S1: Differentially Expressed Genes (DEGs) in PCK rats at various time points.

Author Contributions

Conceptualization, U.A. and M.T.P.; methodology, L.J. and D.P.-C.; validation, S.K..; formal analysis, S.K.; investigation, L.J. and D.P.-C.; resources, U.A. and M.T.P.; data curation, S.K; writing—original draft preparation, U.A. and M.T.P.; writing—review and editing, U.A., M.T.P., S.K., L.J. and D.P.-C.; visualization, S.K., L.J., D.P.-C.; supervision, U.A. and M.T.P.; project administration, U.A. and M.T.P.; funding acquisition, U.A. and M.T.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by NIH: P20 RR021940, P20 GM103549, R01 DK98414, P30 DK106912, The Kansas Intellectual and Developmental Disabilities Research Center U54 HD090216, and DST SERB SRG grant (SRG/2020/001414).

Institutional Review Board Statement

The animal study protocol was approved by the Institutional Review Board (Institutional Animal Care and Use Committee) of the University of Kansas Medical Center (protocol code 2016-2330 and 2017-2417).

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated for this study can be found in NCBI SRA, PRJNA799864.

Acknowledgments

The authors would like to thank Clark Bloomer and Roseann Skinner of the KUMC Genomics Core for the RNA sequencing.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Histopathological features of CHF/ARPKD. (A) Liver weight expressed as a percent of body weight in SD and PCK rats from PND0 through PND90; (B) Representative photomicrographs of Masson’s trichrome stained liver sections from SD rats (left) and PCK rats (right) at PND90. Blue staining indicates collagen. CV, central vein; CST, hepatic cyst; PCF, pericystic fibrosis; PT, portal triad area. (* p-value < 0.05).
Figure 1. Histopathological features of CHF/ARPKD. (A) Liver weight expressed as a percent of body weight in SD and PCK rats from PND0 through PND90; (B) Representative photomicrographs of Masson’s trichrome stained liver sections from SD rats (left) and PCK rats (right) at PND90. Blue staining indicates collagen. CV, central vein; CST, hepatic cyst; PCF, pericystic fibrosis; PT, portal triad area. (* p-value < 0.05).
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Figure 2. Characteristics of whole liver RNAseq data. (A) Principle component analysis revealed clustering of biological replicates within a group, suggesting good reproducibility. The samples are separated by age on the first principal component and genotype on the second principal component. This indicates more age related changes relative to gentoypte related changes in gene expression. Also, there are fewer differentially expressed genes in younger age liver samples (e.g., PND15) than older age samples (e.g., PND90). (B) Gene ontology analysis showing classification of overall differential gene expression in SD and PCK rats.
Figure 2. Characteristics of whole liver RNAseq data. (A) Principle component analysis revealed clustering of biological replicates within a group, suggesting good reproducibility. The samples are separated by age on the first principal component and genotype on the second principal component. This indicates more age related changes relative to gentoypte related changes in gene expression. Also, there are fewer differentially expressed genes in younger age liver samples (e.g., PND15) than older age samples (e.g., PND90). (B) Gene ontology analysis showing classification of overall differential gene expression in SD and PCK rats.
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Figure 3. Genes involved in wound healing and cell migration. (A) Heatmap showing significant changes in genes involved in wound healing response in PCK rats over time. Many of these genes were associated with change in extracellular matrix, including Tgfb2, Igta1, Pdgfb, Cdh3, Timp1, and Mmp12. (B) Heatmap of differentially expressed genes associated with cell migration including CxCl16, Tgfb2, Lamc2, and Cyr61 in PCK rats. Postnatal age (PND15, 20, 30, 90) is indicated at the bottom of the heatmap, where SD rat data are labeled in green text, and PCK data are labeled in purple text. Red squares indicate upregulated genes, blue squares indicate down regulated genes; color intensity indicates magnitude of change. The red and blue outlines in each heatmap indicate gene expression subclusters based on relative expression between PCK and SD rats over time. See text for details.
Figure 3. Genes involved in wound healing and cell migration. (A) Heatmap showing significant changes in genes involved in wound healing response in PCK rats over time. Many of these genes were associated with change in extracellular matrix, including Tgfb2, Igta1, Pdgfb, Cdh3, Timp1, and Mmp12. (B) Heatmap of differentially expressed genes associated with cell migration including CxCl16, Tgfb2, Lamc2, and Cyr61 in PCK rats. Postnatal age (PND15, 20, 30, 90) is indicated at the bottom of the heatmap, where SD rat data are labeled in green text, and PCK data are labeled in purple text. Red squares indicate upregulated genes, blue squares indicate down regulated genes; color intensity indicates magnitude of change. The red and blue outlines in each heatmap indicate gene expression subclusters based on relative expression between PCK and SD rats over time. See text for details.
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Figure 4. Dysregulated cell adhesion gene expression in PCK rats. Heatmap showing significant induction of several cell adhesion genes, including Cd44, Spp1, Itga3, Col12a1, and Icam in PCK rats (subcluster with red outline). Subclusters outlined in blue and black contain genes with temporal changes over time or that are mostly down regulated in PCK rats, respectively. Postnatal age (PND15, 20, 30, 90) is indicated at the bottom of the heatmap, where SD rat data are labeled in green text, and PCK rat data are labeled in purple text. Red squares indicate upregulated genes, blue squares indicate down regulated genes; color intensity indicates magnitude of change. The red, blue, and black outlines in the heatmap indicate gene expression subclusters based on relative expression between PCK and SD rats over time. See text for details.
Figure 4. Dysregulated cell adhesion gene expression in PCK rats. Heatmap showing significant induction of several cell adhesion genes, including Cd44, Spp1, Itga3, Col12a1, and Icam in PCK rats (subcluster with red outline). Subclusters outlined in blue and black contain genes with temporal changes over time or that are mostly down regulated in PCK rats, respectively. Postnatal age (PND15, 20, 30, 90) is indicated at the bottom of the heatmap, where SD rat data are labeled in green text, and PCK rat data are labeled in purple text. Red squares indicate upregulated genes, blue squares indicate down regulated genes; color intensity indicates magnitude of change. The red, blue, and black outlines in the heatmap indicate gene expression subclusters based on relative expression between PCK and SD rats over time. See text for details.
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Figure 5. Downreglated genes in PCK rats. Heatmap showing significant decrease in expression of genes associated with (A) antigen presentation and (B) ribosomal biogenesis in PCK rats. Postnatal age (PND15, 20, 30, 90) is indicated at the bottom of the heatmap, where SD rat data are labeled in green text, and PCK data are labeled in purple text. Red squares indicate upregulated genes, blue squares indicate down regulated genes; color intensity indicates magnitude of change.
Figure 5. Downreglated genes in PCK rats. Heatmap showing significant decrease in expression of genes associated with (A) antigen presentation and (B) ribosomal biogenesis in PCK rats. Postnatal age (PND15, 20, 30, 90) is indicated at the bottom of the heatmap, where SD rat data are labeled in green text, and PCK data are labeled in purple text. Red squares indicate upregulated genes, blue squares indicate down regulated genes; color intensity indicates magnitude of change.
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Figure 6. Cyst wall epithelial cells express hepatic progenitor cell markers. Photomicrographs (200×) of immunofluorescence analysis of OV6 (green), a HPC marker, on fresh frozen liver sections of SD and PCK rats at PND30 and PND90. DAPI (blue) was used as the nuclear counterstain. PT, portal triad; CST, hepatic cyst.
Figure 6. Cyst wall epithelial cells express hepatic progenitor cell markers. Photomicrographs (200×) of immunofluorescence analysis of OV6 (green), a HPC marker, on fresh frozen liver sections of SD and PCK rats at PND30 and PND90. DAPI (blue) was used as the nuclear counterstain. PT, portal triad; CST, hepatic cyst.
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Figure 7. Expression levels of Aoc1, Cdx2, Reg3b, and Tm4sf20 genes plotted as log normalized counts in SD and PCK rats at (A) PND30 and (B) PND90 stages. (*** p < 0.001, N = 3).
Figure 7. Expression levels of Aoc1, Cdx2, Reg3b, and Tm4sf20 genes plotted as log normalized counts in SD and PCK rats at (A) PND30 and (B) PND90 stages. (*** p < 0.001, N = 3).
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Figure 8. Immunohistochemical confirmation of top upregulated genes at PND 30. Representative photomicrographs (400×) of IHC for AOC1, CDX2, Reg3b, and TM4SF20 was performed on formalin-fixed, paraffin-embedded liver sections from PND30 SD and PCK rats. PT, portal triad; CV, central vein; CST, hepatic cyst.
Figure 8. Immunohistochemical confirmation of top upregulated genes at PND 30. Representative photomicrographs (400×) of IHC for AOC1, CDX2, Reg3b, and TM4SF20 was performed on formalin-fixed, paraffin-embedded liver sections from PND30 SD and PCK rats. PT, portal triad; CV, central vein; CST, hepatic cyst.
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Figure 9. Immunohistochemical confirmation of top upregulated genes at PND90. Representative photomicrographs (400×) of IHC for AOC1, CDX2, Reg3b, and TM4SF20 was performed on formalin-fixed, paraffin-embedded liver sections from PND90 SD and PCK rats. PT, portal triad; CV, central vein; CST, hepatic cyst.
Figure 9. Immunohistochemical confirmation of top upregulated genes at PND90. Representative photomicrographs (400×) of IHC for AOC1, CDX2, Reg3b, and TM4SF20 was performed on formalin-fixed, paraffin-embedded liver sections from PND90 SD and PCK rats. PT, portal triad; CV, central vein; CST, hepatic cyst.
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Table
Table 1. Differential gene expression * in PCK rats as compared to SD rats.
Table 1. Differential gene expression * in PCK rats as compared to SD rats.
PNDUpregulatedDownregulated
15620667
2018602174
3027322767
9032083279
* Based on adj. p-value cut-off of 0.01. No fold change cut-off was applied.
Table
Table 2. Changes in Upstream regulators in PCK rats.
Table 2. Changes in Upstream regulators in PCK rats.
Activated Upstream Regulators
15203090
NameZ-ScoreNameZ-ScoreNameZ-ScoreNameZ-Score
CTNNB12.782RB13.359MTPN3.44STAT34.599
MTPN2.433STAT43.174IRF73.01FOXO14.186
TCF7L22.167SMARCA43.022SMARCA42.847STAT44.016
STAT5B2.000MTPN2.728STAT32.641MTPN3.501
ARNT2.513STAT12.629
Inhibited Upstream Regulators
15203090
NameZ-ScoreNameZ-ScoreNameZ-ScoreNameZ-Score
CBX5−2.236KDMSA−2.530HNF4A−3.029HNF4A−2.821
CBX5−2.309ATF4−2.94SMAD7−2.668
CBX5−2.84ZFP36−2.559
NFE2L2−2.775SNAI1−2.509
SMAD7−2.691HDAC5−2.236
Table
Table 3. Top five up and down regulated genes in PCK rats at all time points.
Table 3. Top five up and down regulated genes in PCK rats at all time points.
Gene SymbolFold ChangeGene SymbolFold Change
Reg3b175.49Abhd3−101.76
Aoc172.31Pla2g4c−72.09
Tm4sf2025.48Ly6al−22.77
Cdx210.30Mpped1−19.74
Krt1710.30Stfa2l1−5.21
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Khare, S.; Jiang, L.; Paine-Cabrera, D.; Apte, U.; Pritchard, M.T. Transcriptomics of Congenital Hepatic Fibrosis in Autosomal Recessive Polycystic Kidney Disease Using PCK Rats. Livers 2023, 3, 331-346. https://doi.org/10.3390/livers3030025

AMA Style

Khare S, Jiang L, Paine-Cabrera D, Apte U, Pritchard MT. Transcriptomics of Congenital Hepatic Fibrosis in Autosomal Recessive Polycystic Kidney Disease Using PCK Rats. Livers. 2023; 3(3):331-346. https://doi.org/10.3390/livers3030025

Chicago/Turabian Style

Khare, Satyajeet, Lu Jiang, Diego Paine-Cabrera, Udayan Apte, and Michele T. Pritchard. 2023. "Transcriptomics of Congenital Hepatic Fibrosis in Autosomal Recessive Polycystic Kidney Disease Using PCK Rats" Livers 3, no. 3: 331-346. https://doi.org/10.3390/livers3030025

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