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. 2025 Nov 13;8(12):2115–2127. doi: 10.1002/ame2.70094

Temporal glomerular gene expression dynamics during disease progression in a mouse model of hypertension‐accelerated diabetic kidney disease

Adam B Marstrand‐Jørgensen 1, Frederikke Emilie Sembach 1, Maria Ougaard 1, Ditte Hansen 2,3, Mette Viberg Østergaard 4, Henrik H Hansen 1, Louise S Dalbøge 1, Ole Jørgen Kaasbøll 5, Michael Christensen 1,✉
PMCID: PMC12884439  PMID: 41229272

Abstract

Background

The current understanding of diabetic kidney disease (DKD) has significant gaps regarding the underlying pathogenesis. In this study, we aimed to characterize the temporal progression of DKD using a state‐of‐the‐art mouse model of hypertension‐accelerated disease, integrating kidney biomarker analysis, histopathology, and glomerular transcriptomic profiling.

Methods

Female diabetic db/db mice received a single intravenous dose of adeno‐associated virus‐mediated renin overexpression (ReninAAV, week 5) and underwent uninephrectomy (UNx, week 4). db/db UNx‐ReninAAV mice were terminated at weeks 1, 4, 8, and 12 (n = 7–8 per group). Female db/m mice were used as healthy controls. Study endpoints included plasma and urine biochemistry, glomerulosclerosis scoring, quantitative kidney histology, and RNA sequencing of glomeruli isolated using laser‐capture microdissection.

Results

db/db UNx‐ReninAAV mice developed progressive albuminuria (from week 4) and glomerulosclerosis (from week 8). A pathway analysis of clustered gene regulations revealed broad glomerular transcriptome perturbations with signatures of increased extracellular matrix (ECM) turnover from week 8 and early onset of metabolic dysfunction. Markers of glomerular cell types and injury exhibited temporal regulation over the course of DKD, with early and sustained downregulation of endothelial markers, heterogeneous regulation of podocyte markers, and significant mesangial and parietal epithelial aberrations. Furthermore, the upregulation of cell injury markers confirmed progressive glomerular injury in the model.

Conclusion

The db/db UNx‐ReninAAV mouse model exhibits distinct temporal dynamics in glomerular cell markers, metabolic dysregulation, ECM remodeling, and injury. Together, these results highlight the utility of the db/db UNx‐ReninAAV model as a relevant preclinical platform for studying progressive DKD.

Keywords: db/db UNx‐ReninAAV mouse, diabetic kidney disease, glomerular transcriptomics, glomerulosclerosis, laser‐capture microdissection

The current study characterized temporal changes in glomerular gene expression, pathology, and biomarkers in a mouse model of hypertension‐accelerated diabetic kidney disease (DKD). Progressive albuminuria and glomerulosclerosis were paralleled by dynamic transcriptomic changes associated withmetabolic dysfunction, extracellular matrix remodeling, and glomerular cell dysregulation, supporting this model as a valid platform for preclinical DKD research and drug development.

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1. INTRODUCTION

Diabetic kidney disease (DKD) is a common complication of diabetes and the leading cause of end‐stage renal disease (ESRD) worldwide. 1 Clinical hallmarks of DKD include increased albuminuria and impaired kidney function. 2 , 3 , 4 Renal inflammation, glomerulosclerosis, and fibrosis are the histological hallmarks of DKD.

In addition to hyperglycemia, several risk factors predispose to DKD, particularly hypertension, obesity, and dyslipidemia. 5 Thus, lifestyle changes, along with tight glycemic control and lowering blood pressure, remain essential for the prevention and management of DKD. 6 , 7 , 8 , 9 More recently, sodium‐glucose cotransporter‐2 inhibitors, glucagon‐like peptide‐1 receptor agonists, and nonsteroidal mineralocorticoid antagonists have been approved for DKD. 10 , 11 , 12 , 13 , 14 Despite the significant benefits of these drug therapies, a significant proportion of DKD patients advance toward ESRD, ultimately necessitating hemodialysis or kidney transplantation. 15 This highlights the need for more effective therapeutic options and a better understanding of the molecular mechanisms underlying DKD progression.

The glomerulus is the main filtering unit of the kidney, and glomerular dysfunction plays a central role in the onset and progression of DKD. In DKD, pathological changes affect all glomerular cell types, manifesting as podocyte hypertrophy and detachment, glomerular endothelial cell (EC) apoptosis and dysfunction, mesangial cell proliferation with enhanced production of matrix proteins, and hypertrophy with vacuolization of parietal epithelial cells. 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 Collectively, these cellular alterations drive progressive mesangial expansion, glomerulosclerosis, and a decline in slit diaphragm integrity, ultimately compromising the glomerular filtration barrier and resulting in proteinuria.

A widely used translational model for studying glomerular alterations in DKD is the diabetic db/db mouse subjected to uninephrectomy (UNx) and adeno‐associated virus‐mediated renin overexpression (ReninAAV), referred to as the db/db UNx‐ReninAAV model. This model was developed to more accurately recapitulate the renal pathology of hypertension‐associated DKD and reproduces the key glomerular features of human DKD, including podocyte injury, mesangial expansion, and glomerulosclerosis. 24 , 25 , 26 , 27

Various methods have been utilized to characterize the glomerular transcriptome in humans and mice. Early studies employed manual microdissection of tissue biopsies in combination with microarrays to assess the human glomerular transcriptome. 28 , 29 Laser‐capture microdissection (LCM) allows for efficient and precise isolation of counterstained glomeruli, enabling downstream transcriptomic profiling of the glomerular compartment in the db/db UNx‐ReninAAV mouse. 27 Recently, atlas‐scale datasets based on single‐cell and single‐nucleus RNA sequencing (RNAseq) have become available, providing high‐resolution data on renal gene expression signatures and potential glomerular cell–cell interactions in humans as well as in the db/db UNx‐ReninAAV mouse model. 30 , 31 Although these studies have increased the understanding of DKD‐driven changes in the renal transcriptome, it remains to delineate how the glomerular transcriptome evolves over the course of progressive DKD, a gap that could be critical for advancing DKD research and drug development.

To address this gap, the present study investigated the longitudinal evolution of renal pathology and glomerular transcriptome profiles during the progression of DKD in the db/db UNx‐ReninAAV mouse model.

2. MATERIALS AND METHODS

2.1. Animals

The Danish Animal Inspectorate approved all experiments (license no. 2013‐15‐2934‐00843). All animal experiments were fully compliant with internationally accepted principles for the care and use of laboratory animals and accredited by AAALAC. Female db/db mice (5–7 weeks old, BKS.Cg‐Dock7m +/+ Leprdb/J, Charles River, Calco, Italy) were acclimatized for 1 week prior to injection with renin‐encoding AAV and were housed in a controlled environment (12 h light–dark cycle, lights turned on at 3:00 a.m., 23 ± 2℃, humidity: 50% ± 10%). Each animal was identified by an implantable subcutaneous microchip (PetID Microchip, E‐vet, Haderslev, Denmark). Age‐matched db/m (BKS.Cg‐Dock7m +/+ Leprdb/J) mice served as healthy controls. Mice had ad libitum access to tap water and standard chow (3.22 kcal/g, Altromin 1324, Brogaarden, Hoersholm, Denmark) and were weighed daily during the study. An overview of the study design is shown in Figure 1. db/db mice received an intravenous injection of ReninAAV (2.5 × 1010 genome copies, Vector Biolabs, Malvern, PA). One week post‐ReninAAV injection, db/db ReninAAV mice underwent UNx of the left kidney as described in detail previously. 32 After a 4‐week recovery period, db/db UNx‐ReninAAV mice were randomly assigned to four groups (n = 7–8 per group) based on body weight and fed blood glucose levels. The mice were killed by cardiac puncture under isoflurane anesthesia at week 1 (beginning of the study), week 4, week 8, or week 12 based on their group of randomization, corresponding to 4, 8, 12, and 16 weeks after UNx, respectively. Healthy db/m control mice (n = 5) were terminated at week 8. The right kidney was dissected and weighed, and the renal capsule was removed. One section was used for histology, and an adjacent section was snap frozen in dry ice and stored at −70℃ until LCM. LCM‐dissected glomeruli from age‐matched unoperated db/m mice served as healthy control samples (Figure 1). 27

FIGURE 1.

FIGURE 1

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Study outline. AAV, adenoassociated virus; BG, blood glucose; BW, body weight; HbA1c, hemoglobin A1c; IHC, immunohistochemistry; LCM, laser‐capture microdissection; PAS, periodic acid–Schiff; PSR, Picrosirius Red; RNAseq, RNA sequencing; uACR, urine albumin‐creatinine ratio; UNx, uninephrectomy.

2.2. Blood and urine biochemistry

Terminal tail vein blood samples collected in heparinized glass capillary tubes from nonfasted animals were analyzed for blood glucose and glycated hemoglobin A1C (HbA1c), whereas terminal spot urine samples collected from mice in clean cages were used to determine albumin‐to‐creatinine ratio as previously described. 26

2.3. Histology

Formalin‐fixed kidney sections were used for staining with periodic acid–Schiff (PAS) and Picrosirius Red (PSR) and for collagen 1a1 (Col1a1) immunohistochemistry as described elsewhere. 32 , 33 , 34 Stainings were scanned under a 20× objective in an Aperio ScanScope AT slide scanner (Leica, Deer Park, IL) using Visiomorph (Visiopharm, Hørsholm, Denmark) for quantitative image analysis of the entire renal section. 35 PAS staining was used for glomerulosclerosis scoring by AI‐assisted image analysis as described previously. 24 The glomeruli were classified based on the following criteria: GS0 (normal), GS1 (mild, sclerotic area 0%–24%), GS2 (moderate, sclerotic area 25%–49%), GS3 (severe, sclerotic area 50%–74%), and GS4 (global, sclerotic area 75%–100%).

2.4. Laser‐capture microdissection

Frozen kidney poles were sectioned (20 μm) using a cryostat (CM3050 S, Leica) and collected on polyethylene naphthalate membrane glass slides (LCM0522, Thermo Fisher). LCM was performed as described previously using the Arcturus PicoPure RNA isolation kit (KIT0204, Thermo Fisher) with DNAse treatment (AM1906, Thermo Fisher). 27

2.5. RNA sequencing

RNA sequencing (RNA‐seq) was performed using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (E7760L, NEB, Ipswich, MA) and the NS500/550 High Output Kit version 2 (75 cycles) (Illumina, San Diego, CA) on an Illumina NextSeq 550 platform (Illumina) as described previously. 27 Gene expression levels are shown as reads per kilobase million, or DESeq2 normalized counts. 36 STAR version2.7.0f was used to map the reads to the GRCm38 version 96 Ensembl genome. 37 FASTQC and PICARD were used to validate sequence and alignment quality. Differential expression analysis was performed using DESeq2 version 1.40.2, and Benjamini–Hochberg adjusted p (False Discovery Rate (FDA)) <0.05 was considered statistically significant. 36 , 38 Clustering and subclustering of temporal gene expression were performed with hclust on scaled, DESeq2 normalized counts, using Euclidian distances and the Ward.D2 clustering method. 39 Optimal number of clusters was determined using silhouette width. Pathway enrichment analysis was conducted with the R package HypeR version 2.0.0 using the hypergeometric method. 40 The MSigDB version of the Reactome database was used for pathway enrichment analyses. 41 , 42

2.6. Statistical analysis

Results are presented as mean ± standard error of the mean (SEM). Data were analyzed using R (version 4.3.1) and GraphPad Prism software (version 9.4.1). A two‐tailed Dunnett's test was used for significance testing, with Dunnett‐corrected p < 0.05 considered statistically significant. If data were non‐normally distributed, a Dunn‐corrected Kruskal–Wallis test was used instead, with Dunn‐corrected p < 0.05 considered statistically significant.

3. RESULTS

3.1. Temporal evolution of DKD in db/db UNx‐ReninAAV mice

db/db UNx‐ReninAAV mice were overtly obese compared to db/m mice (Figure 2A). Hyperglycemia was evident from the beginning of the study and significantly worsened during the study period, as indicated by significant and incremental increases in HbA1c and fed blood glucose levels (Figure 2B,C). db/db UNx‐ReninAAV mice showed clear evidence of progressive glomerulosclerosis manifesting from week 1 and increasing in severity during the 12‐week study period (Figure 3A,B). Correspondingly, the fraction of glomeruli with severe or global sclerosis (scores 3–4) increased over the course of DKD in db/db UNx‐ReninAAV mice, reaching a plateau at week 8. db/m mice demonstrated no or mild glomerulosclerosis (score 0–1, Figure 3A–C). Occurrence of severe albuminuria coincided with development and advancement of glomerulosclerosis in db/db UNx‐ReninAAV mice (Figure 3D; Table S1).

FIGURE 2.

FIGURE 2

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db/db UNx‐ReninAAV mice demonstrate overt obesity and advancing diabetes. (A) Body weight (g ± SEM). (B) HbA1c (% ± SEM). (C) Blood glucose (mmol/L ± SEM). Statistical analysis using two‐tailed Dunnett's test: a: p < 0.05 versus db/m, b: p < 0.05 versus week 1. n = 5–8.

FIGURE 3.

FIGURE 3

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Progressive worsening of renal disease in the db/db UNx‐ReninAAV mouse model of DKD. (A) Representative periodic acid–Schiff (PAS)–stained kidney sections from healthy db/m mice (week 12) and db/db UNx‐ReninAAV mice (weeks 1, 4, 8, and 12). Arrows indicate glomeruli with higher levels of sclerosis. Scale bar: 200 μm. (B) Groupwise distribution of glomerulosclerosis scores (fraction, %). (C) Fraction of glomeruli showing severe or global glomerulosclerosis (GS3 + GS4). (D) Urine albumin‐to‐creatinine ratio (ACR). Statistical analysis using Dunn‐corrected Kruskal–Wallis test: a: p < 0.05 versus db/m, b: p < 0.05 versus week 1. n = 5–8.

Histomorphometric analysis of Col1a1 IHC and PSR staining was performed on renal cortex whole sections (Figure 4A,B). It should be noted that the quantitative IHC analysis did not discriminate between glomerular and tubulointerstitial compartments. Although db/db UNx‐ReninAAV mice showed no significant change in percentage area of Col1a1 and PSR staining compared to db/m mice (Figure 4D,F), total kidney content of these fibrosis markers was robustly increased from week 4 onward (Figure 4E,G), reflecting progressive kidney hypertrophy in the model (Figure 4C).

FIGURE 4.

FIGURE 4

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Limited interstitial fibrosis is evident in the db/db UNx‐ReninAAV mouse model of DKD. (A) Representative Col1a1 (collagen 1a1)–stained kidney sections from healthy mice (db/m) and all timepoints. Scale bar: 200 μm. (B) Representative Picrosirius Red (PSR)–stained kidney sections from healthy mice (db/m) and db/db UNx‐ReninAAV mice (weeks 1, 4, 8, and 12). Scale bar: 200 μm. (C) Kidney weight (mg ± SEM). (D) Relative (% area ± SEM) and (E) Total kidney Col1a1 content (mg ± SEM). (F) Relative (% area ± SEM) and (G) Total kidney PSR content (mg ± SEM). PSR (% area) was analyzed using Dunn‐corrected Kruskal–Wallis test; all other data were analyzed using two‐tailed Dunnett's test: a: p < 0.05 compared to db/m mice, b: p < 0.05 compared to week, c: p < 0.05 compared to week 4. n = 5–8.

3.2. Temporal gene regulation reveals broad shifts in pathway regulation

LCM‐assisted harvesting of glomeruli provided enabled the assessment of glomerulus‐specific gene expression changes during disease progression in db/db UNx‐ReninAAV mice. Compared to healthy db/m mice, db/db UNx‐ReninAAV mice exhibited a progressively increasing number of glomerular differentially expressed genes (DEGs) until week 8 (upregulated: week 1, n = 1927; week 4, n = 2586; week 8, n = 2733; week 12, n = 2710; downregulated: week 1, n = 1342; week 4, n = 1778; week 8, n = 1973; week 12, n = 1997) (Figure 5A). Many genes exhibited temporally specific expression dynamics (Figure S1).

FIGURE 5.

FIGURE 5

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Clustering of regulated genes in the glomeruli reveals temporal patterns of regulation in the db/db UNx‐ReninAAV mouse model of DKD. (A) Total number of significantly up‐ and downregulated genes at each timepoint compared to healthy db/m mice. (B) Hierarchical clustering of the gene expression at a given timepoint using the Ward.D2 method. Ten clusters were formed. (C) Mean gene expression (black) as well as individual gene expression of all genes found in cluster 2 shown at each timepoint, with individual gene expression colored by their mean SD (standard deviation) from the mean. (D) Top 10 major Reactome pathways most enriched in cluster 2, using hypergeometric pathway analysis. (E) Mean gene expression (black) as well as individual gene expression of all genes found in cluster 8 shown at each timepoint, with individual gene expression colored by their mean SD from the mean. (F) Top 10 major Reactome pathways most enriched in cluster 8, using hypergeometric pathway analysis. (G) Mean gene expression (black) as well as individual gene expression of all genes found in cluster 9 shown at each timepoint, with individual gene expression colored by their mean SD from the mean. (H) Top 10 major Reactome pathways most enriched in cluster 9, using hypergeometric pathway analysis. (I) Mean gene expression (black) as well as individual gene expression of all genes found in cluster 10 shown at each timepoint, with individual gene expression colored by their mean SD from the mean. (J) Top 10 major Reactome pathways most enriched in cluster 10, using hypergeometric pathway analysis.

To further explore temporal gene regulation in the glomeruli, the mean expression profiles of DEGs were grouped into 10 clusters using hierarchical clustering (Figures 5B and S2). The clusters were then utilized in an unbiased pathway enrichment analysis of top‐level MSigDB Reactome pathways to reveal distinct temporal patterns of regulation (Figures 5C–J and S3; Files S1 and S2). 41 , 42 Clusters 2 and 8–10 were selected for further investigation as they represented the largest clusters (n = 1284, 694, 840, and 900 DEGs, respectively) with well‐defined temporal patterns of gene regulation and relevance to glomerulosclerosis pathology, including extracellular matrix (ECM) remodeling and metabolic and signaling regulation. An overview of cluster‐wise gene expression was plotted across time (Figure 5C,E,G,I), with the most significant top‐level Reactome pathways highlighted (Figure 5D,F,H,J). The selected clusters indicated significant dynamics in the glomerular global transcriptome during disease progression in db/db UNx‐ReninAAV mice.

Cluster 2 was characterized by progressive decreases in gene expression, with predominant perturbations associated with cell cycle control (Figure 5C,D). Cluster 8 was enriched with genes showing delayed onset of upregulation at week 8, involving pathways associated with ECM organization, hemostasis, and the innate immune system (Figure 5E).

Cluster 9 contained genes showing early and sustained upregulation of gene expression, with most prominent changes observed from weeks 1 to 4 (Figure 5G). Changes were dominated by gene expression associated with lipid, vitamin, and nucleotide metabolism, as well as the innate immune system (Figure 5H).

Cluster 10 was characterized by biphasic gene regulations, initially showing a significant and progressive increase in expression during weeks 1–8 followed by a decline at week 12. Most prominent changes in gene expression were associated with ECM remodeling, amino acid metabolism, and the innate immune system (Figure 5I,J).

Due to the prominent perturbations in ECM organization pathways, genes in clusters 8 and 10 were subclustered using hierarchical clustering to further define their temporal signature (Figure 6A). This yielded four subclusters, of which subclusters 2 and 3 were selected for further analysis given their temporal resemblance to clusters 8 and 10 (Figure 6B,D). A pathway enrichment analysis of the subclusters revealed that subcluster 2 exhibited a biphasic gene regulation peaking at week 8 (Figure 6B) and contained pathways related to ECM formation (Figure 6C, integrin–cell surface interactions, molecules associated with elastic fibers, elastic fiber formation, syndecan interactions, and nonintegrin membrane ECM Interactions). Subcluster 3 exhibited a delayed onset of upregulation starting at week 8 (Figure 6D) and largely contained pathways associated with ECM degradation (Figure 6E).

FIGURE 6.

FIGURE 6

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Subclustering of clusters 8 and 10 reveals a late regenerative ECM (extracellular matrix) response in the db/db UNx‐ReninAAV mouse model of DKD. (A) Hierarchical clustering of the gene expression from clusters 8 and 10 at a given timepoint using the Ward.D2 method. Four clusters were formed. (B) Mean gene expression (black) as well as individual gene expression of all genes found in subcluster 2 shown at each timepoint, with individual gene expressions colored by their mean SD (standard deviation) from the mean. (C) Top 5 Reactome pathways most enriched in subcluster 2, using hypergeometric pathway analysis. (D) Mean gene expression (black) as well as individual gene expression of all genes found in subcluster 3 shown at each timepoint, with individual gene expression colored by their mean SD from the mean. (E) Top 5 Reactome pathways most enriched in subcluster 3, using hypergeometric pathway analysis.

Hierarchical clustering revealed temporally distinct dynamics of cell cycle, metabolic, immune, and ECM pathways in db/db UNx‐ReninAAV mice. Particularly, a transition in ECM remodeling at week 8 coincided with peak levels of albuminuria and glomerulosclerosis.

3.3. db/db UNx‐ReninAAV mice exhibit temporal changes in glomerular cell–type markers

Glomerular cell dysfunction is integrally connected to DKD progression. In db/db UNx‐ReninAAV mice, selected glomerular cell–type markers of ECs, podocytes, mesangial cells, and parietal epithelial cells were assessed in addition to markers of glomerular injury (Figure 6A). Glomerular ECs are essential for the correct maintenance of the glomerular filtration barrier, and endothelial dysfunction is linked to renal decline in DKD. 16 , 43 , 44 Most glomerular EC‐type markers (Figure 7A,B) were significantly downregulated in db/db UNx‐ReninAAV mice compared to healthy db/m mice. Emcn, Kdr, and Ehd3 exhibited sustained downregulation from weeks 1, 4, and 8, respectively, whereas Tmem204 expression was significantly reduced at week 8 only.

FIGURE 7.

FIGURE 7

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Temporal changes in gene expression of glomerular cell–type markers and genes related to glomerular injury are evident in the db/db UNx‐ReninAAV mouse model of DKD. (A) Log2‐fold change in gene expression of glomerular cell–type markers and genes related to glomerular injury, comparing all timepoints to healthy db/m mice. Rows are unscaled. Also shown are p‐values representing directionality and significance of regulation at all timepoints. (B) Expression level of important or temporally regulated cell type and injury markers (Kdr, Emcn, Podxl, Nphs1, Cldn5, Tagln, Pdgfra, Pax8, Mme, Ccl2, Ctsd, Tgfb1). All genes are shown as mean RPKM (reads per kilobase million) ± SEM. a: p < 0.05 compared to db/m mice, b: p < 0.05 compared to week 1. n = 5–8.

Some of the earliest DKD‐related changes have been observed in podocytes, as dysfunctional podocytes lead to degradation of the slit diaphragm, podocyte detachments, and apoptosis. 16 , 17 , 45 Podocyte markers exhibited heterogeneous regulation, with both significant upregulation (Tspan2, Plod2, Nes, Sulf1, Mgat5) and downregulation (Podxl, St3gal6, Clic5, Tcf21, Wt1, Crb2, Myo1d, Nphs1, Ddn, Plce1, Mpp5, Cldn5, Ptpro, Thsd7a) in db/db UNx‐ReninAAV mice. The classical podocyte marker, Podxl, was significantly downregulated from week 4 onward, whereas Cldn5 and Nphs1 progressively declined in expression, with maximal suppression being observed at weeks 8 and 12, respectively (Figure 7B).

Mesangial cells are important drivers of glomerulosclerosis in DKD through excessive expansion of the mesangial matrix. 20 , 21 In the glomeruli of db/db UNx‐ReninAAV mice, mesangial markers were consistently upregulated (Tagln, Mmp9, Sept4, Itga8). Similarly, Tagln exhibited consistently strong, progressive upregulation over the entire course of DKD in the model (Figure 7B). Among the mesangial cell markers analyzed, only Pdgfra exhibited significantly downregulated expression, observed at both weeks 4 and 8 (Figure 7B).

Parietal epithelial cells play a critical role in maintaining fluid homeostasis within the glomeruli, and their dysfunction is associated with cellular hypertrophy and vacuolization. 22 , 23 , 46 We detected widespread regulation of parietal epithelial cell markers, both upregulation (Tacstd2, Rbfox1, Cldn1, Myo5b, Aldh1a2, Pax8, Arhgef28, Cfh) and downregulation (Mme, Epn3, Ncam1) in db/db UNx‐ReninAAV mice (Figure 7B). Different temporal dynamics were apparent; for example, Pax8 exhibited progressive upregulation peaking at week 8, whereas Mme was significantly downregulated from week 4 onward.

A subset of glomerular injury markers associated with inflammatory responses, tissue remodeling, and glomerulosclerosis (Ccl2, Pdgfa, Ctsd, Tgfb1, Adam10, Actn4, Adam17) were significantly upregulated in db/db UNx‐ReninAAV mice. Ccl2 and Ctsd were upregulated compared to healthy db/m mice at weeks 1 and 4, respectively (Figure 7B). Tgfb1 was significantly upregulated only at weeks 4 and 12 (Figure 7B).

These findings underscore a progressively worsening glomerular disease phenotype in db/db UNx‐ReninAAV mice, characterized by early and sustained downregulation of endothelial markers, heterogeneous regulation of podocyte‐associated genes, and pronounced abnormalities in mesangial and parietal epithelial cells. Moreover, the upregulation of key injury markers further supports the accumulation of glomerular damage over time in the model.

4. DISCUSSION

In this study, we comprehensively profiled changes in biochemical, histological, and glomerular molecular changes during the progression of kidney disease in the db/db UNx‐ReninAAV mouse, a translational model of hypertension‐accelerated DKD. 24 , 26 , 27 , 47 Our findings demonstrate that db/db UNx‐ReninAAV mice exhibit key clinical, histological, and molecular hallmarks of human DKD. Particularly, progressive kidney disease in db/db UNx‐ReninAAV mice was reflected by extensive alterations in the glomerular transcriptome. Collectively, this work provides a valuable resource for uncovering molecular drivers of DKD progression and for guiding future therapeutic strategies.

The db/db UNx‐ReninAAV mouse model exhibited progressively worsening hyperglycemia in the context of overt obesity, emphasizing a robust type 2 diabetic and metabolic disease phenotype. Advancing obesity and hyperglycemia co‐occurred with histological and biochemical hallmarks of DKD, including glomerulosclerosis and albuminuria, making this model relevant in preclinical studies of DKD progression with a particular focus on glomerular molecular pathology.

Although the db/db UNx‐ReninAAV mice exhibited pronounced glomerular fibrotic injury, as evidenced by advanced glomerulosclerosis, tubulointerstitial fibrosis remained mild in the model, consistent with previous observations. 24 , 47 The lack of pronounced tubulointerstitial fibrosis indicates that the model does not fully recapitulate DKD‐associated tubulointerstitial pathology and may instead represent a predominantly hypertension‐driven phenotype in the hyperfiltration state. 24 , 48 While the db/db UNx‐ReninAAV model primarily reflects glomerular pathology, it cannot be excluded that more severe tubulointerstitial fibrosis may develop with prolonged disease duration. However, increased mortality during prolonged studies, driven by severe hypertension and progressive diabetes, restricts both study duration and the development of significant tubulointerstitial fibrosis in this model. 24 The observed robust features of glomerulosclerosis, a key pathological hallmark of human DKD, in db/db UNx‐ReninAAV mice can be complemented by the use of other models with more pronounced tubulointerstitial fibrosis.

Our longitudinal glomerular transcriptomics analysis revealed a progressively increasing number of DEGs during the first 8 weeks of the study period, closely mirroring worsening glomerular pathology in the db/db UNx‐ReninAAV mouse. Clustering of gene expression profiles revealed distinct temporal dynamics, including perturbations in pathways associated with cell cycle control, ECM organization (dynamic shifts in markers of fibrogenesis and fibrolysis), and lipid and amino acid metabolism. Previous studies have shown that impaired cell cycle control in DKD leads to podocyte mitotic catastrophe, resulting in podocyte death, and increased proliferation of mesangial and glomerular ECs, culminating in filtration barrier injury and enhanced glomerular fibrosis. 49 , 50 , 51 Furthermore, we observed significant changes in glomerular metabolism, marked by enrichment of lipid metabolic pathways. This suggests metabolic dysregulation and potential lipotoxicity from the early stages of DKD in db/db UNx‐ReninAAV mice. In agreement, lipotoxicity can lead to excess mitochondrial dysfunction, oxidative and endoplasmic stress, and dysregulation of autophagy mechanisms in human DKD. 52 , 53 Enrichment of amino acid metabolism pathways suggests additional metabolic alterations as these changes coincided with advanced glomerular injury. Consistent with this, dysregulated glomerular amino acid metabolism, particularly within podocytes, has been linked to glomerular damage in both hypertensive and DKD. 54 , 55

Opposing expression patterns were observed among ECM pathways. Fibrogenesis‐related pathways (integrin–cell surface interactions, elastic fiber formation, and syndecan interactions) initially increased and then decreased. This shift was accompanied by increased enrichment of fibrolytic signaling pathways (activation of matrix metalloproteinases, collagen degradation, and degradation of the extracellular matrix). These changes suggest dynamic shifts in ECM remodeling activity, with transition from initially enhanced collagen deposition to enhanced ECM turnover. The timing of ECM remodeling coincided with progressive glomerulosclerosis and albuminuria, whereas kidney fibrosis, quantified by Col1a1 and PSR histology, did not worsen. This may suggest subsequent activation of glomerular fibrolytic processes and/or enhanced collagen clearance. Renal fibrosis is the most pronounced pathological feature of DKD, and experimental evidence indicates that excessive or dysregulated regenerative responses may drive ECM accumulation over time, for example, by inducing inflammatory stress. 56 , 57 , 58 , 59

The glomerular molecular cell‐type markers revealed temporal changes in the db/db UNx‐ReninAAV mouse, being overall consistent with previously identified complex molecular mechanisms of DKD, further supporting the validity of the LCM‐RNAseq analysis applied in the current study. For example, the observed decrease in endothelial markers, including the vascular endothelial growth factor (VEGF) receptor Kdr and the VEGF modulator Emcn, suggests VEGF dysregulation and endothelial dysfunction, which can lead to increases in renal fibrosis and deteriorated glomerular filtration barrier function. 43 , 60 Interestingly, podocyte markers exhibited heterogeneous regulation over the course of the disease. Regulations in gene expression markers included early and sustained downregulation of Podxl, Nphs1, and Cldn5, which could be indicative of podocyte injury, hinting at degradation of the glomerular filtration barrier. 45 , 61 , 62 Several mesangial cell markers (e.g., Tagln, Mmp9, Sept4, and Itga8) were consistently upregulated, potentially indicating activation and proliferation of mesangial cells as well as reflecting dynamics in ECM remodeling.

Gene expression markers of parietal epithelial cells also exhibited heterogeneous regulation. Although the role of parietal epithelial cells has been less characterized in DKD, our findings of widespread regulation of the parietal epithelial cell markers suggest a significant role of this glomerular cell type in DKD pathogenesis. Particularly, we observed increased expression of Pax8 and Mme, which has also been reported in humans and is known to accompany podocyte loss, though the association with DKD is not fully understood. 63 , 64 Persistent activation of the parietal epithelial cells has also been associated with glomerular lesions and fibrosis. 22 , 46 Interestingly, the temporal expression pattern of Pax8 mirrored the ECM pathway dynamics. Increased Pax8 expression has previously been reported in glomeruli from human patients with DKD and associated with tubular injury in human acute kidney injury and chronic kidney disease. 28 , 29 , 63

The temporally specific expression patterns of glomerular injury markers indicated increasing Ccl2 and Ctsd expression concurrent with worsening of glomerulosclerosis. In human DKD, these markers correlate with disease severity. 65 , 66 The transient upregulation of Tgfb1 is also significant due to the critical role of transforming growth factor‐beta (TGFβ) signaling in renal fibrogenesis and DKD progression. 67 , 68 Collectively, these markers support the critical role of inflammation and fibrosis in the pathogenesis of DKD.

The dynamic shifts in pathway and marker regulations may inform the design of studies using the db/db UNx‐ReninAAV mouse model, particularly in optimizing the timing of therapeutic interventions. For example, targeting metabolic dysregulation holds potential for therapeutic efficacy across multiple stages of renal disease in the model. In contrast, antifibrotic strategies are likely best evaluated in the context of early or prophylactic treatment, due to the rapid pro‐fibrotic responses in the model. Additionally, the observed temporal dynamics may support the development of precision medicine, enabling treatment strategies tailored to the patient's disease stage and phenotype.

This study has limitations. Healthy db/m control mice were terminated at study week 12 and thus did not cover all timepoints assessed in db/db UNx‐ReninAAV mice. Although a fully age‐matched comparison between db/m and db/db UNx‐ReninAAV mice could improve the sensitivity of our analyses, recent studies indicate that db/m mice exhibit minimal changes in glomerular physiology and transcriptomics within the age span examined here. 69 , 70 Only female mice were included in the analysis, which may obscur sex‐related differences in disease progression that remain uncharacterized in the db/db UNx‐ReninAAV mouse model. Moreover, our RNA‐seq analysis was limited to the glomeruli and was not complemented by proteomic or functional validation. Additionally, incorporating spatial transcriptomics could offer valuable insights into spatial and cell type–specific dynamics, proving further molecular resolution of disease progression in the db/db UNx‐ReninAAV mouse.

In conclusion, this longitudinal study of kidney biomarkers, histology, and glomerular transcriptome signatures during disease progression in the db/db UNx‐ReninAAV mouse model provides important insights into the temporal dynamics of DKD. Collectively, our findings underscore the utility of this model in preclinical research and may support the development of glomerular‐targeted therapies for DKD.

AUTHOR CONTRIBUTIONS

Adam B. Marstrand‐Jørgensen: Conceptualization; data curation; formal analysis; methodology; visualization; writing – original draft; writing – review and editing. Frederikke Emilie Sembach: Conceptualization; investigation; supervision; writing – original draft; writing – review and editing. Maria Ougaard: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing. Ditte Hansen: Supervision; writing – review and editing. Mette Viberg Østergaard: Conceptualization; methodology; writing – original draft; writing – review and editing. Henrik H. Hansen: Conceptualization; writing – original draft; writing – review and editing. Louise S. Dalbøge: Conceptualization; supervision; writing – original draft; writing – review and editing. Ole Jørgen Kaasbøll: Conceptualization; writing – original draft; writing – review and editing. Michael Christensen: Conceptualization; writing – original draft; writing – review and editing.

FUNDING INFORMATION

This research received funding from the Innovation Fund Denmark (grant number 2040‐00034B).

CONFLICT OF INTEREST STATEMENT

Adam B. Marstrand‐Jørgensen, Frederikke Emilie Sembach, Maria Ougaard, Henrik H. Hansen, Louise S. Dalbøge, and Michael Christensen are employed by Gubra and shareholders in Gubra. Mette Viberg Østergaard was employed by Gubra and Tribune Therapeutics and is presently employed by Novo Nordisk A/S. Ole Jørgen Kaasbøll is presently employed at and is a shareholder of Tribune Therapeutics AS. No other potential conflicts of interest were reported.

ETHICS STATEMENT

The Danish Animal Inspectorate approved all experiments (License #2013‐15‐2934‐00843). All animal experiments were conducted in agreement with internal Gubra bioethical guidelines, being fully compliant with internationally accepted principles for the care and use of laboratory animals and accredited by AAALAC.

Supporting information

File S1.

AME2-8-2115-s001.csv (54.4KB, csv)

File S2.

AME2-8-2115-s007.csv (83.1KB, csv)

Figure S1.

AME2-8-2115-s005.png (582.6KB, png)

Figure S2.

AME2-8-2115-s004.png (990.7KB, png)

Figure S3.

AME2-8-2115-s002.png (7.4MB, png)

Table S1.

AME2-8-2115-s003.docx (15KB, docx)

Captions.

AME2-8-2115-s006.docx (13KB, docx)

ACKNOWLEDGMENTS

The authors have nothing to report.

Marstrand‐Jørgensen AB, Sembach FE, Ougaard M, et al. Temporal glomerular gene expression dynamics during disease progression in a mouse model of hypertension‐accelerated diabetic kidney disease. Anim Models Exp Med. 2025;8:2115‐2127. doi: 10.1002/ame2.70094

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are not publicly available due to samples obtained from a research program where commercial restrictions may apply but are available from the corresponding author upon reasonable request.

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Associated Data

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

Supplementary Materials

File S1.

AME2-8-2115-s001.csv (54.4KB, csv)

File S2.

AME2-8-2115-s007.csv (83.1KB, csv)

Figure S1.

AME2-8-2115-s005.png (582.6KB, png)

Figure S2.

AME2-8-2115-s004.png (990.7KB, png)

Figure S3.

AME2-8-2115-s002.png (7.4MB, png)

Table S1.

AME2-8-2115-s003.docx (15KB, docx)

Captions.

AME2-8-2115-s006.docx (13KB, docx)

Data Availability Statement

The data that support the findings of this study are not publicly available due to samples obtained from a research program where commercial restrictions may apply but are available from the corresponding author upon reasonable request.

Articles from Animal Models and Experimental Medicine are provided here courtesy of Wiley

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