1. Cell Biology

The insulin/IGF axis is critically important for controlling gene transcription in the podocyte

  1. Jenny A Hurcombe
  2. Lusyan Dayalan
  3. Fern Barrington
  4. Frederic Burdet
  5. Lan Ni
  6. Joseph Talih Coward
  7. Mark Ibberson
  8. Paul T Brinkkoetter
  9. Martin Holzenberger
  10. Aaron R Jeffries
  11. Sebastian Oltean
  12. Gavin I Welsh
  13. Richard JM Coward  Is a corresponding author
  1. Bristol Renal, University of Bristol, United Kingdom
  2. Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Switzerland
  3. Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Germany
  4. Center of Excellence in Nephrology, University Hospitals Aachen, Cologne, Dusseldorf, University of Cologne, Germany
  5. Department II of Internal Medicine and Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, University Hospital Cologne, Germany
  6. INEM, Paris Cité University, France
  7. Biosciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom
  8. Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, United Kingdom
https://doi.org/10.7554/eLife.107791.5
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Cite this article
  1. Jenny A Hurcombe
  2. Lusyan Dayalan
  3. Fern Barrington
  4. Frederic Burdet
  5. Lan Ni
  6. Joseph Talih Coward
  7. Mark Ibberson
  8. Paul T Brinkkoetter
  9. Martin Holzenberger
  10. Aaron R Jeffries
  11. Sebastian Oltean
  12. Gavin I Welsh
  13. Richard JM Coward
(2026)
The insulin/IGF axis is critically important for controlling gene transcription in the podocyte
eLife 14:RP107791.
https://doi.org/10.7554/eLife.107791.5
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eLife Assessment

This valuable study used genetic and pharmacological manipulations of insulin/IGF signaling to address the role of insulin/IGF axis in the function of renal glomerular podocyte. Solid data are presented to demonstrate that co-inhibition of insulin/IGF signaling in podocytes led to aberrant splicing of mRNAs, which could contribute to the loss of podocytes in vitro and in vivo in mice. In light of the fact that IR/IGF-1R signaling are critically required for normal development and growth in multiple cells and organs, the lack of the assessment of developmental phenotype of podocytes in the mouse model limits the interpretation of the data.

[Editors' note: this paper was reviewed by Review Commons.]

https://doi.org/10.7554/eLife.107791.5.sa0

Abstract

Podocyte integrity depends critically on signalling through the insulin receptor and IGF1 receptor, and this study defines their combined importance using dual-receptor knockdown in mice and cultured podocytes. Podocyte-specific reduction of both receptors in transgenic mice caused kidney disease characterised by albuminuria and glomerulosclerosis, with premature death occurring in some animals between 4 and 24 weeks. Receptor-deficient cultured podocytes exhibited >50% cell loss within 7 days. Integrated proteomic and transcriptomic analyses revealed marked depletion of spliceosome-associated proteins and widespread intron retention with premature termination codons, indicating profound disruption of RNA processing. Phospho-proteomic profiling further showed that insulin/IGF1 stimulation induces dynamic post-translational modifications across spliceosomal components and regulatory kinases. Together, these findings uncover a previously unrecognised role for podocyte insulin/IGF1 signalling in maintaining spliceosomal integrity and transcriptional fidelity, establishing this hormonal axis as a key extrinsic regulator of podocyte gene expression.

Introduction

Podocytes are highly specialised, terminally differentiated epithelial cells located at the urinary aspect of the glomerular basement membrane in the kidney. They are crucial in maintaining glomerular filtration barrier integrity thus preventing the development of albuminuria, an independent risk factor for end-stage kidney disease (Tonelli et al., 2006), cardiovascular morbidity (Perkovic et al., 2008), and death. Therefore, understanding the critical signalling pathways necessary for maintaining podocyte health is desirable.

The insulin/insulin-like growth factor signalling axis regulates many aspects of growth and metabolism: insulin signalling is predominantly associated with metabolism and IGF signalling with growth and cell division (Nakae et al., 2001). However, insulin, IGF1, and their cognate receptors (the insulin receptor [IR] and insulin-like growth factor receptor 1 [IGF1R]) share significant structural homology and several common downstream signalling nodes (Belfiore et al., 2009). The IR and IGF1R can bind each other’s ligands and can act redundantly in some contexts (Garwood et al., 2015; Weinstein et al., 2014). The constitutive global (whole body) loss of either the IR or IGF1R in murine knockout models is lethal (Accili et al., 1996; Holzenberger et al., 2003), suggesting a limited ability for these receptors to compensate for each other in every cell type. However, the insulin/IGF axis has different roles in different cell types. Several mouse models with tissue-specific dual knockout of both the IR and IGF1R show differential consequences occurring in a cell type-dependent manner (Boucher et al., 2016; Li et al., 2016; O’Neill et al., 2015; Soto et al., 2019; Neirijnck et al., 2018).

Insulin signalling to the podocyte via the IR is critical for podocyte function (Coward et al., 2005; Ising et al., 2015; Madhusudhan et al., 2015; Welsh et al., 2010) and it is apparent that IGF signalling is also important for podocyte homeostasis (Bridgewater et al., 2008; Bridgewater et al., 2005; Hale et al., 2013). Recently, we have shown that loss of podocyte IGF1 signalling can have both detrimental and beneficial effects dependent upon the level of IGF1R suppression: whilst near total loss of podocyte IGF1R expression results in severe mitochondrial dysfunction associated with cell death, partial receptor inhibition promotes podocyte survival in conditions of cellular stress (Hurcombe et al., 2024).

Here, we show that combined, simultaneous loss of both the IR and IGF1R in podocytes is physiologically highly detrimental and present proteomic and transcriptomic analyses, indicating that the IR/IGF1R hormonal signalling axis is crucial in controlling spliceosome assembly and RNA processing in this cell.

Results

podDKD mice develop a severe renal phenotype by 24 weeks

To study the effect of the combined loss of podocyte IR and IGF1R in vivo, transgenic mice were generated by crossing Insrfl/fl;Igf1rfl/fl mice with mice expressing Cre recombinase under the control of a podocin promoter (Koehler et al., 2017) to generate podocin Cre recombinase heterozygous Insrfl/fl;Igf1rfl/fl mice (podDKD) (Figure 1—figure supplement 1A). There was no difference in body weight or blood glucose in podDKD mice compared with littermate controls (Figure 1A and B). However, knockdown mice developed albuminuria with significantly increased urinary albumin-to-creatinine ratio (uACR) (p<0.01) at 24 weeks of age (Figure 1C, Figure 1—figure supplement 1B). The severity of the phenotype was variable and while some mice exhibited only mild albuminuria, more severe renal disease was observed in others resulting in death in ~20% of the mice. Of note, we found variable levels of receptor knockdown in the models which we suspect contributed to the variable phenotype observed (Figure 1—figure supplement 1C). We considered using an inducible podocyte-specific Cre driver to eliminate any developmental confounders associated with the constitutive podocin Cre (Koehler et al., 2017) model, but the level of floxed gene excision is less in these systems compared to constitutive Cre-driven models (Figure 1—figure supplement 1D), so we concluded that the podocyte IR and IGF1R levels would be even less ‘knocked down’ using this approach. uACR was not increased in Cre negative and podCre expressing mice at 6 months excluding Cre toxicity as the driver of albuminuria in podDKD mice (Figure 1—figure supplement 1E). Glomerulosclerosis was significantly increased (p<0.05) in podDKD mice with areas of sclerosis and tubular protein casts visible on periodic acid-Schiff (PAS) stained sections (Figure 1D), together with evidence of increased fibrosis as determined by Masson’s trichrome staining (Figure 1E). Transmission electron microscopy analysis revealed disruption of the filtration barrier ultrastructure with significantly increased (p<0.01) foot process width (Figure 1F). A limited number of serum creatinine levels were recorded at 4–6 months and showed no significant difference (Figure 1—figure supplement 1F). Podocyte number, evaluated by immunofluorescence (IF) using a podocyte-specific WT1 antibody, was significantly (p<0.0001) reduced in podDKD mice at 6 months (Figure 1G).

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podDKD mice develop a renal phenotype by 24 weeks.

(A, B) Body weight (A) and blood glucose (B) are not significantly different in podDKD mice at 24 weeks compared with littermate controls (n=7–8 each group). (C) Urinary albumin-to-creatinine ratio (uACR) is significantly increased in podDKD mice at 24 weeks. Unpaired t-test, **p<0.01 (n=7–8 mice per group). (D) Images and quantification of periodic acid-Schiff (PAS) staining show tubular protein casts (indicated by arrows) and glomerulosclerosis in podDKD mice (lower two panels) compared to littermate controls (upper panels). Scale bar = 25 μm. Unpaired t-test *p<0.05. (E) Masson’s trichrome staining shows increased fibrosis (blue staining) in podDKD mice at 24 weeks. Scale bar = 25 μm. (F) Transmission electron microscopy (TEM) images of glomerular filtration barrier (GFB) (scale bar: left panels = 5 μm, right panels = 500 nm) show ultrastructural damage to the GFB in podDKD mice with significantly increased foot process width. Unpaired t-test **p<0.01. (G) Immunofluorescent staining and quantification of WT1 in podDKD and control mice at 24 weeks of age shows significant reduction in % podocytes per glomerulus in podDKD mice. Nuclei counterstained with DAPI. Scale bar = 100 μm. Unpaired t-test ****p<0.0001, ≥8 glomeruli analysed per mouse, 3 mice per group. Scale bar = 100 μm.

Simultaneous knockdown of podocyte IR and IGF1R in vitro is highly detrimental

We surmised that the variable phenotype observed in our transgenic mouse model was predominantly due to differences in the efficiency of Cre-mediated excision and level of receptor knockdown. With the aim of developing a model with near total loss of the IR and IGF1R to use in in vitro studies, a conditionally immortalised podocyte cell line was generated from Insrfl/fl;Igf1rfl/fl mice and these cells subsequently transduced with Cre expressing lentivirus (ciDKD) (Figure 2—figure supplement 1A). This allowed us to initially culture the cells containing both receptors before their simultaneous knockdown using lentiviral delivered Cre recombinase. Western blot analysis showed a highly significant decrease of the IR (p<0.0001) and IGF1R (p<0.0001) in ciDKD podocytes of >80% (Figure 2A). Phosphorylation of AKT and p44/42 MAPK in response to either acute insulin or IGF1 stimulation was reduced (p<0.05), indicating suppression of insulin/IGF1 signalling in ciDKD podocytes (Figure 2B and C). Compound loss of both the IR and IGF1R was detrimental, resulting in >50% podocyte loss (p<0.0001) 7 days after Cre-mediated gene excision (Figure 2D, Figure 2—figure supplement 1B).

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Simultaneous knockout of podocyte insulin receptor (IR) and insulin-like growth factor receptor 1 (IGF1R) in vitro is highly detrimental.

(A) Western blot shows >80% reduction of IR and IGF1R protein in ciDKD cells. t-test, ****p<0.00001, n=18. (B) Phosphorylation of AKT and p44/42MAPK in response to acute insulin stimulation at 10 nM and 100 nM for 10 min was significantly reduced in ciDKD podocytes. One-way ANOVA, **p<0.01, *p<0.05, n=3. (C) Phosphorylation of AKT and p44/42MAPK in response to acute IGF1 stimulation at 10 and 100 ng/ml for 10 min was reduced in ciDKD podocytes. One-way ANOVA, *p<0.05, n=3. (D) Fewer than 50% of ciDKD cells survive 7 days after gene excision. t-test, ****p<0.0001, n=3–4 independent experiments.

Figure 2—source data 1

Uncropped western blots for Figure 2A–C.

https://cdn.elifesciences.org/articles/107791/elife-107791-fig2-data1-v1.zip
Figure 2—source data 2

Uncropped western blots for Figure 2A–C, showing relevant bands.

https://cdn.elifesciences.org/articles/107791/elife-107791-fig2-data2-v1.zip

Proteomic analysis of ciDKD podocytes reveals downregulation of spliceosome proteins

To elucidate the mechanistic processes regulated by insulin/IGF1 signalling in podocytes, we performed unbiased tandem mass tagged (TMT) LC MS/MS proteomics to compare total protein expression in ciDKD compared with wild-type (WT) cells (Figure 3A). We performed this 3 days after Cre exposure when cell death was approximately 20% compared to the 50% cell death observed at 7 days (Figure 2—figure supplement 1B). Hierarchical clustering and principal component analysis (PCA) were performed using Perseus software to determine that samples were clustered according to the experimental group (Figure 3—figure supplement 1A and B). 4842 proteins were significantly differentially expressed in ciDKD cells compared to WT controls with 2127 proteins upregulated and 2715 proteins downregulated (Figure 3A, Figure 3—figure supplement 1C). Perseus software was used to perform hierarchical clustering and identify Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) terms enriched in ciDKD podocytes (Figure 3B). This revealed a downregulation of proteins involved in DNA replication and cell cycling along with a striking downregulation of proteins involved in spliceosome function and RNA processing (Figure 3B). These findings were confirmed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) to identify KEGG terms overrepresented in the ciDKD proteome (Figure 3C). STRING identified the spliceosome as the most significantly enriched KEGG term in dual receptor knockout cells with a log10 false discovery rate ~2-fold greater than any other (Figure 3D).

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Proteomic analysis of ciDKD podocytes reveals downregulation of spliceosomal proteins.

(A) Schematic outlining workflow for proteomic analysis. (B) Heat map showing hierarchical clustering of ciDKD vs wild-type podocyte proteomes (decreased protein expression in green, increased expression in red) and the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) terms enriched in four major clusters. (C, D) KEGG enriched terms in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Downregulated pathways (green) are associated with higher enrichment scores in comparison to upregulated pathways (red). Enrichment scores are computed by STRING using the Kolmogorov-Smirnoff test. KEGG term ‘Spliceosome’ (in black) is associated with a high enrichment score and the highest false discovery rate across all terms. (E) Western blots show significantly reduced levels of EIF4A, SF3B4, and PTBP2 in ciDKD podocytes compared with wild-type cells. Unpaired t-test, ***p<0.001, **p<0.01, *p<0.05, n=3 independent experiments. (F) Representative immunohistochemistry and quantification using an antibody to SF3B4 shows reduced expression in the glomeruli of podDKD mice compared to littermate controls. t-test ****p<0.0001. Scale bar = 25 µm.

Figure 3—source data 1

Uncropped western blots for Figure 3E – ACTIN, EI4A3, PTBP2, SF3B4.

https://cdn.elifesciences.org/articles/107791/elife-107791-fig3-data1-v1.zip
Figure 3—source data 2

Uncropped western blots for Figure 3E – ACTIN, EI4A3, PTBP2, SF3B4, showing relevant bands.

https://cdn.elifesciences.org/articles/107791/elife-107791-fig3-data2-v1.zip

To validate results from the proteomic data, western blot analysis of WT and ciDKD podocytes was performed using antibodies to selected spliceosome proteins. This confirmed that levels of eukaryotic initiation factor-4A (EIF4A) (p<0.001), splicing factor 3B subunit 4 (SF3B4) (p<0.01), and polypyrimidine tract binding protein 2 (PTBP2) (p<0.05) were significantly reduced in knockout cells (Figure 3E). We also validated that proteomic changes occurring in our cell model were present in our animal model. Immunohistochemical glomerular SF3B4 expression was significantly lower in podDKD mice compared to littermate controls (Figure 3F).

Exposure of cultured podocytes, but not glomerular endothelial cells, to the spliceosome inhibitor pladienolide B results in dose-dependent cell death

Proteomic analysis indicated a significant reduction in spliceosome proteins associated with all stages of the splicing process in ciDKD podocytes (Figure 4A). We therefore hypothesised that impaired spliceosome assembly and function may be at least partially responsible for the deleterious phenotype observed with podocyte IR/IGF1R knockdown. To determine the importance of spliceosome activity to mature podocyte homeostasis and to ascertain whether different cell types are equally sensitive to spliceosome inhibition, the cytotoxic effect of pladienolide B (a spliceosome inhibitor targeting SF3B) (Yokoi et al., 2011) was compared in HeLa cells, fully differentiated, WT murine podocytes, and glomerular endothelial cells. Consistent with previous observations (Yokoi et al., 2011), exposure of HeLa cells to pladienolide B at 0–100 nM for 48 hr was cytotoxic at ≥1 nM (Figure 4B). Pladienolide B-induced spliceosome inhibition also resulted in a significant (p<0.05) dose-dependent loss of podocytes at a concentration of ≥25 nM (Figure 4D and E). In contrast, no cytotoxicity was observed in glomerular endothelial cells (Figure 4C). As previously stated, we also found decreased levels of SF3B4 in podocytes from podDKD mice (Figure 3F).

Figure 4
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Exposure of cultured podocytes, but not glomerular endothelial cells, to the spliceosome inhibitor pladienolide B results in dose-dependent cell death.

(A) Schematic overview of the spliceosome pathway showing multiple spliceosome proteins are significantly downregulated (green) in ciDKD podocytes. (B–D) Pladienolide B exposure for 48 hr in HeLa cells (B), glomerular endothelial cells (GenC) (C), and podocytes (D). One-way ANOVA, ****p<0.0001, **p<0.01, *p<0.05, n=3 independent experiments. (E) Bright-field images of wild-type podocytes exposed to the indicated concentrations of pladienolide B. Scale bar = 100 μm.

Loss of podocyte IR and IGF1R is associated with increases in intron retention and unproductive transcript expression

To obtain an overview of global differences in various alternative splicing events in the ciDKD transcriptome, long-read RNA sequencing was used. Long-read sequencing was preferred as short-read RNA sequencing tools have been found to detect such events with poor accuracy (David et al., 2022). RNA was isolated from ciDKD and WT control cells, sequenced using long-read RNA Oxford Nanopore PromethION flow cells (Figure 5A, Figure 5—figure supplement 1A–C), and the resulting data analysed using the full-length alternative isoform analysis of RNA (FLAIR) tool. An overview of the main types of alternative splicing events is depicted in Figure 5B, and rank lists of genes in ciDKD podocytes with significantly increased (adjusted p-value<0.05) intron retention splicing, alternative 5’ splicing, alternative 3’ splicing, and cassette exon splicing are shown in Supplementary files 1–4. KEGG pathway enrichment analysis using STRING shows an overlap in overrepresented pathways (including spliceosome, RNA transport, and ribosome) from rank lists of genes with intron retention, cassette exon splicing, and alternative 5’ splicing (Figure 5—figure supplement 1D–F).

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Loss of podocyte insulin receptor (IR) and insulin-like growth factor receptor 1 (IGF1R) is associated with increases in intron retention and unproductive transcript expression.

(A) Schematic outlining long-read RNA sequencing workflow. (B) An overview of the alternative splicing events quantified by full-length alternative isoform analysis of RNA (FLAIR). Boxes represent exons (blue = constitutive exons; yellow = alternative exons), lines represent introns. (C) Boxplot showing the fraction of transcripts with intron retention events in ciDKD and wild-type podocytes. t-test, **p<0.01, n=4 for each experimental condition. (D) Box plot of the fraction of productivity events in the ciDKD and wild-type transcriptomes shows a higher proportion of unproductive transcripts in ciDKD podocytes. Includes no start codon; start codon but no stop codon; productive transcripts; premature termination codon, i.e., unproductive transcript. t-test, ***p<0.001, **p<0.01, *p<0.05, n=4 for each experimental condition. (E) Proportional stacked bar graph of productivity events in the ciDKD and wild-type transcriptomes.

To compare the intron retention in the ciDKD vs controls, the number of intron retention events by sample was counted using FLAIR mark_intron_retention and then divided by the number of transcripts per sample. The ciDKD samples had a significantly (p-value=0.004) higher proportion of transcripts with intron retention: ~18% of transcripts in ciDKD podocytes compared with 14% in WT controls (Figure 5C). The overlap of the intron retention events between experimental repeats is ~30% in both WT and ciDKD podocytes. The transcript productivity was also assessed using FLAIR predictProductivity with transcripts classified in four categories: PRO (productive), PTC (premature termination codon, i.e. unproductive), NGO (no start codon), and NST (has start codon but no stop codon appended to the end of the isoform name). The fraction of transcripts harbouring a premature termination codon is significantly higher (p<0.001) in ciDKD podocytes (Figure 5D and E).

Compound knockdown of the IR and IGF1R alters the podocyte transcriptome

To determine the effect of spliceosome depletion on the transcriptomic profile of ciDKD podocytes, IsoQuant (Prjibelski et al., 2023) was used to quantify genes and transcripts from existing GENCODE mouse annotation, then limma Law et al., 2016 used to determine differential gene and transcript expression. The number of reads per sample is shown in Figure 5—figure supplement 1A. Variance-stabilised transformed RNA sequencing data (Figure 5—figure supplement 1B) were used in principal component analysis and confirmed that samples were clustered according to the experimental group (Figure 5—figure supplement 1C). The number of minimally expressed transcripts in the experiment was 31,580 and using an adjusted p-value threshold of 0.01, 831 transcripts were significantly upregulated and 570 significantly downregulated in ciDKD podocytes. The top 10 most significantly differentially expressed genes (analysed by transcript) are shown in Table 1. These included downregulation of host cell factor C1 regulator 1 (Hcfc1r1), a nuclear export factor which regulates the activity of the transcription factor host cell factor 1 (Hcfc1) and which analysis using FLAIR identified as subject to increased alternative 3’ splicing in ciDKD podocytes (Supplementary file 3). Also, fibronectin 1 (Fn1) encoding an extracellular matrix protein which exists as different isoforms derived by alternative splicing at three sites: extra domains A and B exons (EDA and EDB) and the type III connecting segment (White and Muro, 2011). Fn1 splice variants were differentially expressed in ciDKD podocytes with increased expression (log fold change of 1.11[=2.16 fold]) of an Fn1 transcript containing the EDA and EDB exons (Figure 6A–C). Quantitative PCR (qPCR) validation confirmed the decrease in Hcfc1r1 expression (Figure 6D) while primers designed to specifically amplify transcripts containing the detrimental fibrosis-associated Fn1 EDB exon also verified that expression of such transcripts is significantly increased (1.8-fold, p<0.001) in ciDKD podocytes when compared with WT control cells (Figure 6E).

Figure 6
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Compound knockdown of the insulin receptor (IR) and insulin-like growth factor receptor 1 (IGF1R) alters the podocyte transcriptome.

(A) UCSC genome browser tracks of transcripts annotated to Fn1. Red lines indicate the number of reads in each sample containing the EDA and EDB exons (n=4 each group). (B) Boxplot showing differential expression of an Fn1 transcript containing EDA/EDB exons in ciDKD podocytes. (C) Exon/intron structure of Fn1 transcript differentially expressed in ciDKD vs wild-type podocytes. Expression of an EDA and EDB exon-containing transcript (not normally expressed in mature podocytes and associated with fibrosis) is increased in ciDKD podocytes. (D) Quantitative PCR (qPCR) shows decreased expression of Hcfc1r1 mRNA in ciDKD podocytes. t-test, ****p<0.00001, n=4 independent experiments. (E) qPCR using primers specific to the Fn1 EDB exon shows an increase in the expression of fibrosis-associated EDB exon-containing transcripts in ciDKD podocytes. t-test, **p<0.001, n=4 independent experiments.

Table 1
Top 10 most significantly expressed genes detected by transcript in ciDKD v WT podocytes.
Gene nameTranscript IDLog FCValueadj.P.Va1Transcript type
D130062J10RikENSMUST00000223763.2–3.81963610.00000000.0000085IncRNA
Fn1ENSMUST00000186879.2–6.96257490.00000000.0000010Retained intron
Fn1ENSMUST00000055226.131.10977110.00603830.0509845Protein coding
Fn1ENSMUST00000189821.7–0.87373980.03599240.1538286Protein coding
Fn1ENSMUST00000185408.72.62149480.16209500.3828405Retained intron
Fn1ENSMUST00000190780.7–0.47417400.20932010.4430398Protein coding
Fn1ENSMUST00000187938.70.36811710.36337590.6025781Protein coding
Fn1ENSMUST00000186129.7–0.15912730.52480950.7369268Protein coding
Fn1ENSMUST00000189160.20.11490730.75234220.8668309Retained intron
Fn1ENSMUST00000188894.70.05456390.86974790.9320835Protein coding
Gm13196ENSMUST00000118297.27.06826610.00000000.0000043Processed pseudogene
Gm43461ENSMUST00000202351.2–2.55329910.00000000.0000127TEC
Gsta4ENSMUST00000034903.72.84740400.00000000.0000079Protein coding
Gsta4ENSMUST00000213215.20.28102440.62157510.7841542Protein coding
Hcfc1r1ENSMUST00000179928.2–7.53177030.00000000.0000079Protein coding
Hcfc1r1ENSMUST00000180140.81.77278650.00984950.0692495Protein coding
Hcfc1r1ENSMUST00000024697.50.41340730.01718690.0973040Protein coding
Id3ENSMUST00000008016.32.68388640.00000000.0000079Protein coding
Lamtor3-psENSMUST00000164408.86.70703880.00000000.0000098Protein coding
MarcksENSMUST000000092584.65.87638650.00000000.0000001Protein coding
Rpl15-ps2ENSMUST000000184247.213.81358210.00000000.0000001Processed pseudogene

Multiple spliceosomal protein PTMs occur in response to insulin and IGF1 stimulation

As our data demonstrated the spliceosome complex was disrupted in ciDKD podocytes, we used phosphoproteomics to establish if there were any insulin- or IGF1-stimulated post-translational modifications (PTMs) in our cells. WT and ciDKD cells were studied 3 days after Cre application (Figure 2—figure supplement 1B). Cells were fasted of insulin and IGF1 for 4 hr and then stimulated with either 10 nM insulin or 10 ng/ml IGF1 for 10 min before performing TMT-based phosphoproteomics. PTM phosphorylation events were filtered for spliceosomal and associated proteins by cross-referencing with the 199 proteins found in R-MMU-72203 reactome pathway database (mouse processing of capped intron-containing pre-mRNA) (Supplementary file 5). 94 of these proteins appeared in our dataset. We then determined those PTMs that significantly changed between the receptor replete (WT) and deplete cells (ciDKD) at a p-value<0.005 in response to insulin or IGF1 stimulation. As there was little difference when stimulating cells with either insulin or IGF1, we combined the data. 70 statistically significant phosphorylation events were detected in 27 spliceosomal proteins (Figure 7A). A recent study by Turewicz et al., 2025, has comprehensively examined the dynamic phosphorylation status of human myotubes exposed to 100 nM of insulin from 1 and 60 min. It detected 49 insulin-stimulated spliceosomal phosphorylation events and demonstrated that 100 nM insulin rapidly altered the mRNA splicing pattern of these cells. We detected the same phosphorylation events in four of the proteins in our dataset. Splicing factor 3A subunit 1 serine 329 (SF3A1[S329]) was present in both datasets; serine/arginine-rich splicing factor 2 serine 189 and 191 (SRSF2[S189 S191]) was altered in our data and SRSF2[S191] in Turewicz’s; serine/arginine-rich splicing factor 1 serine 199 (SRSF1[S199]) appeared in our data and SRSF1[S199 S201 S205] in Turewicz’s. Finally, we detected a phosphorylation event(s) in heterogeneous nuclear ribonucleoprotein D (HNRNPD) located between amino acids 68–85 (Figure 7A). In Turewicz’s data, HNRNPD was phosphorylated at S80 and S83 in response to a high dose of insulin. Spliceosomal proteins throughout the complex were modified in response to insulin and IGF1 (Figure 7B). Some proteins exhibited multiple phosphorylation effects, including serine/arginine repetitive matrix protein 2 (SRRM2), which was modified extensively with (Robinson et al., 2010) different PTM detected (Figure 7C).

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Multiple spliceosomal protein post-translational modifications (PTMs) occur in response to insulin and IGF1 stimulation.

(A) Heat map showing differential phosphorylation at the indicated phosphosites of spliceosome-related proteins in ciDKD podocytes and control cells stimulated with 10 nM insulin or 10 ng/ml IGF1 for 10 min (decreased phosphorylation in green, increased phosphorylation in red). Those phosphorylation events that also occurred in Turewicz et al., 2025, study highlighted in yellow boxes. (B) Differentially phosphorylated proteins identified in (A) occur throughout the spliceosome cycle (red boxes). (C) Heat map showing differential phosphorylation at the indicated phosphosites of serine/arginine repetitive matrix 2 (SRRM2) in ciDKD podocytes and control cells stimulated with 10 nM insulin or 10 ng/ml IGF1 for 10 min (decreased phosphorylation in green, increased phosphorylation in red).

Considering this, we went back to our phosphoproteomic dataset and investigated if any kinases known to phosphorylate splicing factors were modulated by insulin/IGF1 and found that cyclin-dependent kinase 11 (CDK11[S578]) and serine/arginine-rich splicing factor protein kinase 1 (SRPK1) [S311] were. Additionally, we discovered that protein kinase C and casein kinase substrate in neurones 3 PACSIN3 (S383) phosphorylation were significantly reduced in ciDKD cells (Figure 7—figure supplement 1A–C). PACSIN3 is a casein kinase 2 (CK2) substrate.

Relative contributions of the IR and IGF1R to spliceosomal modulation

To investigate the relative contributions of the IR and IGF1R to spliceosome control, we also generated conditionally immortalised podocyte cell single-receptor knockdown lines lacking either the IR (ciIRKD) or the IGF1R (ciIGF1RKD) (Figure 8—figure supplement 1A and B) in isolation and performed total and phosphoproteomic analysis on these and compared to the ciDKD cells. For total proteome analysis, Perseus software was used to compare the four podocyte cell lines (WT, ciIRKD; ciIGF1RKD and ciDKD). Principal component analysis determined that samples were clustered according to experimental group (Figure 8—figure supplement 1C–E) and a heat map with hierarchical clustering of proteins was subsequently generated to compare the four experimental groups (Figure 8A). The spliceosome targets identified from proteomic analysis of WT vs ciDKD podocytes, and which were validated by western blotting (Figure 3E), were mapped on to this new heat map (Figure 8A). This revealed that while EIF4A3 and PTBP2 downregulation were restricted to ciDKD and ciIGF1RKD podocytes, reduced SF3B4 expression occurred in all three knockout cell lines. When compared to WT cells, SF3B4 expression was lowest in ciDKD podocytes (fold change –0.56) while in ciIGF1RKD and ciIRKD the fold change was –0.28 and –0.34, respectively. Functional enrichment analysis comparing the four cell lines was performed and identified enrichment of the GO term ‘spliceosomal tri-snRNP complex assembly’ exclusively in the ciDKO podocytes (Figure 8B).

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Relative contributions of the insulin receptor (IR) and insulin-like growth factor receptor 1 (IGF1R) to spliceosomal modulation.

(A) Heat map of hierarchical clustering of ciDKD vs ciIGF1RKD vs ciIRKD vs wild-type podocytes (n=9) proteomes (high relative expression in red, low relative expression in green, equal expression in black). Top of the heat map contains most significantly enriched groups, with enrichment intensity decreasing further down. The spliceosomal targets SF3B4, EIF4A3, and PTBP2 were mapped onto the proteomic heat map comparing wild-type podocytes to cells with knockdown of the IR and/or the IGF1R. SF3B4 expression is reduced in all three knockdown cell lines. (B) Schematic showing signalling pathways uniquely enriched in ciDKD podocytes, including the downregulation of spliceosomal tri-snRNP complex assembly. (C) Heat map showing differential phosphorylation events in ciIGF1RKD, ciDKD, ciIRKD, and wild-type podocytes (decreased phosphorylation in green, increased phosphorylation in red).

To assess the relative contribution of each receptor to spliceosomal PTM modulation, we re-analysed our phosphoproteomic datasets comparing ciDKD cells with single receptor knockout lines stimulated with either 10 nM insulin or 10 ng/ml IGF1 for 10 min. We found that 49/70 PTM had signal in all cell lines studied (Figure 8C). There was no consistent pattern of a single receptor being more dominant in this analysis. Some modifications were predominant through the IR (DDX42(S185), SRSF11(S462), SRRM2(S1400)), some through the IGF1R (SRRM2 (S222, S349, S351, S570 S572 T574, S778, S870 S1825, T1428 S1434, S1438, S1439, S2370, S2679, S2681), CASC3 (S145), HTATSF1 (S441)), but most were partially abrogated through both. Collectively, this data suggests a complex network of insulin and IGF signalling via both receptors modulating the spliceosome complex.

Discussion

We have previously shown that signalling to the podocyte via the IR is critical for kidney function (Welsh et al., 2010) while the IGF1R has a role in maintaining podocyte mitochondrial function (Hurcombe et al., 2024). The current study used in vivo and in vitro models lacking both receptors simultaneously to further elucidate important common signalling pathways regulated by the insulin/IGF1 axis in the podocyte.

We initially used a transgenic mouse model (podDKD) to determine the physiological consequences of dual receptor loss in vivo. These mice developed an obvious renal phenotype, with significant albuminuria, glomerulosclerosis, and ultrastructural filtration barrier damage, all apparent by 6 months and was severe enough to cause death in several mice. However, body weight and blood glucose level in podDKD mice were comparable to those in controls, in contrast to several other mouse models, where alongside tissue-specific manifestations, double receptor knockout also gives rise to systemic effects. For example, IR/IGF1R knockout in kidney proximal tubule cells (Li et al., 2016) and muscle (O’Neill et al., 2015) results in decreased body size while other models have impairments in systemic metabolism: mice with adipose tissue-specific IR/IGF1R knockout develop diabetes (Boucher et al., 2016) while dual receptor knockout in hippocampus and central amygdala results in a decrease in glutamate receptors and reduced glucose tolerance (Soto et al., 2019). Our models showed a variable phenotype which we think was predominantly due to the variable levels of receptor knockdown. We suspect that if the IR and IGF1R knockdown had been >80% for both receptors in these mice, a significant embryological/neonatal renal phenotype would have been observed, as the podocin Cre driver becomes active from approximately day 12 of development. For this reason, we subsequently generated podocyte cell lines in which we could reproducibly knock down the receptors by more than 80% to explore the underlying mechanism of disease.

We performed unbiased proteomic analysis to identify processes responsible for the detrimental phenotype observed in our models. This revealed major changes in the spliceosome complex. The spliceosome is a large ribonucleoprotein complex whose critical function in all eukaryotic cells is to catalyse the removal of introns from precursor messenger RNA molecules and join the flanking exons to generate mature mRNA transcripts that direct protein synthesis during translation (Matera and Wang, 2014). Spliceosome function is also a driver of significant proteomic diversity via alternative splicing, whereby multiple mRNA transcripts containing different exon combinations are produced from a single gene (Matera and Wang, 2014). Mutations in core spliceosome components, spliceosome assembly factors, or regulatory proteins can result in defects in pre-RNA processing, increased nonsense-mediated decay of affected transcripts, abnormal or truncated protein products, and changes to alternative splicing. Such erroneous splicing can substantially alter the transcriptome in specific cell types. This has been shown in immune cell dysfunction (Yang et al., 2022), cancer (Yang et al., 2022), and neurodegeneration (Li et al., 2021). Somatic mutations in genes encoding spliceosome components can result in diseases, known as spliceosomopathies (including retinitis pigmentosa, myelodysplastic syndromes, and craniofacial disorders; Griffin and Saint-Jeannet, 2020), with tissue-specific manifestations, suggesting that although spliceosome factors are ubiquitously expressed, they likely have cell-type-specific functions. Interestingly, one of the splice factors frequently involved in these spliceosomopathies – SF3B4 – was downregulated in the double knockdown podocytes (Figure 3E) and in our murine model (Figure 3F). We used the spliceosome inhibitor pladienolide B (Zhang et al., 2019), which targets the SF3B complex, to investigate the effect of reduced spliceosome activity in WT podocytes. Developed as an anti-neoplastic agent, pladienolide B is effective at reducing cell viability, particularly in cancer cell lines (Zhang et al., 2019; Jorge et al., 2020; Suda et al., 2017; Zhang et al., 2020), presumably due to an increased demand for translation and protein production in such actively dividing cells. In contrast, a comparatively high dose of pladienolide B is not sufficient to induce cytotoxicity in HEK 293T cells, suggesting that not all cell types are equally sensitive to spliceosome inhibition (Kim Guisbert et al., 2020). While we were able to reproduce the low-dose cytotoxic effect of this spliceosome inhibitor in HeLa cells reported previously (Zhang et al., 2019), the response of the glomerular cell types we assayed differed. No cytotoxic effect was apparent in glomerular endothelial cells, while in contrast, we found podocytes to be sensitive to pladienolide B, resulting in significant cell death and suggestive of the importance of adequate spliceosome function in this cell type. We therefore performed long-read RNA sequencing to explore the global effect of podocyte IR/IGF1R loss on the transcriptome of ciDKD cells.

Long-read RNA sequencing analysis showed that podocyte IR/IGF1R depletion had a marked impact on multiple alternative splicing events, notably an increase in the fraction of transcripts with intron retention/premature termination codons. Interestingly, enrichment analysis showed that many transcripts associated with spliceosome function and RNA transport had increased levels of intron retention, cassette exon splicing, and alternative 5’ and 3’ splicing. Although it has recently been recognised that intron retention can serve as a mechanism for regulating gene expression (Middleton et al., 2017), including the regulation of RNA processing genes (Pimentel et al., 2016), our results could also be explained by the apparent loss of the spliceosome in ciDKD podocytes. Indeed, intron retention secondary to mutations in spliceosome components is a hallmark of a number of cancers (Dvinge and Bradley, 2015) and neurodegenerative conditions (Li et al., 2021). Moreover, it is likely that our RNA sequence analysis underestimates the incidence of transcripts with premature termination codons as many will be targeted for nonsense-mediated decay (Braunschweig et al., 2014) and would thus be unmeasurable in our samples.

Significant differential transcript expression was apparent in ciDKD podocytes with transcripts encoding the extracellular matrix protein fibronectin among the top hits. We detected increased expression of an Fn1 splice variant that included the EDA and EDB exons. Although EDA/EDB-fibronectin is important during development, its expression is limited in adult tissue, including kidney (Laitinen et al., 1991). However, EDA-fibronectin is upregulated in cultured podocytes exposed to TGFβ treatment (Madne and Dockrell, 2018), and a pathological increase in this isoform results in insulin resistance (Gupta et al., 2022), together with fibrosis in multiple tissues, including lung (Muro et al., 1998) and kidney (Vliet et al., 2001). It is conceivable that the changes in podocyte splicing resulting from IR/IGF1R knockdown promote a fibrotic phenotype by altering the ratio of fibronectin variants expressed, consistent with the increased fibrosis observed in our podDKD mice. However, it is likely many detrimental biological processes are initiated through spliceosomal dysregulation.

Our phosphoproteomic data suggests that there are multiple insulin and IGF1-mediated signalling events in a variety of spliceosomal proteins that cause PTMs. This is supported by Turewicz’s recent work (Turewicz et al., 2025), which built on induced pluripotent stem cell myoblast studies by Batista et al., 2020, showing that multiple spliceosomal PTM events occur in myotubes stimulated with 100 nM insulin at a variety of time points, ranging from 1 to 60 min. 100 nM insulin will signal through both the IR and IGF1R, as well as insulin/IGF1R hybrid receptors (Belfiore et al., 2009), which is relevant to our study. We discovered that some proteins appeared to have multiple insulin/IGF1-induced PTMs, most notably SRRM2 with 30 changes detected. SRRM2 is a large protein thought to act as a scaffold protein for nuclear speckles. Nuclear speckles function as reservoirs of splicing factors, coordinating their release into the nucleoplasm to regulate transcription (Galganski et al., 2017). It has previously been shown that SRRM2 phosphorylation status is key for its spliceosomal function with nearly 80 phosphorylation sites identified (Zhang et al., 2025). Due to these multiple phosphorylation events, we looked for evidence that kinases known to phosphorylate spliceosome proteins were modified in response to insulin/IGF1 and found three kinases were differentially phosphorylated (CDK11[S578], SRPK[S311], and PACSIN3[S383]). Interestingly PACSIN3 is a substrate for casein kinase 2 (CK2), which has recently been shown to be a key kinase in regulating SRRM2 (Zhang et al., 2025).

We found a predictable downregulation of cell cycle and DNA replication pathways given the established mitogenic effect of IGF1 signalling (Kim et al., 2001). However, we also observed a decrease in DNA damage response pathways and histone methylation. As post-mitotic cells with a limited capacity for replenishment, maintaining genomic integrity is crucial for podocyte health (Braun et al., 2020; Hishikawa et al., 2019). Furthermore, altered histone methylation, another feature of the podocyte IR/IGF1R knockdown proteome, has been shown to affect the function of the DNA repair system in podocytes (Hayashi et al., 2019). Recent studies suggest that podocyte DNA damage and changes in glomerular DNA methylation may be useful markers of chronic kidney disease and correlate with estimated glomerular filtration rate decline in patients with diabetic nephropathy (Yoshimoto et al., 2023). It is therefore plausible that deficiencies in DNA damage repair mechanisms also contribute to the detrimental phenotype and ultimate loss of podocytes resulting from IR/IGF1R knockdown.

Evidence from previous studies suggests that DNA damage is intrinsically linked with spliceosome function: DNA damage affects the expression, activity, and localisation of splicing factors (Shkreta and Chabot, 2015), while spliceosome dysfunction leads to DNA damage by promoting the aberrant formation of R loops during transcription (Tam and Stirling, 2019; Chen et al., 2018). Thus, accurate spliceosome function is critical for maintaining genomic integrity (Tam and Stirling, 2019). In addition to the decreased expression of DNA damage response proteins revealed by our proteomic data, we observed a striking reduction in the levels of proteins required for spliceosome assembly and regulation in ciDKD podocytes. We have previously used lentiviral-mediated expression of Cre recombinase to induce knockout of other podocyte genes, notably glycogen synthase kinase 3 α and β in combination (Hurcombe et al., 2019). While this resulted in a highly detrimental cellular phenotype, proteomic analysis of these cells showed no differences in the expression of spliceosome-related proteins, so we are confident that the results from the current study are not due to generalised podocyte cell death/dysfunction.

Finally, we explored the relative contribution of the IR and IGF1R to spliceosomal dysfunction. From a signalling perspective, we discovered that there were some spliceosome protein PTMs predominantly induced through the insulin or IGF1 receptor. However, many PTMs required the loss of both receptors to be phosphorylated or dephosphorylated. This suggests a complex signalling cascade that has evolved to compensate for a single receptor loss. Our total proteomic analysis suggested perhaps a predominance of the IGF1 receptor for spliceosome function, but pathway enrichment analysis revealed tri-snRNP complex assembly, a key process in the spliceosome cycle, required for the formation of the pre-catalytic spliceosome complex B, was only downregulated when both receptors were depleted.

There are several limitations to our studies: Our mouse model showed variability which we think was predominantly driven by the variable level of receptor knockdown but may have also been the result of studying mice on a mixed genetic background (as we consider this more relevant to human disease). We could have studied a single background (or backgrounds) to explore this. Furthermore, we could have investigated the early effects of partial receptor knockdown in the embryonic/neonatal phase in the mice. We also considered an inducible Cre system to study IR/IGF1R loss in fully developed adult podocytes, but these systems result in much less gene excision, as shown by our reporter studies, and would likely have had a very mild or no phenotype.

For our in vitro work we only examined a single time point (10 min) and single dose of insulin and IGF1 in our phosphoprotein omics studies. A more comprehensive dose-time course of insulin/IGF1 phosphorylation changes may reveal more detail of the signalling pathways induced through the IR and IGF1R in isolation and together. The doses of insulin and IGF1 used are likely not specific for their cognate receptor and the kinetics of phosphorylation events probably spans for minutes to hours in this system as shown in the study of insulin in myotubes (Batista et al., 2020). It may also be the case that some of the PTMs we discovered are the result of evolving damage to the spliceosome, which could have been induced or exacerbated by the putative DNA damage revealed by our proteomic analysis. Future work will include a comprehensive time and dose-response analysis of WT and receptor deficient cells to more precisely delineate the important signalling pathways in this process. Additionally, this may identify targets for genetic manipulation, which can rescue the phenotype.

In summary, we have shown that the IR/IGF1R axis is critical for podocyte health. Mechanistically, this axis controls gene transcription by maintaining spliceosome function. This suggests that hormonal signals are important for the transcriptional homeostasis of these terminally differentiated cells.

Methods

Mouse models

Mice in which exon four of the IR (Accili et al., 1996) and exon three of the IGF1R (Holzenberger et al., 2003) have been flanked by loxP sites were crossed with podCre mice (Koehler et al., 2017). Progeny with the genotype PodCre/+;Insrwt/fl;Igf1rwt/fl was then crossed with Insrfl/fl;Igf1rfl/fl mice to generate podocyte-specific Insr;Igf1r knockdown animals from embryonic day 12 (podDKD) (Figure 1—figure supplement 1A). Littermates served as controls. All mice were on a mixed genetic background, including contributions from 129/Sv, FVB, and C57BL/6. Mice were studied up to 24 weeks of age. Both sexes were studied, and no phenotypic differences observed. Transgenic mouse work was carried out in accordance with the University of Bristol’s institutional guidelines, and procedures approved by the United Kingdom (UK) Home Office in accordance with the UK legislation (Home Office Protocol numbers PPL 3003394 and PPL 2012285) and ARRIVE guidelines.

Urinary albumin and creatinine measurements

Albumin and creatinine levels in spot collections of mouse urine were measured using a mouse-specific albumin ELISA (Universal Biologicals, Cat# E90-134) and creatinine companion kit (Exocell, Cat# 1012), following the manufacturer’s methodology.

Histology and PAS staining

Kidneys were fixed in 10% buffered neutral formalin, further processed, and paraffin embedded. 3 μm sections were cut and stained using a PAS staining kit (Sigma, Cat# 395B) according to the manufacturer’s instructions. Tissues were imaged using a Leica DN2000 microscope and micrographs taken using Leica Application Suite software (here; RRID:SCR_013673). Image analysis was performed using ImageJ (here; RRID:SCR_013673); all images were contrast-enhanced using the same parameters. Glomerulosclerosis was scored for each glomerulus as follows: 0=normal glomeruli; 1=up to 25% involvement; 2=25–50% involvement; 3=50–75% involvement; 4=over 75% involvement. The glomerulosclerosis index was calculated as described previously (Maric et al., 2004) and according to the formula [(1×N1)+(2×N2)+(3×N3)+(4×N4)]/(N0+N1+N2+N3+N4), where Nx is the number of glomeruli with each given score. Sections were assessed and scored blindly.

Immunofluorescence

Frozen kidneys were sectioned at 5 μm. Sections were blocked in phosphate-buffered saline (PBS) containing 3% bovine serum albumin (BSA) and 0.3% Triton X-100 for 1 hr, an antibody to Wilms Tumour Protein 1 (Abcam, Cat# ab89901 RRID:AB_2043201). Following three PBS rinses, sections were incubated with a fluorophore-conjugated secondary antibody (Thermo Fisher Scientific, Cat# A11008 RRID:AB_143164) for 1 hr at room temperature. Tissues were imaged using a Leica DM2000 microscope and micrographs taken with Leica Application Suite X software (here; RRID:SCR_013673). Image analysis was performed with ImageJ (https://imagej.nih.gov/ij/download.html RRID:SCR_003070); all images were contrast-enhanced using the same parameters.

Immunohistochemistry

Tissues were fixed in 10% buffered neutral formalin (Merck, Cat# HT501128), processed, and paraffin-embedded. 3 μm sections were deparaffinised in Histo-Clear II (National Diagnostics, Cat# HS-202) and rehydrated through a graded alcohol series. Antigen retrieval was in 10 mM citrate buffer, pH 6, by boiling for 10 min. Sections were quenched using 3% H2O2, followed by a blocking step in 3% normal goat serum for 30–45 min. Sections were incubated overnight at 4°C with an SF3B4 antibody (Novus Biologicals, Cat# NBP-9269255) diluted 1:100. Sections were washed and then incubated with SignalStain Boost detection reagent (Cell Signaling Technology, Cat# 8114) for 30 min at room temperature. SignalStain DAB substrate kit (Cell Signaling Technology, Cat# 8059) was applied for 1–2 min and the sections dehydrated and mounted in DPX (Sigma, Cat# 06522). Tissues were imaged using a Leica DM2000 microscope and micrographs taken with Leica Application Suite X software (here; RRID:SCR_013673). Image analysis was performed using ImageJ (https://imagej.nih.gov/ij/download.html RRID:SCR_003070); all images were contrast-enhanced using the same parameters.

Electron microscopy

Tissues for electron microscopy were fixed in 0.1 M sodium cacodylate, 2% glutaraldehyde, and imaged on a Technai 12 transmission electron microscope. Average slit diaphragm, foot process, and glomerular basement membrane width were calculated using ImageJ, assessing at least 20 regions of glomerular basement membrane from at least two glomeruli per mouse.

Lentiviral transduction of conditionally immortalised IR and/or IGF1R floxed podocyte cell lines

Kidneys were isolated from Insrfl/fl;Igf1rfl/fl, Insrfl/fl, and Igf1rfl/fl mice and used to make homozygous floxed temperature-sensitive SV40 conditionally immortalised podocyte cell lines: double receptor knockdown (ciDKD), insulin receptor knockdown (ciIRKD), and IGF1 receptor knockdown (ciIGF1RKD) cells (Figure 2—figure supplement 1A), using techniques as described previously (Keir et al., 2015). Podocyte markers, including nephrin, podocin, synaptopodin, and CD2AP, were present in the cell lines shown by western blotting (Keir et al., 2015), and the cells were tested and free of mycoplasma infection.

Podocytes were cultured at 33°C and when 50% confluent were thermo-switched to 37°C and incubated for a further 7 days before transduction with a lentivirus expressing Cre recombinase as described previously (Hurcombe et al., 2024). Transduction was in RPMI media with hexadimethrine bromide (Merck, Cat# H9268) at 4 μg/ml and the virus used at a multiplicity of infection of 1. Following a 24 hr incubation, the lentivirus was removed and replaced with fresh media. Cells were incubated for a further 3–7 days before imaging and protein extraction. To determine cell number, cells were washed three times in PBS, the nuclei stained with Hoechst 33342 (Thermo Fisher Scientific, Cat# H3570) at 1 μg/ml and imaged using an IN Cell Analyser 2200 (GE Healthcare), and data analysed using IN Cell Analyzer Developer software (GE Healthcare).

Cell culture

Conditionally immortalised podocyte cell lines were cultured as described previously (Saleem et al., 2002). For acute insulin and IGF1 stimulation, cells were serum-starved for 4 hr, then 10 nM and 100 nM of insulin (Biotechne, Cat# 3435) or 10 ng/ml and 100 ng/ml IGF1 (Novus, Cat# NBP2-35081) was applied to the cells for 10 min. Spliceosome inhibitor treatment was carried out using pladienolide B (Cambridge Biosciences, Cat#16538) at 0–100 nM for 48 hr before determination of cell number.

Western blotting

Cultured cells were lysed in radioimmunoprecipitation assay (RIPA) buffer supplemented with protease (Merck, Cat# P8340) and phosphatase inhibitors (Merck, Cat# P5726 and P0044). 10–30 μg of protein was resolved by electrophoresis and then transferred to a polyvinylidene difluoride membrane (Millipore). Membranes were blocked in Tris-buffered saline with 0.1% Tween 20 and 5% BSA for 1 hr and then incubated overnight with primary antibody at a dilution of 1:1000. Primary antibodies used were from Cell Signalling Technology unless otherwise stated: IRβ Cat# 3025 RRID:AB_2280448; IGF1R Cat# 9750 RRID:AB_10950969; pAKT Cat#4060 RRID:AB_2315049; AKT Cat# 2920 RRID:AB_1147620; P-p44/42 MAPK Cat#4370 RRID:AB_2315112; P44/42 MAPK Cat# 9102 RRID:AB_330744; EIF42A (Proteintech, Cat#17564-1-AP); SF3B4 (Novus Biologicals cat#NBP-9269255); PTBP2 (Proteintech Cat#55186-1-AP RRID:AB_10837230); β-actin clone AC-74 (Sigma Cat# A5316 RRID:AB_476743); GAPDH (Sigma, Cat# G8795 RRID:AB_1078991). Membranes were washed before incubation with the appropriate horseradish peroxidase-conjugated secondary antibody (anti-rabbit IgG peroxidase antibody, Sigma, Cat# A6667 RRID:AB_258307; anti-mouse IgG peroxidase antibody, Sigma, Cat# A9044 RRID:AB_258431). Immunoreactive bands were visualised using Clarity ECL western blotting substrate (Bio-Rad, Cat# 1705061) on a GE AI600 imager. Densitometry was performed using ImageJ software (https://imagej.nih.gov/ij/download.html RRID:SCR_003070).

Proteomic analysis

ciDKD, ciIRKD, and ciIGF1RKD podocytes (3 days after Cre lentiviral transduction and before major cell loss) (Figure 2—figure supplement 1B) were lysed in RIPA buffer and subjected to LC-MS/MS using isobaric TMT labelling as described previously (Hurcombe et al., 2019; Lay et al., 2020). n=9 knockdown and control podocytes were analysed. WT mouse podocytes served as controls.

The raw data files for the total proteome analysis were processed and quantified using Proteome Discoverer software v2.1 (Thermo Fisher Scientific RRID:SCR_014477) and searched against the UniProt Mouse database (http://www.uniprot.org/ RRID:SCR_002380 downloaded October 2019: 83152 entries) using the SEQUEST algorithm. Peptide precursor mass tolerance was set at 10 ppm, and MS/MS tolerance was set at 0.6 Da. Search criteria included oxidation of methionine (+15.9949) as a variable modification and carbamidomethylation of cysteine (+57.0214) and the addition of the TMT mass tag (+229.163) to peptide N-termini and lysine as fixed modifications. The data output from Proteome Discoverer 2.1 was further analysed in Microsoft Office Excel (https://www.microsoft.com/en-gb/ RRID:SCR_016137), GraphPad Prism 9 (GraphPad software, San Diego, CA, USA, RRID:SCR_002798) and Perseus 1.6.10.43 (https://maxquant.net/perseus/ RRID:SCR_015753) using log2-transformed scaled total protein abundance data. A t-test was used to determine significantly differentially expressed proteins. Hierarchical clustering was performed in Perseus by Euclidean distance using k-means pre-processing. GO and KEGG (http://www.kegg.jp/ RRID:SCR_012773), and enrichments were performed in Perseus using a Fisher test of significantly changed proteins vs the unchanged population after annotation with the proteins.

For STRING (http://string.embl.de/ RRID:SCR_005223) enrichment analysis, the ciDKD expression dataset was submitted to STRING with log2 fold change values associated with each protein. Functional enrichments were identified, and enrichment scores calculated based on aggregate fold change or Kolmogorov-Smirnov testing in STRING.

Phosphoproteomic analysis

ciDKD, ciIRKD, ciIGF1RKD cells were generated as described above. The phosphoproteome was assessed 3 days after initiating receptor knockdown with lentiviral Cre. WT (receptor sufficient conditionally immortalised murine podocytes) were also studied in the same manner. Cells were starved of insulin and IGF1 for 4 hr and then stimulated with either 10 nM insulin or 10 ng/ml IGF1 for 10 min. The raw data files for the phosphoproteome analyses were processed and quantified using Proteome Discoverer software v2.1 (Thermo Fisher Scientific RRID:SCR_014477) and searched against the UniProt Mouse database (http://www.uniprot.org/ RRID:SCR_002380) using the SEQUEST algorithm. Peptide precursor mass tolerance was set at 10 ppm, and MS/MS tolerance was set at 0.6 Da. Search criteria included oxidation of methionine (+15.9949) as a variable modification and carbamidomethylation of cysteine (+57.0214) and the addition of the TMT mass tag (+229.163) to peptide N-termini and lysine as fixed modifications. For the phosphoproteome analysis, phosphorylation of serine, threonine, and tyrosine (+79.966) was also included as a variable modification. Searches were performed with full tryptic digestion and a maximum of two missed cleavages were allowed. The reverse database search option was enabled and all data was filtered to satisfy false discovery rate of 5%. The data output from the Proteome Discoverer 2.1 analysis was processed in the following ways. The annotation of master proteins was improved using an in-house R script which integrates Uniprot Annotation levels into the selection of master proteins. Scaled protein abundance data were log2 transformed to bring them closer to a normal distribution. Phosphopeptide abundance data were calculated by summing the abundances of identical phosphorylated PSM sequences into a single phosphopeptide abundance value. The Proteome Discoverer software normalises the data for the Total Proteome analysis such that the total peptide abundance for each sample is the same. These normalisation factors were then applied to the raw phosphopeptide abundances for each sample (generated above). Each phosphopeptide was then further normalised to the observed abundance of its corresponding protein, such that any changes observed reflected changes in protein phosphorylation rather than in protein abundance. To identify differentially expressed proteins and phosphopeptides, a paired t-test was conducted between conditions of interest with a cut-off of p<0.05.

Long-read RNA sequencing

RNA was isolated from ciDKD podocytes (3 days after Cre lentiviral transduction) and WT control cells (n=4 for each group) using a RNeasy Mini Kit (QIAGEN Cat# 74104). RNA was quantified using a Qubit Fluorimeter (Thermo Fisher Scientific) and quality-assessed using the TapeStation system (Agilent). All samples had an RNA integrity number >8. Barcoded PCR-cDNA library preparation was performed on 75 ng of RNA from each sample using the SQK-PCB109 kit (Oxford Nanopore Technologies). The resulting library was quantified using a Qubit Fluorimeter (Thermo Fisher Scientific) and molecular weight estimated using the TapeStation system (Agilent). 50 fmol of library was then loaded onto three PromethION R9.4.1 flow cells (Oxford Nanopore Technologies), sequenced for 72 hr and basecalled using guppy_basecaller 5.0.17 (Oxford Nanopore Technologies) in high accuracy mode.

Long-read RNA analysis

IsoQuant analysis: the reads were aligned using IsoQuant (version 2.3.0), with option --data_type nanopore. The mouse gencode annotation (V29) (Frankish et al., 2021) was used. The IsoQuant reference-based transcripts counts were used for further analysis. Differential expression analysis between the two conditions was performed using limma function with voom approach from limma Bioconductor package (Law et al., 2016). The transcripts were analyzed without being grouped by genes, which will lead to a more stringent analysis than if done by gene (multiple correction testing is done by transcript). For visualization in the genome browser, the bam files were converted to bigWig files (using a scaleFactor calculated with edgeR Robinson et al., 2010, http://bioconductor.org/packages/edgeR/ RRID:SCR_12802). FLAIR analysis (flair version 1.6.4): pychopper (version 2.7.2) (Tang et al., 2020) was first run on the total reads. Then the full length and rescued reads were aligned with ‘flair align’ on the reference fasta file. The alignments were corrected using ‘flair correct’. The alignments were then collapsed using ‘flair collapse’, using the eight replicates, into one complete reference. Then flair quantify, diffExp, and diffSplice (http://www.netlab.uky.edu/p/bioinfo/DiffSplice RRID:SCR_013215) were run using the 2×4 replicates to get the rank lists of the 4 splicing event types (cassette, 3', 5', IR). A custom R script was used to find the nearest gene to the splicing events. In parallel, flair mark_intron_retention and predictProductivity (option --longestORF) were run on each corrected alignments to list the intron retention events by sample and predict the transcripts productivity by sample.

Quantitative PCR

cDNA was synthesised from RNA isolated from ciDKD and WT podocytes using the High-Capacity RNA-to-cDNA kit (Thermo Fisher Scientific Cat# 4368814). qPCR was performed using SYBR Green JumpStart Taq ReadyMix (Merck, Cat# S9194) in a StepOnePlus PCR machine (Applied Biosystems). Primers with the following sequences were purchased from Life Technologies: Fn1 F 5' CCCAGCTCACTGACCTAAGC 3'; Fn1 R 5' GGAAGAGTTTAGCGGGGTCC 3'; Hcfc1r1 F 5' GCCACCACTGGGGTAACTC 3'; Hcfc1r1 R 5' CTTCGGGAAAAGTCACAGGG 3'; Actb F 5' GACAGGATGCAGAAGGAGATTACT 3'; Actb R 5' TGATCCACATCTGCTGGAAGGT 3'.

Statistical analysis

Statistical analysis was performed in GraphPad Prism 9 and data presented as the mean ± SD unless otherwise stated. Statistical significance was calculated using t-tests to compare two groups or one-way ANOVA to compare more than two groups and taken as p<0.05.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Gene (Mus musculus)InsrGenBankID: 16337
Gene (M. musculus)Igf1rGenBankID: 16001
Cell line (M. musculus)Wild-type podocyteKeir et al., 2015
Cell line (M. musculus)Insrfl/fl/Igf1rfl/flThis paperGeneration of this cell line is described in the Methods section of this paper.
Cell line (M. musculus)Insrfl/flThis paperGeneration of this cell line is described in the Methods section of this paper.
Cell line (M. musculus)Igf1rfl/flThis paperGeneration of this cell line is described in the Methods section of this paper.
AntibodyAnti WT1 (rabbit monoclonal)AbcamCat# ab89901;
RRID:AB_2043201		IF(1:100)
AntibodyAnti SF3B4 (mouse monoclonal)Novus BiologicalsCat# NBP-9269255WB (1:1000)
IHC (1:100)
AntibodyAnti IR β (rabbit monoclonal)Cell SignalingCat# 3025;
RRID:AB_2280448		WB (1:1000)
AntibodyAnti IGF1R β (rabbit monoclonal)Cell SignallingCat# 9750;
RRID:AB_10950969		WB (1:1000)
AntibodyAnti phospho AKT (Ser 473) (rabbit monoclonal)Cell SignalingCat# 4060;
RRID:AB_2315049		WB (1:1000)
AntibodyAnti AKT (rabbit monoclonal)Cell SignalingCat# 2920;
RRID:AB_1147620		WB (1:1000)
AntibodyAnti phospho P44/42 MAPK (rabbit monoclonal)Cell SignalingCat# 4370;
RRID:AB_2315112		WB (1:1000)
AntibodyAnti P44/42 MAPK (rabbit monoclonal)Cell SignalingCat# 9102;
RRID:AB_330744		WB (1:1000)
AntibodyAnti PTBP2 (rabbit polyclonal)ProteintechCat# 55186-1-AP;
RRID:AB_10837230		WB (1:1000)
AntibodyAnti beta actin (mouse monoclonal)Sigma-AldrichClone AC-74
Cat# A5316;
RRID:AB_476743		WB (1:10,000)
AntibodyAnti GAPDH (mouse monoclonal)Sigma-AldrichCat# G8795;
RRID:AB_1078991		WB (1:10,000)
AntibodyGoat anti-Rabbit IgG (H+L)
Cross-Adsorbed Secondary
Antibody Alexa Fluor 488		Thermo Fisher ScientificCat# A11008;
RRID:AB_143165		IF (1:200)
AntibodyGoat anti-Guinea-pig IgG (H+L)
Cross-Adsorbed Secondary
Antibody Alexa Fluor 568		Thermo Fisher ScientificCat# A11075;
RRID:AB_2534119		IF (1:200)
AntibodyAnti-rabbit IgG peroxidase
secondary antibody		Sigma-AldrichCat# A6667;
RRID:AB_258307		WB (1:10,000)
AntibodyAnti-mouse IgG peroxidase
secondary antibody		Sigma-AldrichCat# A9044;
RRID:AB_258431		WB (1:10,000)
Sequence-based reagentFibronectin forward primerThermo Fisher ScientificPCR primersCCCAGCTCACTGACCTAAGC
Sequence-based reagentFibronectin reverse primerThermo Fisher ScientificPCR primersGGAAGAGTTTAGCGGGGTCC
Sequence-based reagentHcfc1r1 forward primerThermo Fisher ScientificPCR primersGCCACCACTGGGGT
AACTC
Sequence-based reagentHcfc1r1 reverse primerThermo Fisher ScientificPCR primersCTTCGGGAAAAGTCACAGGG
Sequence-based reagentBeta actin forward primerThermo Fisher ScientificPCR primersGACAGGATGCAGAAGGAGATTACT
Sequence-based reagentBeta actin reverse primerThermo Fisher ScientificPCR primersTGATCCACATCTGCTGGAAGGT
Peptide, recombinant proteinInsulinBiotechneCat# 3435
Peptide, recombinant proteinRecombinant mouse IGF1NovusCat# NBP2-35081
Commercial assay or kitBethyl mouse albumin ELISA quantitationUniversal BiologicalsCat# E90-134
Commercial assay or kitThe Creatinine CompanionExocellCat# 1012
Commercial assay or kitPeriodic Acid Schiff staining kitSigmaCat# 395B
Commercial assay or kitTrichrome Staining KitSigmaCat# HT15
Chemical compound, drugHexadimethrine bromide
MerckCat# H9268
Software, algorithmImageJNIHRRID:SCR_003070
Software, algorithmLeica Application Suite X softwareLeica MicrosystemsRRID:SCR_013673
Software, algorithmGraphPad PrismGraphPad Software, San Diego, CA, USAVersion 9.4.0
RRID:SCR_002798
Software, algorithmIN Cell InvestigatorGE Healthcare
Software, algorithmProteome Discoverer 2.1Thermo Fisher ScientificRRID:SCR_014477
Software, algorithmMicrosoft Office ExcelRRID:SCR_016137
Software, algorithmPerseus softwareMaxQuantRRID:SCR_015753
Software, algorithmSTRINGhttp://string.embl.de/RRID:SCR_005223
Software, algorithmKEGGhttp://www.kegg.jp/RRID:SCR_012773
Software, algorithmUniProthttp://www.uniprot.org/RRID:SCR_002380
Software, algorithmedgeRhttp://bioconductor.org/packages/edgeR/RRID:SCR_012802
Software, algorithmdiffSplicehttp://www.netlab.uky.edu/p/bioinfo/DiffSpliceRRID:SCR_013215
Software, algorithmProteomeXchangehttp://www.proteomexchange.orgRRID:SCR_004055
OtherHoechst 33342Thermo Fisher
Scientific		Cat# H3570Fluorescent DNA dye.
OtherProtease inhibitor cocktailMerckCat# P8340Supplement for cell lysis buffer.
OtherPhosphatase inhibitor cocktail 2MerckCat# P5726Supplement for cell lysis buffer.
OtherPhosphatase inhibitor cocktail 3MerckCat# P0044Supplement for cell lysis buffer.
OtherHematoxylin Solution, Gill No. 1SigmaCat# GHS132Histological stain.
OtherRIPA bufferFisherCat# 10017003Cell lysis buffer, described in the Methods section.
OtherClarity Western ECL substrateBio-RadCat# 1705061Reagent for visualisation of western blot bands.
OtherSignalStain Boost IHC detection reagent (HRP rabbit)Cell SignalingCat# 8114Secondary antibody used for IHC.
OtherSignalStain DAB substrate kitCell SignalingCat# 8059Reagent for visualisation of IHC staining.
OtherVectaMountVector LaboratoriesCat# H-5000Histological mounting medium.
OtherDPX mount for histologySigmaCat# 06522Histological mounting medium.

Data availability

The authors declare that all data supporting the findings of this study are available within the article and its supplementary files. The mass spectrometry proteomics data have been deposited in the PRIDE repository as part of the ProteomeXchange Consortium (Vizcaíno et al., 2014) under the dataset identifier PXD051018. The raw long-read RNA sequencing data is available on the GEO repository under the dataset identifier GSE345955.

The following data sets were generated
    1. Coward R
    (2024) ProteomeXchange
    ID PXD051018. Insulin and IGF1 receptor function in murine podocytes.
    https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD051018
    1. Burdet F
    (2026) NCBI Gene Expression Omnibus
    ID GSE345955. Long-read (ONT) RNA-seq of Insr/Igf1r double-knockout and wild-type mouse podocytes.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE345955

References

    1. Laitinen L
    2. Vartio T
    3. Virtanen I
    (1991)
    Cellular fibronectins are differentially expressed in human fetal and adult kidney
    Laboratory Investigation; a Journal of Technical Methods and Pathology 64:492–498.

Article and author information

Author details

  1. Jenny A Hurcombe

    Bristol Renal, University of Bristol, Bristol, United Kingdom
    Contribution
    Conceptualization, Data curation, Investigation, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4064-7306

  2. Lusyan Dayalan

    Bristol Renal, University of Bristol, Bristol, United Kingdom
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0821-9149

  3. Fern Barrington

    Bristol Renal, University of Bristol, Bristol, United Kingdom
    Contribution
    Data curation, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7521-7712

  4. Frederic Burdet

    Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
    Contribution
    Data curation, Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2923-827X

  5. Lan Ni

    Bristol Renal, University of Bristol, Bristol, United Kingdom
    Contribution
    Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4733-5081

  6. Joseph Talih Coward

    Bristol Renal, University of Bristol, Bristol, United Kingdom
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0004-5416-3099

  7. Mark Ibberson

    Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
    Contribution
    Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3152-5670

  8. Paul T Brinkkoetter

    1. Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
    2. Center of Excellence in Nephrology, University Hospitals Aachen, Cologne, Dusseldorf, University of Cologne, Cologne, Germany
    3. Department II of Internal Medicine and Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, University Hospital Cologne, Cologne, Germany
    Contribution
    Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4287-2080

  9. Martin Holzenberger

    INEM, Paris Cité University, Paris, France
    Contribution
    Conceptualization, Data curation, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4869-725X

  10. Aaron R Jeffries

    Biosciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
    Contribution
    Data curation, Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1235-8291

  11. Sebastian Oltean

    Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared

  12. Gavin I Welsh

    Bristol Renal, University of Bristol, Bristol, United Kingdom
    Contribution
    Conceptualization, Supervision, Visualization, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2148-6658

  13. Richard JM Coward

    Bristol Renal, University of Bristol, Bristol, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    Richard.Coward@bristol.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6183-2546

Funding

Medical Research Council (MR/K010492/1)

  • Richard JM Coward

HORIZON EUROPE Health

https://doi.org/10.3030/115974
  • Richard JM Coward

Kidney Research UK (RP_011_20230628)

  • Richard JM Coward

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the Proteomic Facility and Histology Services Unit at the University of Bristol for technical support. This study was funded by the Medical Research Council (Senior Clinical Fellowship) to RJMC (MR/K010492/1), Kidney Research UK (RP_011_20230628), and funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 115974. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and JDRF. Any dissemination of results reflects only the author’s view; the JU is not responsible for any use that may be made of the information it contains.

Ethics

Transgenic mouse work was carried out in accordance with the University of Bristol's institutional guidelines, and procedures approved by the United Kingdom (UK) Home Office in accordance with UK legislation. (Home Office Protocol numbers PPL 3003394 & PPL 2012285).

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You can cite all versions using the DOI https://doi.org/10.7554/eLife.107791. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2025, Hurcombe et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Jenny A Hurcombe
  2. Lusyan Dayalan
  3. Fern Barrington
  4. Frederic Burdet
  5. Lan Ni
  6. Joseph Talih Coward
  7. Mark Ibberson
  8. Paul T Brinkkoetter
  9. Martin Holzenberger
  10. Aaron R Jeffries
  11. Sebastian Oltean
  12. Gavin I Welsh
  13. Richard JM Coward
(2026)
The insulin/IGF axis is critically important for controlling gene transcription in the podocyte
eLife 14:RP107791.
https://doi.org/10.7554/eLife.107791.5

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