Methylseq, Single-nuclei RNAseq, and Discovery Proteomics Identifies Pathways Associated with Nephron Deficit CKD in the HSRA Rat Model

Abstract

Low nephron numbers are associated with increased risk of developing chronic kidney disease (CKD) and hypertension, both significant global health problems. To investigate the impact of nephron deficiency, our lab developed a novel inbred rat model (HSRA rat). In this model, approximately 75% of offspring are born with a single kidney (HSRA-S), compared to two-kidney littermates (HSRA-C). HSRA-S rats show impaired kidney development, resulting in ~20% fewer nephrons. Our previous data and current findings demonstrate nephron deficit (failure of one kidney to form and altered development in the remaining kidney) predispose HSRA-S to CKD late in life (increased proteinuria by 18 months of age in HSRA-S=51± 3.4 versus HSRA-C = 8± 1.5 mg/24hours). To understand early molecular mechanisms contributing to the increased predisposition to CKD, Methylseq using reduced representation bisulfite sequencing, single nuclei (sn)RNAseq, and discovery proteomics were performed in kidneys of 4-week-old HSRA rats. Methylation analysis revealed a small number of differences, including 5 differentially methylated cytosines and 6 differentially methylated regions between groups. The snRNAseq analysis identified differentially expressed genes in most kidney cell types, with several hundred genes dysregulated depending on the analysis method (Seurat vs DESeq2). Notably, many genes are involved in kidney development. Discovery proteomic analysis identified 366 differentially expressed proteins. A key finding was dysregulation of Deptor/DEPTOR and Amdhd2/AMDHD2 across omics layers, suggesting a potential role in compensatory mechanisms or the genetic basis of altered kidney development. Further understanding of these mechanisms may guide interventions to preserve nephron health and slow kidney disease progression.

Graphical Abstract

INTRODUCTION

Understanding the link between nephron numbers and predisposition to hypertension and chronic kidney disease (CKD) is a key challenge in nephrology today. Pioneering work by Brenner et al. in 1988 introduced the hypothesis that a lower nephron count inversely correlates with the risk of developing hypertension (1). This hypothesis suggests that individuals, even those who are healthy, undergo a decline in nephron number as part of the natural aging process. Consequently, those with fewer nephrons, whether due to genetic predisposition, intrauterine insults, or accelerated nephron loss, are at an elevated risk of developing hypertension and kidney injury (2). This condition, over time, can lead to CKD (and other cardiovascular diseases), a significant health concern given its association with morbidity and mortality worldwide. Left untreated, CKD can progress to end-stage kidney disease (ESKD), necessitating dialysis or kidney transplantation. In 2019, treatment for Medicare beneficiaries with CKD cost taxpayers $87.2 billion with an additional $37.3 billion spent treating ESKD (3). Therefore, understanding molecular and genetic determinants of nephron number may lead to new interventions or treatments to lessen the health and financial burden of CKD.

Nephron number and its implications in humans and rodents have been studied extensively. These studies have shown that nephron numbers can be affected by a variety of factors (genetic, maternal nutrition, preterm birth, etc.), with low nephron numbers associated with various health outcomes, including hypertension, CKD, and cardiovascular issues (4–9) Also, birth weight has been found to correlate linearly with nephron number in humans, with lower birth weight associated with reduced nephron numbers (10). Animal studies have demonstrated that both prenatal and postnatal environments can affect nephron number, renal function, and the development of hypertension in adult life (11–13). Studies have also emphasized the role of genetic contributions to renal hypoplasia and the mechanisms controlling nephron endowment in humans and rodents (14). Lastly, the effect of environmental and epigenetic factors on nephron number has become a focus of research in recent years (15, 16). It has even been suggested that methylation differences at specific CpG sites can predict longitudinal eGFR changes, suggesting a potential role for epigenetic modifications in kidney disease development and progression (17).

The number of nephrons present at birth can have serious consequences for long-term kidney health and increase the risk of developing hypertension, albuminuria, and CKD in adulthood (18, 19). Human nephron numbers have been reported to vary significantly among individuals, ranging from 210,000 to 2.7 million (9). In total, these findings emphasize the importance of understanding nephron development and its implications for long-term health outcomes.

The HSRA model was selected from a single pair of outbred heterogenous stock (HS) rats and subsequently inbred (>20 generations). The HS population that this model was selected from is genetically heterogeneous and derived from 8 inbred strains (ACI, BN, BUF, F344, M520, MR, WKY, and WN). Therefore, the genetic background of the HSRA rat is a mix of these 8 progenitor strains (20). In the HSRA rat, ~75% of offspring are born with a single kidney (HSRA-S) compared to normal two-kidney littermates (HSRA-C). HSRA-S rats also exhibit altered development in their solitary kidney which leads to ~20% fewer nephrons when compared to an individual kidney from HSRA-C. Previous studies have characterized the HSRA rat for its susceptibility to develop renal injury and dysfunction (21). HSRA-S rats consistently develop renal injury with age, including proteinuria and histological injury, that is greater than HSRA-C, as early as five months of age. By 20 months of age, HSRA-S rats demonstrate significantly greater kidney injury compared to uninephrectomized animals (HSRA-UNX). HSRA-S animals are also highly susceptible to CKD from a secondary hit of hypertension using an experimental model of deoxycorticosterone acetate (DOCA) and salt loading, with HSRA-S demonstrating increased proteinuria within two weeks after the induction of hypertension, compared to both HSRA-C and HSRA-UNX (22). HSRA rats are genetically identical (as they are inbred), thus the differential response to hypertension and kidney injury across the three groups (HSRA-S, -C, and -UNX) is likely attributed to differences in nephron endowment. Additionally, HSRA-S animals show sex-specific susceptibility to increased blood pressure and kidney injury, with males being more prone to these effects (21).

The current study aimed to investigate early molecular differences in kidneys from HSRA-S animals that are associated with the long-term susceptibility to develop increased kidney injury and increased blood pressure. A multi-omics analysis of the kidney isolated from HSRA-S and HSRA-C using Methylseq (reduced representation bisulfite sequencing), single nuclei RNAseq, and discovery proteomics was performed to better understand these early changes in the kidney.

MATERIAL AND METHODS

Animals

All experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Mississippi Medical Center (UMMC). HSRA rats are bred and maintained at UMMC via a brother-sister mating scheme. HSRA rats born with a single kidney are denoted as “HSRA-S,” while rats born with two kidneys are denoted as “HSRA-C.” HSRA animals were studied at four weeks of age (month 1), and the kidneys were collected upon euthanasia and weighed, at which point animals were recorded as -S (n= 9) or -C (n= 12). Kidneys were snap-frozen and archived at −80 °C until proceeding with molecular analyses. For studies in aged animals, HSRA animals were weaned at four weeks of age (month 1), kidney status was determined at six weeks, animals were divided into groups of HSRA-S (n= 9) and HSRA-C (n=9), and subsequently, urine was collected and proteinuria determined over 18 months as done previously (21). At the end of the 18 months, terminal blood pressure was measured (under isoflurane) and kidneys were collected, weighed, and formalin-fixed for histology (Hematoxylin and eosin (H&E) staining).

Additionally, a separate cohort of HSRA animals was used to confirm some of the changes observed in the multiomics analysis. Kidneys were collected at 4 weeks of age from HSRA-S (n=4) and HSRA-C (n=4), snap frozen, and archived at −80 °C for subsequent quantitative real-time PCR and Western analysis.

Reduced Representation Bisulfite Sequencing.

DNA was isolated from 4-week-old male HSRA-C (n=6) and HSRA-S (n=6) kidney samples using the PureLink Genomic DNA Mini Kit (Invitrogen) and quantified with the Qubit High-Sensitivity DNA Assay Kit. A total of 100 ng of genomic DNA was used as input for each sample in duplicate and libraries were processed using the Ovation RRBS Methylseq System 1–16 per manufacturer’s instruction with a single adapter/index. Paired samples were subjected to either bisulfite conversion (RRBS) as well as the optional DNA oxidation procedure (oxRRBS). This allowed for the determination of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications of genomic DNA. RRBS measures 5mC and 5hmC but does not differentiate between the two, while oxRRBS only measures 5mC modifications.

Final libraries were validated by bioanalyzer (QIAxcel-DNA High Sensitivity) and concentrations were measured by fluorescence (Qubit HS DNA Assay Kit). Final libraries averaged ~300 base pairs in length and were normalized to 10nM for sequencing. Libraries were sequenced on Illumina NextSeq 2000 per Ovation RRBS Methyl-Seq specifications (100 PE reads) using a P2 200 cycle reagent kit.

After sequencing, TrimGalore was used for adaptor and quality trimming. A custom Python script was used to remove the sequence diversity added by the Ovation RRBS adaptor. Bismark was used for alignment to a reference genome (mRatBn7) and the methylation extractor function was used to process the BAM files and extract the methylation call for each cytosine analyzed (23). Using the methylKit R Package (v1.32.0), bismark.cov files, containing genome-wide methylation data, were read (methRead function) into the R environment (v4.40) (24). Following the initial data reading, a filtering step was implemented using filterByCoverage to refine the dataset based on coverage criteria, with a lower count threshold of 10 and an upper percentile threshold of 99.9. After filtering, the coverage data were normalized using the normalizeCoverage function with the “median” method (default). For differential methylation analysis, the focus was on identifying differentially methylated cytosines (DMCs) between groups (HRSA-S vs. HSRA-C). The calculateDiffMeth function was used, with overdispersion correction set to ‘MN’, as proposed by McCullagh and Nelder (25). For p-value corrections, the Benjamini-Hochberg (BH) method was used.

Differentially methylated cytosines (DMCs) were stringently identified based on a q-value threshold of ≤ 0.01 and a minimum methylation percentage difference of 10%. In addition to differential methylation analysis, the identified DMCs were annotated to assess their genomic context using the methylKit and genomation (v1.34.0) R packages (26). The annotateWithGeneParts function from the genomation package was then used to assign genomic features to each DMC, categorizing them into genomic regions such as promoters, introns, exons, intergenic areas, and CpG islands or shores. getAssociationWithTSS was used to map each DMC to the nearest transcription start sites (TSS).

For the differentially methylated region (DMR) analysis, Promoter regions were defined as regions +2000 base pairs upstream to −500 base pairs downstream from the TSS. The methylation data were aggregated over the defined promoter regions using the regionCounts function of methylKit. This aggregation was performed separately for the RRBS and oxRRBS datasets. Differential methylation analysis was then conducted on these aggregated data to identify regions with significant methylation changes. The differentially methylated regions (DMRs) were annotated to the nearest gene using the nearest function from the GenomicRanges R package (v1.58.0) (27). To convert RefSeq IDs to gene symbols, the Ensembl database was queried via the biomaRt R package (v2.62.0) (28). The FASTQ files were deposited to the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database, GSE289103.

Single Nuclei RNA sequencing and Bioinformatics.

Nuclei were isolated from archived kidney (same animals for Methylseq) using a central section/biopsy (cortex and medulla were included) collected at 4 weeks of age from HSRA-S (n=3) and HSRA-C (n=4) per 10X Chromium Nuclei Isolation Kit (PN-1000493). Single nuclei samples were processed through the 10X Genomics Chromium Single Cell 3’ protocol v3.1 per manufacturers’ instruction to capture 5000 cells per sample. Single-cell droplets (GEMs) were produced using the 10X Genomics Chromium Controller and barcoded for sample identification. Final libraries were validated on the QIAxcel DNA High Sensitivity bioanalyzer to visualize the expected insert size, and concentration. The pooled 10X library (n=7 samples) was subsequently sequenced twice on the Illumina NextSeq 2000 per 10X Genomics specifications (28×91 cycles) using a P3 100-cycle Kit. Our snRNAseq approach captured an average of 5,293 cells per sample, with an average of 55,945 reads per cell, detecting a median of 1,180 genes per cell.

The raw sequencing reads were processed using Cell Ranger v7.0 (10x Genomics). The cellranger mkfastq pipeline was used to demultiplex the FASTQ base call files. The outputs were then used to run cellranger count which aligns the sequencing reads to the reference transcriptome built from the Ensembl Rnor6.0 genome. On average, 85.02% of reads were mapped to the genome, with a confident mapping rate of 68.93%. The outputs from Cell Ranger for all samples were merged into the R (v4.3.0) environment using Seurat (v5) (29). A new data matrix was generated post-filtering of low-quality cells, high mitochondrial gene content (>7%), and removal of doublets/doublet-like cells using DoubletFinder v2.0 (30). For doublet prediction, the pN parameter was set to 0.25, consistent with the authors’ recommendation. pK was determined by using the ‘param_sweep_v3’ function and by selecting the peak of the BCV plot. nExp was estimated as 6% of the total cells, and adjusted according to the estimated proportion of homotypic doublets. The top 3000 highly variable genes were then identified using the FindVariableFeatures function. The data was normalized using the sctransform V2 method before performing Principal Component (PC) Analysis. By default, Seurat uses only the variable features identified in the previous step for input. The new streamlined integration function, IntegrateLayers, was used with the “Harmony” method, producing a Seurat object containing both the original count matrix and an “integrated” matrix. The integrated matrix was used to run FindNeighbors, which constructs a cellular KNN graph using the first 30 principal components (default). Cells were subsequently clustered iteratively by applying the Louvain algorithm through the FindClusters function, with the resolution parameter set to 0.5. The same PCs were then used to generate a two-dimensional visualization of the clustered cells using Uniform Manifold Approximation Projection (UMAP).

The marker genes for each cluster were identified through the FindConservedMarkers function to determine the genes uniquely expressed in each cellular cluster compared to all of the other clusters. This command is designed to identify genes that are conserved between the groups (-C and -S) for each cluster of cells. Clusters were annotated manually to assign cell types using multiple canonical markers for kidney cell types. After identifying cellular clusters, differential expression analysis between clusters/groups (HSRA-S vs. HSRA-C) was performed using the FindMarkers function in Seurat. A pseudobulking approach was also taken using the Seurat command AggregateExpression to further mitigate issues associated with multiple hypothesis testing (inflated p-values). Differential expression analysis was performed on these pseudobulked clusters using the command FindMarkers, specifying test.use = “DESeq2”. Genes with an adjusted p-value less than 0.05 were considered differentially expressed. The FASTQ files were deposited to the NCBI GEO database, GSE289104.

Gene Set Enrichment Analysis of Differentially Expressed Genes.

After the differential expression analysis, Gene Set Enrichment Analysis (GSEA) was performed using the cluster-specific DEG list comparing -S to -C (DESeq2 processed), the clusterProfiler (v4.10.0) R package, and the Gene Ontology (GO) resource (31–33). In our study, the entire list of differentially expressed genes (DEGs) was analyzed by GSEA without a predefined p-value cutoff. This approach contrasts with Over-Representation Analysis (ORA), which typically focuses on genes that surpass a significance threshold. For the GSEA, DEGs were ranked based on their fold change values. The GO resource provided functionally annotated gene sets, which is required for GSEA. The significance threshold for enriched pathways was set at an adjusted p-value of less than 0.05.

Discovery Proteomics via Mass Spectrometry.

Total protein from tissue samples (same as for Methylseq and snRNAseq) was reduced, alkylated, and purified by chloroform/methanol extraction before digestion with sequencing-grade modified porcine trypsin (Promega). Tryptic peptides were then separated by reverse phase XSelect CSH C18 2.5 um resin (Waters) on an in-line 150 × 0.075 mm column using an UltiMate 3000 RSLCnano system (Thermo). Peptides were eluted using a 60 min gradient from 98:2 to 65:35 buffer A:B ratio. Eluted peptides were ionized by electrospray (2.4kV) followed by mass spectrometric analysis on an Orbitrap Exploris 480 mass spectrometer (Thermo). To assemble a chromatogram library, six gas-phase fractions were acquired on the Orbitrap Exploris with 4 m/z DIA spectra (4 m/z precursor isolation windows at 30,000 resolution, normalized AGC target 100%, maximum inject time 66 ms) using a staggered window pattern from narrow mass ranges using optimized window placements. Precursor spectra were acquired after each DIA duty cycle, spanning the m/z range of the gas-phase fraction (i.e. 496–602 m/z, 60,000 resolution, normalized AGC target 100%, maximum injection time 50 ms). For wide-window acquisitions, the Orbitrap Exploris was configured to acquire a precursor scan (385–1015 m/z, 60,000 resolution, normalized AGC target 100%, maximum injection time 50 ms) followed by 50× 12 m/z DIA spectra (12 m/z precursor isolation windows at 15,000 resolution, normalized AGC target 100%, maximum injection time 33 ms) using a staggered window pattern with optimized window placements. Precursor spectra were acquired after each DIA duty cycle.

Following the acquisition, data were searched using an empirically corrected library, and quantitative analysis was performed to obtain a comprehensive proteomic profile. Proteins were identified and quantified using EncyclopeDIA (34) and visualized with Scaffold DIA using 1% false discovery thresholds at both the protein and peptide levels. Protein-exclusive intensity values were assessed for quality using ProteiNorm (35) Normalization methods evaluated included log2 normalization (Log2), median normalization (Median), mean normalization (Mean), variance stabilizing normalization (VSN) (36) quantile normalization (Quantile) cyclic loess normalization (Cyclic Loess) (37), global robust linear regression normalization (RLR), and global intensity normalization (Global Intensity) (38). The individual performance of each method was evaluated by comparing the following metrics: total intensity, pooled intragroup coefficient of variation (PCV), pooled intragroup median absolute deviation (PMAD), pooled intragroup estimate of variance (PEV), intragroup correlation, sample correlation heatmap (Pearson), and log2-ratio distributions. The data was normalized using cyclic loess. Statistical analysis was performed using linear models for microarray data (limma) with empirical Bayes (eBayes) smoothing to the standard errors (39). Few protein differences fulfilled the FDR-adjusted p-value < 0.05 statistical criteria, so an unadjusted p-value <0.055 was applied. STRING database was then used to analyze the list of differentially expressed proteins (DEPs) for enriched pathways and protein-protein interactions (40).

Quantitative Real-Time and Capillary Western Assay.

Differential expression of Deptor was validated using SYBR-green dye chemistry on CFX96 Bio-Rad Real Time PCR Platform. cDNA was produced from RNA using iScript reverse transcription (Bio-Rad) and real-time PCR was performed using the iQ-SYBR Green Supermix (Bio-Rad) with primers purchased from IDT PrimeTime qPCR Primers for Deptor Exon3–4 and Exon8–9. Primers for Actb and Hprt1 were used for normalization. Statistical analysis of real-time PCR data was performed by Bio-Rad Maestro Software. The data are presented as mean ± SEM.

HSRA rat kidney tissues were collected and snap-frozen in liquid nitrogen. Protein was isolated using the ChemCruz RIPA Lysis Buffer System (Santa Cruz Biotechnology, Cat. No. sc-24948) supplemented with protease and phosphatase inhibitors (PMSF, protease inhibitor cocktail and sodium orthovanadate) provided in the kit. Tissue samples were homogenized, and lysates were incubated on ice for 30 minutes. The homogenates were centrifuged at 10,000 × g for 10 min at 4 °C, and the supernatant was collected. Protein concentration was determined using a BCA protein assay (Bio-Rad Protein Assay Dye Reagent, Cat. No. 5000–0006) following the manufacturer’s protocol. Protein Samples were prepared for analysis using the Jess Simple Western system (ProteinSimple, Bio-Techne). Sample input was 1μg and 3 μl in 0.1X Sample Diluent (ProteinSimple) and combined with Fluorescent Master Mix, as per the manufacturer’s protocol. Samples were denatured at 95 °C for 5 minutes and stored on ice before loading.

Total protein detection was performed for normalization using the Replex functionality. Replex allowed consecutive detection of total protein followed by the target protein on the same capillary. Total protein detection was performed using the Total Protein Detection Module (ProteinSimple, Cat. No. DM-TP01), which includes biotin labeling reagents and Streptavidin-HRP for chemiluminescent detection. Following total protein detection, the capillaries were stripped and re-probed with the DEPTOR primary antibody (Invitrogen, Cat. No. MA5-45977, diluted 1:25 in Antibody Diluent 2) and the HRP-conjugated anti-rabbit secondary antibody provided by ProteinSimple. The prepared samples, blocking reagents, antibodies, and detection reagents were loaded into the assay plate. Electrophoresis and immunodetection were performed automatically by the Jess system. Data were analyzed using Compass Software (ProteinSimple), and signal intensity for DEPTOR was normalized to the total protein signal.

Statistical Analysis.

The methods of statistical analysis for omics datasets are detailed above. For animal experiments (HSRA-S and HSRA-C) followed over time, a two-way ANOVA was used, with p<0.05 to designate significance. For BP and organ weight, an unpaired two-tailed t-test was used for comparison of HSRA-S and HSRA-C, with p<0.05 designating significance. All data are presented as mean + standard error (SEM) (GraphPad Prism 8, La Jolla, CA).

Several figures were generated, in part, using BioRender.com.

RESULTS

Proteinuria and Blood Pressure in HSRA model.

A temporal analysis of urine protein excretion (i.e., proteinuria >20 mg/24 hours) demonstrated a gradual increase in the HSRA-S compared to control (HSRA-C) animals (Figure 1A) starting at 7 months of age. By month 18, HSRA-S exhibited a 6-fold increase in proteinuria (HSRA-C = 8.3± 1.5 mg/24 hours vs HSRA-S=51.9± 3.4 mg/24 hours). No detectable difference in terminal mean arterial pressure (as measured under isoflurane) was observed in the HSRA-S compared to the HSRA-C (Figure 1B). As expected, there was significant kidney hypertrophy in HSRA-S compared to a single kidney in HSRA-C (Figure 1C–D). The physiological study of the HSRA-S and HSRA-C animals from young to old is consistent with previous studies (21) and the hypothesis that reduced nephron number in the HSRA-S predisposes animals to develop increased kidney injury. To identify early molecular changes in the kidney between HSRA-S and HSRA-C rats, a multiomics approach combining Methylseq (RRBS/oxRRBS), snRNAseq, and discovery proteomics was performed at 4 weeks of age (Figure 2) before any overt renal injury differences were observed, although there was significant kidney hypertrophy between HSRA-S compared to HSRA-C (Figure 1C).

Figure 1: Temporal measurement of renal injury in HSRA-C and HSRA-S rats and terminal measurements.

(A) Time course of proteinuria measurements from months 8 to 18. (B) Mean arterial pressure (MAP) obtained from terminal blood pressure measurements. (C) Kidney weight comparing -S to -C (week 4 and month 18). (D) Representative histological sections of kidneys stained with Hematoxylin and Eosin (H&E). Data are presented as mean ± SEM. p<0.05 using independent t-test.

Figure 2: Experimental workflow of multiomics analysis of kidney from HSRA-C and HSRA-S.

Graphical representation of the multiomics approach and methodology for molecular profiling in 4-week-old HSRA rats including epigenetic analysis using RRBS/oxRRBS, transcriptomic (snRNAseq), and proteomic profiles using discovery proteomics.

Genome-wide Methylation.

An epigenetic analysis using reduced representation bisulfite sequencing (RRBS) and oxidative RRBS (oxRRBS) approaches identified few differences when comparing individual cytosine and promoter region methylation between HSRA-S and HSRA-C in the kidney at week 4. RRBS (combined 5mC+5hmC) identified 5 differentially methylated cytosines (DMCs) and 6 differentially methylated regions (DMRs) with a q-value < 0.01 and a methylation difference > 10% (Figure 3A, Table 1). The majority of DMCs were associated with intergenic (43%) or intronic regions (29%), with 29% falling within either promotor or exons (Figure 3A). The oxRRBS (5mC only) analysis identified 6 DMCs and 4 DMRs at the same cutoff (Figure 3B, Table 2). Similar to RRBS, DMCs for oxRRBS were observed mostly in the intergenic/intron region (79%), with only 21% falling within the promotor or exon region (Figure 3B). There was a strong correlation in methylation pattern (either RBBS and oxRRBS) from each group (-S and -C) based on PCA and sample clustering (Figure S1). An expanded list of DMCs and DMRs detected using a less stringent cutoff of >5% methylation difference for both analyses is provided in Table S1.

Figure 3: Methylseq using reduced representation bisulfite sequencing from the kidney of HSRA-C and HSRA-S.

(A) Chromosomal distribution and genomic location of differentially methylated cytosines detected by RRBS (5-methylcytosine (5mC) + 5-hydroxymethycytosine (5hmC)) (B) Chromosomal distribution and genomic location of differentially methylated cytosines detected by oxRRBS (5mC modifications only).

Table 1:

Differentially methylated cytosines (DMCs) and differentially methylated regions (DMR) identified by reduced representation bisulfite sequencing (RRBS).

(A) DMCs for combined 5mC (5-methylcytosine) + 5HmC (5-hydroxymethylcytosine).
Gene Symbol	dist.to.TSS	chr	start	end	pvalue	qvalue	meth.diff
Prdm11	−60	3	79005057	79005057	1.13E-07	0.002772795	11.28
Tmem72	260	4	149973291	149973291	8.38E-07	0.008569464	−20.85
Usp19	−79	8	109192346	109192346	1.50E-07	0.003270384	−12.87
Alox12e	−154	10	55042010	55042010	5.11E-07	0.006523704	−18.79
Nkx2–6	3671	15	44446820	44446820	5.05E-07	0.006482419	15.23

(B) DMRs for combined 5mC + 5HmC.
Gene Symbol	chr	start	end	pvalue	qvalue	meth.diff
Pax8	3	7241863	7244363	8.26E-04	0.003155244	−14.91
Mettl7b	7	1391026	1393526	0.000196678	0.000968667	−11.36
Scarf1	10	60357212	60359712	0.000134954	0.000710968	10.09
Slc46a1	10	63359503	63362003	5.14E-28	3.48E-25	−11.34
Erg	11	34781606	34784106	0.000135483	0.000713249	−10.59
Pcdhb9	18	29103297	29105797	0.000702396	0.00275822	−11.02

The table includes gene symbol, distance to transcription start sites (TSS), chromosomal location, and genomic start/end positions, with corresponding p-values, q-values, and mean methylation differences. A positive meth.diff means that locus or region shows hypermethylation and a negative meth.diff means hypomethylation.

Table 2:

Differentially methylated cytosines (DMCs) and differentially methylated regions (DMR) identified by oxidative reduced representation bisulfite sequencing (oxRRBS).

(A) DMCs for only 5mC.
Gene Symbol	dist.to.TSS	chr	start	end	pvalue	qvalue	meth.diff
Adgrl2	2813	2	238324329	238324329	8.50E-09	0.000520915	−11.39
Pnma1	1004	6	103783026	103783026	3.09E-09	0.000250716	−15.41
Suox	1240	7	1104479	1104479	8.22E-07	0.007257509	−15.59
Sparcl1	−471	14	5632345	5632345	1.50E-06	0.00969029	18.59
Zfp503	2239	15	2315297	2315297	2.95E-07	0.004304031	−12.76
Clpsl2	2682	20	6610808	6610808	1.96E-08	0.00088557	16.11

(B) DMRs for only 5mC.
Gene Symbol	chr	start	end	pvalue	qvalue	meth.diff
Scgb1a1	1	205980110	205982610	2.08E-05	0.000104631	11.25
Dmbx1	5	129402540	129405040	2.84E-06	1.92E-05	10.76
Cnn1	8	20630433	20632933	0.000151913	0.000572392	−11.03
Dao	12	42612546	42615046	1.84E-16	1.24E-14	−10.04

Similar information as Table 1. A positive meth.diff means that locus or region shows hypermethylation and a negative meth.diff means hypomethylation.

By comparing datasets derived from RRBS and oxRRBS (at a 5% cutoff), genomic regions were identified with significant differential methylation between the two. Among the DMRs identified, seven elements showed alterations between RRBS and oxRRBS datasets: Rabepk, Tsc22d4, Glra4, Kynu, Hpd, Slc6a19, and Epn1. It was observed that not all shared elements between RRBS and oxRRBS datasets exhibited the same directionality (hyper vs hypo) in methylation percent differences. Specifically, two elements, Rabepk and Glra4, demonstrated opposing trends; with Rabepk and Glra4 showing a negative methylation percent difference in the RRBS analysis and a positive one in the oxRRBS analysis.

Single Nuclei RNA Sequencing.

Single nuclei RNA sequencing (snRNAseq) was used to identify transcriptome differences in specific cell types of the kidney between HSRA-S and HSRA-C (Figure 4A). As expected snRNAseq produced a similar distribution of cells/types in HSRA-S and HSRA-C kidneys (Figure 4B). Using canonical markers known for kidney cell types, the analysis identified 14 distinct kidney cell types, each exhibiting unique gene expression patterns (Figure 4B–D). Figure S2A shows the top ten genes expressed uniquely by each cluster when compared to all other clusters, many of which were used to define the clusters. Figure 4D shows one representative marker that was used for cell type annotation, but each cluster was annotated by the expression of multiple markers. No significant difference in the proportion of each cell type (versus total) was observed between HSRA-S and HSRA-C kidneys (Figure S2B). For example, HSRA-S podocytes represented 3.9% and for HSRA-C it was 3.8%. An interesting cluster, near the proximal tubule clusters, expressing markers related to cellular proliferation/mitosis (Mki67, Cenpf, Top2a, Kif20b, Cenpe, Tpx2, Sgo2, Kif23, etc.) was also identified and defined as “proliferating.” Differentially expressed genes (DEG) were analyzed using Seurat for each cluster at an adjusted p-value of 0.05. The proximal tubule segments (PT) presented the most DEGs (Figure S3), which may be due to these cluster’s larger cell population size. To mitigate the impact of PT cluster size on statistical analysis (multiple testing), pseudobulking and differential expression analysis were performed via DESeq2 on each cluster. These results showed a reduced, yet significant, list of DEGs for each cell type, with the mesangial cell (MC) cluster exhibiting the most differences (Figure. 4E). A comprehensive list of DEGs by cell type is provided in Table S2.

Figure 4: Single-nuclei RNAseq and differential gene expression of kidney cell types between HSRA-C and HSRA-S.

(A) Detailed diagram of the nephron and associated cell types expected to be identified using snRNAseq. (B) Clustering of cells in a UMAP plot, color-coded either HSRA-S (all n=3) or HSRA-C (all n=4). (C) Annotation of individual clusters by cell type using canonical gene expression signatures. (D) Violin plots depicting the unique and/or high expression patterns of selected marker genes across the identified cell type populations. (E) Bar graph presenting the number of differentially expressed genes using a pseudobulking (DESeq2) approach between the HSRA-C and HSRA-S groups.

Within the Seurat-processed lists, several notable DEGs between HSRA-S and HSRA-C were observed. Figure 5 shows these genes of interest and their differential expression comparing -S to -C for each cell type/cluster with red being upregulated in the -S group and green downregulated. Specifically, Pla2g7 was upregulated in the podocyte cluster of the HSRA-S group, while Cyp4a8 displayed upregulation across many clusters. Observations into genes associated with cell adhesion and the extracellular matrix (ECM) indicated a general downregulation of genes, except for Col27a1, which exhibited upregulation. The downregulation of Col13a1 in the HSRA-S group was particularly distinct within the glomerular cell cluster.

Figure 5: Heatmap of select genes across cell clusters from snRNAseq data processed by Seurat.

Each row represents a gene of interest, and each column corresponds to a distinct cell-type cluster. The genes are categorized manually into functional groups, denoted by the labels on the right. Color intensity indicates the degree of expression change: red signifies upregulation, green indicates downregulation, and white represents no significant change in the -S group. The color scale is capped at ±2.5 to enhance the visibility of smaller changes in expression; values exceeding this range are represented by the maximum color intensity.

Querying genes associated with ion transport showed a general upregulation in the -S group for several members, including Slc5a12, Slc5a11, Slc5a10, Slc4a4, Slc34a1, and Slc27a2. Conversely, Slc16a7 was found to be downregulated in several clusters. Querying genes involved in kidney development identified dysregulation of important genes such as Wnt9b, Pax2, Pax8, Notch2, Eya2, etc., some of which displayed cell type-specific differential expression.

Genes involved in metabolism were dysregulated across various clusters, specifically G6pc and Pdk4. Also, Ppara was identified as upregulated within the glomerular cell (GC) cluster. Examining genes associated with mRNA processing and RNA binding (Srrm2, Rbms1, Rbms2, and Pabpn1) showed dysregulation in several clusters. A group of genes related to the mTOR signaling pathway, including Rptor, mTOR, Deptor, and Dapk1, was found to be downregulated in the -S rats in certain cell clusters (mostly proximal and distal tubular cell clusters). There were also several differentially expressed genes involved in embryogenesis (Jag1, Zbtb20, etc.), differentiation (Meis1, Zeb2, etc.), and the cytoskeletal structure (Actn1, Synpo2, etc.). The full DEG lists for each cluster, along with fold changes comparing HSRA-S to HSRA-C, are available in Table S2.

In comparing the promoter region methylation (DMR) and transcriptome datasets (Seurat), there were overlaps between the Seurat (transcriptome), RRBS, and oxRRBS (methylation) analyses. Two genes, Hpd and Slc6a19, exhibit differential expression in our Seurat analysis but also showed differential methylation in both RRBS and oxRRBS datasets. Interestingly, the percent methylation difference of these regions is consistent in direction and magnitude between RRBS and oxRRBS analysis, and they also follow the general trend that hypomethylation leads to increased gene expression.

A subset of eleven elements (Pax8, Aqp1, Slc11a2, Scarf1, Aldob, Seroinc1, Mettl7b, Loxl1, Hpd, Slc6a19, and Erg) were identified as common between Seurat differential expression analysis and RRBS DMRs. The analysis also revealed twelve elements (Kif12, Slc3a1, Tulp4, AABR07051450.1, Slc23a1, Ass1, Naglt1, Slc66a1, Spp2, Hpd, Slc6a19, and Wscd1) that were common between Seurat analysis and oxRRBS DMRs. In examining the relationship between methylation and gene expression, the data showed a more pronounced overlap between the Seurat DEG and oxRRBS overlap, where 7 out of the 12 common elements followed the general trend that hypermethylation correlates with decreased gene expression and hypomethylation correlates with increased gene expression. In contrast, within the Seurat and RRBS overlap, only 4 out of the 11 shared elements conformed to this trend.

Gene Set Enrichment Analysis of Differentially Expressed Genes.

GSEA identified the activation and suppression of key pathways from cluster-specific DEG lists (Figure 6, Figure S4). Globally, pathways related to ion channel activity were activated in the -S group. At the same time, those associated with the negative regulation of locomotion and motility, cell junction assembly, collagen-containing extracellular matrix (ECM), and kidney development were suppressed. Specifically, in the podocytes, pathways relevant to post-Golgi vesicle-mediated transport and cell differentiation involved in kidney development were downregulated. The mesangial cell (MC) cluster showed suppression in epithelium migration and external encapsulating structure organization. In addition, the proliferating cell cluster demonstrated an increase in ribosome biogenesis and rRNA processing pathways, alongside a decrease in DNA repair and DNA replication pathways in the -S group.

Figure 6: Enrichment of biological process, molecular function, and cellular component GO-terms across kidney cell types snRNAseq.

Dot plots representing the top 10 activated and top 10 suppressed Gene Ontology (GO) terms from GSEA of DEGs across (A) all kidney cell types, (B) podocytes, (C) mesangial cells, and (D) proliferating cells. The dot size reflects the count of genes in the cluster-specific differentially expressed gene list associated with each pathway. The color gradient represents the p-value.

Discovery Proteomics.

Proteomic analysis at an adjusted p-value threshold of 0.055 yielded 4 differentially expressed proteins (DEPs). The only protein downregulated in the HSRA-S group was PODXL. GSTP1, UGT1A1, and UGT2B1 were identified as upregulated in the HSRA-S kidney. The number of DEPs increased to 366 when considering an unadjusted p-value of 0.055 (Figure. 7A). STRING Protein-Protein Interaction Networks and Functional Enrichment Analysis (40) showed that our protein network (list of DEPs) has significantly more interactions than expected. There were 137 Biological Process, 18 Molecular Function, and 63 Cellular Component GO-Terms significantly enriched in our DEPs dataset comparing -S vs -C. There were also 54 pathways (Reactome Pathways and WikiPathway) significantly enriched in the dataset. Some of the top biological processes (GO terms) ranked by strength were nuclear pore localization and positive regulation of RNA splicing (Figure 7B). WikiPathways analysis showed enrichment of pathway for ‘mRNA processing’ among these proteins. Also, within the GO term list, there was enrichment for pathways associated with ‘Mitochondrial translation’, ‘ATP metabolic process’, ‘Oxidative phosphorylation’, and ‘Cellular respiration’. A full analysis of the proteomic dataset is provided in Table S3.

Figure 7: Discovery proteomics and comparison with transcriptome from single nuclei RNAseq data.

(A)Volcano plot of differentially expressed proteins (DEPs) with unadjusted p-value < 0.055 comparing HSRA-S to HSRA-C. The top 10 significant DEPs and those with |log2FC| > 1 are labeled. (B) Bar graph of the top 10 enriched Biological Process Gene Ontology (GO) terms from STRING Functional Enrichment Analysis, ranked by strength and colored by false discovery rate (FDR). (C) Venn diagram showing the overlap of identified proteins/genes from discovery proteomics with an unadjusted p-value < 0.055, global comparisons from Seurat, adjusted p-value < 0.05, and pseudo bulked snRNAseq data p-value < 0.05, processed using DESeq2. (D) List of 15 overlapping proteins/genes between the proteomic and transcriptomic data in Panel C. The table lists each gene/protein alongside corresponding fold changes and p-values from proteomic and transcriptomic data.

The snRNAseq DEG dataset was compared to the discovery proteomics DEP dataset using Seurat analysis and pseudo bulk/DESeq2 (Figure 7C). Of the 4,640 DEG identified by Seurat analysis, 881 (out of 966), or 91% were also observed in the pseudo bulk/DESeq2. Of the 366 DEPs, 111 were observed in the Seurat DEG dataset, with only 16 overlapped with pseudo bulk/DESeq2. An overlap of 15 genes/proteins was observed between the both transcriptomic analysis and the proteomic dataset. Among these, Amdhd2/AMDHD2 and Deptor/DEPTOR were consistent in the direction of their changes across the omics layers with Deptor/DEPTOR being downregulated and Amdhd2/AMDHD2 upregulated. (Figure 7D). Differential expression and protein levels were assessed in a separate group of animals for Deptor/DEPTOR. Significant gene expression changes in Deptor (using two probes) were observed between HSRS-C and -S groups (Figure 8A). Western analysis identified DEPTOR was observed as a monomer (~50kD) and dimer (~100kD), while there appeared a slight reduction in DEPTOR in the monomer for -S, there was no significant difference observed in monomer, dimer, or total protein levels, at least for this small group of animals.

Figure 8: Quantitative real-time PCR and western analysis of Deptor in HSRA kidney.

(A) Bar graph of gene expression differences between HSRS-C and HSRA-S kidney samples using probes directed at Deptor exon 3–4 and exon 8–9. (B) Jess Simple Western (ProteinSimple, Bio-Techne) of DEPTOR protein and analysis using Compass Software (ProteinSimple). Signal intensity for DEPTOR was normalized to the total protein signal.

DISCUSSION

A time course analysis of the HSRA model, up to 18 months of age, demonstrated a significant increase in proteinuria and kidney hypertrophy in HSRA-S animals compared to HSRA-C animals, consistent with previous studies (21, 22). While previous studies observed a modest (+10 mmHg) increase in blood pressure (BP) in conscious HSRA-S animals at 20 months of age compared to HSRA-C, the current study did not detect an elevation in BP when measured under anesthesia at 18 months of age. This may be attributed to the use of anesthesia (isoflurane) during BP measurement in our study, as anesthetic agents can depress cardiovascular function and mask subtle increases in BP (41).

Despite not detecting elevated BP in our current study, the increase in proteinuria and kidney injury with age supports the idea that reduced nephron number is linked with renal injury and an increased risk of hypertension over time (1). The relationship between nephron number, renal injury, and blood pressure involves several molecular pathways, including the activation of the renin-angiotensin system (RAS), oxidative stress, and inflammatory processes (42). Reduced nephron number results in compensatory hyperfiltration and hypertrophy in the remaining glomeruli, allowing for a normalization of the total glomerular filtration rate (GFR) and maintenance of blood pressure, but increases in glomerular capillary pressure promote glomerulosclerosis. These mechanisms meant to maintain biological homeostasis end up leading to the further loss of nephrons (43).

For the methylation analysis, distinct methylation patterns were observed between the RRBS and oxRRBS analyses, especially in the opposing trend of methylation changes in DMRs identified by both. For DMRs such as Rabepk and Glra4, we observed a decrease in methylation percentage in the RRBS analysis, contrasted with an increase in the oxRRBS analysis when comparing HSRA-S and HSRA-C kidney. The observed decrease in methylation in the RRBS data, which measures both 5mC and 5hmC, combined with the observed increase in methylation observed in the oxRRBS data (specific for 5mC), points towards a large amount of 5hmC modifications of these regions in the -S group compared to -C. This conversion is a hallmark of active demethylation, where the epigenetic mark associated with gene repression (5mC) is transformed into a mark (5hmC) that is often linked with the initiation of gene activation processes (44).

In the snRNAseq analysis, we observed a proliferating cell cluster transcriptionally associated with the proximal tubule (PT) clusters, as inferred from spatial proximity in the UMAP plot. The presence of this cluster suggests ongoing kidney maturation or tubular elongation is still occurring at this stage. The pathway analysis of the proliferating cell cluster DEG list identified the upregulation of ribosome biogenesis pathways in the HSRA-S kidney. This lines up with known behaviors of differentiating cells (45). This upregulation indicates an increase in protein synthesis and cellular growth activities, essential features as cells transition to more specialized states. Simultaneously, the observed suppression in DNA repair and replication pathways in the proliferating cell cluster may indicate the cell’s prioritization of functional adaptation over long-term genomic stability, a trade-off that occurs during the differentiation process (46).

A dysregulation of genes previously associated with kidney development was observed across many cell type clusters, such as Lifr, Pax2, Pax8, Notch2, Vegfa, Lrp6, Sox6, Sox5, Fgf9, Fgfr2 & Fgfr1, Eya2, Wnt9b, Lrp2, Lrp1, Robo1, Robo2, P3h2, and Epha4 with some exhibiting cell type-specific differential expression (47–64).

The upregulation of Vegfa is significant and could represent a compensatory response aimed at promoting angiogenesis in the context of reduced nephron number. Conversely, the general trend of downregulation among other critical genes like Lifr, Pax2, Notch2, and Sox9 supports the notion of impaired nephrogenesis. For instance, Lifr downregulation is noteworthy as mutations leading to its increased transcript degradation have been linked to congenital anomalies of the kidney and urinary tract (CAKUT) in humans (47).

When analyzing the most significant DEPs (adjusted p-value < 0.055), PODXL was downregulated in the -S group. Podocalyxin (PODXL) is essential for the formation and maintenance of podocyte foot processes and is a necessary component of the glomerular filtration barrier (65). A decrease in the HSRA-S group may be expected due to the decreased nephron endowment/density. GSTP1, UGT1A1, and UGT2B1 were upregulated in the HSRA-S kidney. However, it has been shown that the overexpression of Glutathione S-transferase P1 (GSTP1) in spontaneously hypertensive rats (SHR) resulted in a decrease in blood pressure (66). The increases in UGT1A1 and UGT2B1 are also interesting. UBT1A1 mRNA is expressed in many tissues including the kidney, while UGT2B1 mRNA was predominately detected in the liver in Sprague-Dawley (SD) rats. The UDP-Glucuronosyltransferases (UGTs) are phase II biotransformation enzymes that glucuronidate numerous substrates. This increases the water solubility of the substrate and facilitates renal excretion of the resulting glucuronide conjugate (67).

Identification of enrichment for pathways ‘Mitochondrial translation’, ‘ATP metabolic process’, ‘Oxidative phosphorylation’, and ‘Cellular respiration’ in our proteomic analysis (unadjusted p-value < 0.055) are relevant (Table S3). As proximal tubule cells make up over half of the cells in the rat kidney, much of our identified proteome differences are likely associated with the proximal tubule (68). Proximal tubule cells rely heavily on mitochondrial oxidative phosphorylation for ATP production to support their reabsorption and transport functions. The enrichment in these pathways suggests potential metabolic changes as a response to cellular stress (69).

Interestingly, there was downregulation of Deptor/DEPTOR across both the transcriptome and proteome. DEPTOR, an acronym for DEP domain-containing mTOR-interacting protein, is a protein that plays a critical role in the regulation of the mammalian target of rapamycin (mTOR) signaling pathway (70). The mTOR pathway is a central regulator of cell growth, proliferation, survival, and metabolism, responding to nutrients, growth factors, and cellular energy levels (71). DEPTOR acts as a natural inhibitor of mTOR activity (70). It is part of the mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), where it negatively regulates their activities. By inhibiting mTOR signaling, DEPTOR can affect various cellular processes controlled by mTOR, including protein synthesis, ribosome biogenesis, autophagy, and cell cycle progression (71).

Previous studies have demonstrated that a decrease in DEPTOR levels alone can trigger cellular differentiation (72). This indicates that the decrease observed in our study on the transcript and protein level may have a significant impact on the developmental and maturation processes within the HSRA kidney. This is especially evident in the proliferating cell cluster, which exhibits increased ribosome biogenesis and decreased DNA repair activities in the HSRA-S kidney, both of which are associated with cellular differentiation (45, 46). Chronic decreased DEPTOR expression could lead to unchecked mTOR activity, which would drive premature or excessive differentiation of nephron progenitor cells. If progenitor cells differentiate too early or inappropriately, there would be a depletion of the progenitor pool, resulting in a reduced number of nephrons being formed (4). This scenario suggests that decreased DEPTOR expression is causative in disrupting the balance between stem cell self-renewal and differentiation. Maintaining this balance is critical for the normal branching and developmental processes in the kidney, and disrupting it can lead to the impairment of nephron formation (73).

Alternatively, the observed decrease in DEPTOR could be part of a compensatory response to nephron deficiency. In this case, the activation of the mTOR pathway is an attempt by the kidney to enhance proliferation and differentiation to compensate for the deficit in nephron number (74). This would lead to an increased tubule volume by growth in the tubular cell size or numbers, which would serve to maximize the remaining nephron function (such as reabsorption capacity). This is consistent with the observed hypertrophy in the HSRA-S kidney at week 4. In response to low nephron number, the remaining nephrons undergo hyperfiltration to maintain overall kidney function as previously demonstrated in the model (21). The hyperfiltration and increased tubular reabsorption required raise the metabolic demand on the kidney.

mTOR has also been shown to regulate proximal tubule endocytosis and nutrient transport thus activation of mTOR may increase nutrient transport by phosphorylation and activation of transporters in the epithelial cells of the proximal tubule. (75). To meet this demand, tubular cells would increase production of ATP. This is consistent with the enrichment of GO term pathways ‘Mitochondrial translation’, ‘ATP metabolic process’, ‘Oxidative phosphorylation’, and ‘Cellular respiration’ in our proteomic analysis.

Research has shown that manipulation of the mTOR pathway in mice can have profound effects on nephrogenesis. Conditional deletion of Mtor in nephron progenitor cells disrupted nephrogenesis, while hemizygous deletion resulted in a reduced nephron endowment (76). Conversely, deletion of Tsc1, an upstream inhibitor of mTOR, led to a lethal tubular lesion. Interestingly, hemizygous deletion of Tsc1 was associated with a 25% increase in nephron number, which suggests a fine balance of mTOR pathway activity is necessary for proper nephron development and function (76). The other gene/protein observed in the transcriptome and proteome analyses was Amdhd2/AMDHD2. Recent studies using mESCs have identified AMDHD2 as a regulator within the hexosamine biosynthetic pathway (HBP) (77). The HBP produces the essential metabolite UDP-GlcNAc and plays an important role in metabolism, health, and aging. Nutrient availability, along with signaling molecules such as mTOR, AMPK, and stress-regulated transcription factors, regulates the HBP (78).

The high upregulation of Pla2g7 in the HSRA-S kidney, specifically within podocytes, is interesting due to its ability to hydrolyze arachidonic acid from membrane phospholipids (79). This arachidonic acid molecule can then serve as a precursor for various biologically active lipids (80). The potential effects of this observation increase when considered alongside the upregulation of Cyp4a8 in renal tubules. Cyp4a8 has been shown to convert arachidonic acid into 20-HETE (81). This metabolite plays an important role in renal physiology, potentially in its renoprotective effects. 20-HETE has been shown to induce constriction of preglomerular arteries, which helps to regulate blood flow to the glomeruli (82). The upregulation of these enzymes may be an attempt to mitigate kidney glomerular damage through localized vascular adjustments by mobilizing arachidonic acid into the tubules and subsequent conversion to 20-HETE or other biologically active lipids. It is also important to point out that UGTs like those identified to be upregulated in the proteomic data (albeit different isoforms), play a role in the metabolism of the arachidonic acid metabolite 20-HETE (83). In addition, 20-HETE has also been linked to superoxide production in the mitochondria of vascular smooth muscle cells and activation of mitogen-activated protein kinase 1 and 3 (84). This could contribute to compensatory cellular hypertrophic growth (85, 86). Of interest, GSTP1 which is upregulated in the proteomic data, also exhibits antioxidant and anti-inflammatory roles, potentially balancing out the oxidative stress observed in states of solitary kidney (87). Recently, Studies using deceased donor kidneys identified upregulation of the arachidonic acid pathway in kidneys with impaired renal function, and inhibition of cytosolic phospholipase A2 (cPLA2) reduced inflammation of proximal tubular epithelial cells in vitro (88). When comparing the promoter region methylation differences to differences observed in the transcriptome, Slc6a19 was identified as differentially methylated and expressed in the -S rats compared to the -C. We also identified the differential expression of Slc5a12. These transporters function as neutral amino acid and lactate/ketone body transporters, respectively. It has been shown that these interact with Slc5a2 (SGLT2). In general, across the transcriptome data, we observed the upregulation of many solute carriers in the -S group.

No strong correlation was observed between the transcriptome and proteomic differences between HSRA-S and HSRA-C kidneys. The enrichment of pathways associated with nuclear pore localization and RNA splicing in the proteomics data along with the dysregulation of genes associated with mRNA processing may explain the discordance between the transcriptome and proteome data. However, discrepancies between transcriptome and proteomic data are not uncommon in multiomics studies (89) and can be explained by various mechanisms including transcript degradation, translation efficiency, post-transcriptional modifications (e.g., RNA splicing, editing, polyadenylation), and post-translational modifications (e.g., phosphorylation, ubiquitination, glycosylation), which influence mRNA stability, protein activity, and their interaction within the cell.

In summary, an overview of the omics datasets, including RRBS, snRNAseq, and discovery proteomics is summarized in Figure 9 to highlight the major gene/pathway changes observed between HSRA-S and -C kidney. The overarching hypothesis is that genetic factors in the HSRA lead to the failure of one kidney to form and altered development in the remaining kidney. Each dataset provides a unique perspective of specific genes/pathways that could be causally linked to these genetic factors as well as represent early compensatory changes. Many of the observed changes are likely related to compensatory mechanisms as a result of reduced renal mass as there is already significant (~60%) hypertrophy in the -S kidney at week 4 as seen in previous studies (21). Ultimately, these changes during the early life period (and during kidney development) set HSRA-S animals on a path to be susceptible to developing progressive kidney injury.

Figure 9: Overview of the key findings across the omics datasets.

The top genes/pathways identified from the multiomics analysis of the HSRA rat model, including RRBS, oxRRBS, snRNAseq, and discovery proteomics. Of note, genes such as Pla2g7, Pax8, and Deptor are dysregulated. These findings are discussed in more detail in the discussion section, with pathways identified through snRNAseq related to ‘kidney development’, ‘ECM’, ‘solute carriers’, ‘metabolism’, and ‘mRNA processing’, while proteomics data identified pathways associated with ‘RNA splicing’ and ‘mRNA processing’.

One key limitation of this study is species-specific differences in rats when compared to humans. The HSRA model, while extremely valuable, may not fully replicate human kidney development and disease. Additionally, this study focuses on the single timepoint of 4 weeks of age, which would not capture all of the developmental changes or the onset of disease that occurs later. Multiomics approaches, despite providing a comprehensive overview, are limited by resolution, sensitivity, and molecular detection limits inherent to each technique. Another limitation is the sample size for the snRNA-seq study. While this sample size is common for snRNA-seq studies, additional samples would increase statistical power to detect subtle transcriptome differences. Similarly, deeper sequencing could have captured additional low-abundance transcripts. Future studies with larger sample sizes and deeper sequencing could further extend these findings. While the quantitative real-time PCR did validate the expression differences in Deptor, the western analysis on the limited number of samples studied, did not show a significant difference. Ultimately, direct functional validation through knockdown or overexpression studies in model systems and/or in the HSRA model will be needed to conclusively demonstrate a role in kidney development, function, or maturation.

Future applications of this research extend into the evolving field of regenerative medicine, particularly through the development of strategies for kidney bioengineering. Thus, understanding the molecular mechanisms involved in kidney development, nephrogenesis, and renal compensation could ultimately improve outcomes for individuals with hypertension and CKD.

Supplementary Material

Supplemental data, including Figures (Figure S1–5) and Tables (Table S1–3) can be found at https://doi.org/10.6084/m9.figshare.26870476.

New and Noteworthy.

The HSRA rat is a novel model of nephron deficiency and provides a unique opportunity to study the association between nephron number and chronic kidney disease (CKD). Previous work characterized the impact of age, hypertension, and diabetes on the development of CKD in HSRA animals. This study examined early changes in epigenetics, cell-type specific transcriptome, and proteomic changes in the kidney that likely predispose the model to CKD with age.