Multiomic identification of factors associated with progression to cystic kidney disease in mice with nephron Ift88 disruption

Chunyan Hu; Katherine Beebe; Edgar J Hernandez; Jose M Lazaro-Guevara; Monica P Revelo; Yufeng Huang; J Alan Maschek; James E Cox; Donald E Kohan

doi:10.1152/ajprenal.00409.2021

. 2021 Dec 20;322(2):F175–F192. doi: 10.1152/ajprenal.00409.2021

Multiomic identification of factors associated with progression to cystic kidney disease in mice with nephron Ift88 disruption

Chunyan Hu ¹, Katherine Beebe ², Edgar J Hernandez ^3,⁴, Jose M Lazaro-Guevara ^1,³, Monica P Revelo ⁵, Yufeng Huang ¹, J Alan Maschek ⁵, James E Cox ⁶, Donald E Kohan ^1,^✉

PMCID: PMC8782669 PMID: 34927449

graphic file with name f-00409-2021r01.jpg

Keywords: cilia, cysts, nephron, predictor, sex

Abstract

Ift88 gene mutations cause primary cilia loss and polycystic kidney disease (PKD) in mice. Nephron intraflagellar transport protein 88 (Ift88) knockout (KO) at 2 mo postnatal does not affect renal histology at 4 mo postnatal and causes PKD only in males by 11 mo postnatal. To identify factors associated with PKD development, kidneys from 4-mo-old male and female control and Ift88 KO mice underwent transcriptomic, proteomic, Western blot, metabolomic, and lipidomic analyses. mRNAs involved in extracellular matrix (ECM) synthesis and degradation were selectively upregulated in male KO mice. Proteomic analysis was insufficiently sensitive to detect most ECM components, while Western blot analysis paradoxically revealed reduced fibronectin and collagen type I in male KO mice. Only male KO mice had upregulated mRNAs encoding fibrinogen subunits and receptors for vascular endothelial growth factor and platelet-derived growth factor; period 2, period 3, and nuclear receptor subfamily 1 group D member 1 clock mRNAs were selectively decreased in male KO mice. Proteomic, metabolomic, and lipidomic analyses detected a relative (vs. the same-sex control) decrease in factors involved in fatty acid β-oxidation in female KO mice, while increased or unchanged levels in male KO mice, including medium-chain acyl-CoA dehydrogenase, 3-hydroxybutyrate, and acylcarnitine. Three putative mRNA biomarkers of cystogenesis in male Ift88 KO mice (similar control levels between sexes and uniquely altered by KO in males) were identified, including high levels (fibrinogen α-chain and stromal cell-derived factor 2-like 1) and low levels (BTG3-associated nuclear protein) in male KO mice. These findings suggest that relative alterations in renal ECM metabolism, fatty acid β-oxidation, and other pathways precede cystogenesis in Ift88 KO mice. In addition, potential novel biomarkers of cystogenesis in Ift88 KO mice have been identified.

NEW & NOTEWORTHY Male, but not female, mice with nephron intraflagellar transport protein 88 (Ift88) gene knockout (KO) develop polycystic kidneys by ∼1 yr postnatal. We performed multiomic analysis of precystic male and female Ift88 KO and control kidneys. Precystic male Ift88 KO mice exhibited differential alterations (vs. females) in mRNA, proteins, metabolites, and/or lipids associated with renal extracellular matrix metabolism, fatty acid β-oxidation, circadian rhythm, and other pathways. These findings suggest targets for evaluation in the pathogenesis of Ift88 KO polycystic kidneys.

INTRODUCTION

Mutations involving genes encoding proteins critical for primary cilia structure and/or function can cause polycystic kidney disease (PKD) in mice. One such ciliary gene mutation involves intraflagellar transport protein 88 (Ift88, Tg737, OSM-5, or polaris); this mutation has been relatively extensively studied using in vitro and in vivo models (1–4). Mice with global or kidney-specific Ift88 gene targeting develop renal cysts, the timing of which depends on the timing of gene knockout (KO). Induction of Ift88 gene disruption during embryogenesis or in the first few weeks postnatal causes early and severe PKD, whereas induction of Ift88 gene KO beyond 1–2 mo postnatal causes PKD much later in life (5, 6). We recently took advantage of this strategy by inducing nephron-specific Ift88 gene disruption in mice at 2 mo of age (7). Unexpectedly, only male mice developed polycystic kidneys as determined 9 mo after Ift88 KO (11 mo of age) (7). Examination of precystic mice (2 mo after Ift88 KO at 4 mo of age) revealed some distinguishing characteristics about male Ift88 KO mice (compared with male controls), including 1) increased salt-dependent natriuresis, 2) increased urinary nitric oxide (NO) metabolite excretion, 3) reduced arterial pressure that was normalized by NO synthase inhibition, and 4) a doubling of urinary kidney injury molecule-1 (KIM-1) excretion (7). In contrast, female Ift88 KO mice did not differ from female control mice in any of the above parameters. These findings indicate that male Ift88 KO mice are uniquely susceptible to PKD and raise the possibility that factors specific to male Ift88 KO mice may be identified before cyst formation that are responsible for and/or can serve as biomarkers for eventual PKD. While the previous study identified a few such potential factors, a more exhaustive discovery-driven approach was deemed necessary to identify factors uniquely altered in precystic male Ift88 KO mouse kidneys. To this end, in the present study we conducted transcriptomic, proteomic, metabolomic, and lipidomic analyses of noncystic (4 mo of age) male and female Ift88 KO and control kidneys.

METHODS

Animal Care

All animal experiments were conducted with the approval of the University of Utah Animal Care and Use Committee in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

Generation of Inducible Nephron-Specific Ift88 KO Mice

Nephron-specific Ift88 KO mice were generated as previously described (7). Briefly, mice with loxP sites flanking exons 4−6 of the Ift88 gene (Stock 022409, Jackson Labs, B6.129P2-Ift88 < tm1Bky>/J) were bred with C57/BL6 mice containing the Pax8 promoter-reverse tetracycline transactivator (rtTA; conferring nephron-specific targeting) and LC-1 (containing doxycycline/rtTA-inducible Cre recombinase and luciferase) transgenes. All mice were homozygous for the loxP-flanked Ift88 gene and hemizygous for Pax8-rtTA and LC-1 transgenes. Doxycycline (2 mg/mL) was given in 2% sucrose drinking water to 2-mo-old mice for 12 days (Ift88 KO). Littermates of the same genotype and sex, but without doxycycline treatment, were used as controls. All experiments were performed on control and Ift88 KO mice aged 4 mo (2 mo after doxycycline) (1:1 male:female).

Genotyping

PCR was performed on tail DNA using the Ift88 forward 5′- GACCACCTTTTTAGCCTCCTG-3′ and reverse 5′- GAATAGTGGCAATTCTGGCTC-3′ primers, which yielded a 260-bp product from the floxed Ift88 gene and a 220-bp product from the wild-type allele; Pax8-rtTA forward 5′- CCATGTCTAGACTGGACAAGA-3′ and reverse 5′- CATCAATGTATCTTATCATGTCTGG-3′ primers yielded a 600-bp product; and LC-1 forward 5′- TCGCTGCATTACCGGTCGATGC-3′ and reverse 5′- CCATGAGTGAACGAACCTGGTCG-3′ primers yielded a 480-bp product.

Histology

Ift88 KO and control mouse kidneys were fixed in 10% formaldehyde overnight and embedded in paraffin, and 5-μm sections were obtained. Kidney sections underwent hematoxylin and eosin, periodic acid-Schiff, and trichrome staining by the histology core at Associated Regional and University Pathologists Labs (Salt Lake City, UT). Slides were analyzed in a blinded fashion by a renal pathologist, Dr. Patricia Revelo, reviewing three sections per kidney.

Western Blot Analysis

Whole kidneys were weighed and homogenized in ice-cold buffer containing 250 mM sucrose, 50 mmol/L Tris·Cl buffer, 0.1 mmol/L EDTA, 0.1 mmol/L EGTA (pH 7.5), 1% (wt/vol) Na deoxycholic acid, 1% (vol/vol) Nonidet P-40, and 0.1% (vol/vol) SDS with protease and phosphatase inhibitors (Roche, Indianapolis, IN). Total protein concentration was measured by the Lowry assay (Bio-Rad, Hercules, CA). Samples were diluted with the lysis buffer described above, heated at 65°C for 15 min, and stored at −80°C in aliquots. Proteins were separated using a 4–12% bis-Tris minigel (Invitrogen, Carlsbad, CA) and transferred onto a PVDF membrane. Membranes were blocked with 5% nonfat dry milk in Tris-buffered saline with Tween 20 for 1 h at room temperature. Membranes were incubated with primary antibodies overnight at 4°C. After being washed with Tris-buffered saline with Tween 20, membranes were incubated with horseradish peroxidase-conjugated secondary antibodies for 1 h at room temperature.

Primary antibodies were anti-rabbit fibronectin (1:1,000, F3648, Sigma, St. Louis, MO), anti-goat collagen type I (1:500, no. 1310-01, SouthernBiotech, Birmingham, AL), anti-rabbit collagen type IV (1:500, no. 600-401-106-0.5, Rockland, Gilbertsville, PA), anti-rabbit collagen type VI (1:500, ab199720, Abcam, Burlingame, CA), and anti-rabbit GAPDH (1:2,000, no. 2118S, Cell Signaling, Danvers, MA). Secondary horseradish peroxidase-conjugated antibodies were goat anti-rabbit IgG (1:2,000, ab6721, Abcam) and rabbit anti-goat (1:2,000, ab6741, Abcam). Images were obtained and quantified by ImageLab (Bio-Rad). All antibodies were initially tested for linearity by loading 2.5, 5, 10, 20, and 40 µg protein; linear results were obtained for all antibodies between 5 and 40 µg, so 20 µg protein were loaded into each lane for all experiments. Normalization to GAPDH was performed.

Lipid and Metabolite Extraction and Analysis

Tissue lipids and metabolites were extracted from whole kidneys in 1:3 methanol:methyl tert-butyl ether, internal standards [Avanti SPLASH LipidoMix Lot no.12, Cer(d18:1-d7/15:0), 25 µg/mL, Avanti Polar Lipids], fatty acid 16:0-d31 (100 µg/mL, Cayman Chemical, Ann Arbor, MI), d4-succinate (Sigma), and PBS. Samples were homogenized and centrifuged, and the upper phases were collected and evaporated to dryness. The bottom aqueous layer was dried for gas chromatography–mass spectrometry (GC-MS) analysis. Lipid extracts were reconstituted in mobile phase B [isopropanol-acetonitrile-water, 90:9:1 (vol/vol/vol) in 10 mM ammonium formate and 0.1% formic acid]. Concurrently, a process blank sample and a pooled quality control (QC) sample were prepared by taking equal volumes from each sample after final resuspension.

Lipid extracts were separated on a Waters Acquity UPLC CSH C18 column connected to an Agilent 6545 Accurate Mass Q-TOF dual AJS-ESI mass spectrometer. Samples were analyzed in randomized order in both positive and negative ionization modes in separate experiments. Mobile phase A consisted of acetonitrile-water [60:40 (vol/vol)] in 10 mM ammonium formate and 0.1% formic acid. The chromatography gradient for both positive and negative modes started at 15% mobile phase B and then increased sequentially to 30%, 48%, 82%, and 99% mobile phase B. QC samples (n = 8) and blanks (n = 4) were injected throughout the sample queue. Results from LC-MS experiments were collected using an Agilent Mass Hunter (MH) Workstation and analyzed using the software packages MH Qual, MH Quant, and Lipid Annotator (Agilent Technologies). Results from the positive and negative ionization modes from Lipid Annotator were merged based on the class of lipid identified. Only lipids with relative standard deviations of <30% in QC samples were used for data analysis. Additionally, only lipids with background area under the curve counts in process blanks <30% of QC were used for data analysis.

Metabolite extracts were submitted to GC-MS analysis with an Agilent 7200 GC-QTOF and an Agilent 7693 A automatic liquid sampler. Dried samples were suspended in 40 µL of 40 mg/mL O-methoxylamine HCl (no. 155405, MP Biomedical, Irvine, CA) in dry pyridine (PX2012-7, EMD Millipore, Burlington, MA) and incubated for 1 h at 37°C in a sand bath; 25 µL of this solution were added to autosampler vials. N-methyl-N-trimethylsilyltrifluoracetamide (60 µL) with 1% TMCS (TS48913, ThermoFisher, Waltham, MA) was added automatically via the auto sampler and incubated for 30 min at 37°C. After incubation, samples were vortexed, and 1 µL of the prepared sample was injected into the gas chromatograph inlet in the split mode with the inlet temperature held at 250°C. A 10:1 split ratio was used for analysis for most metabolites. Any metabolites that saturated the instrument at the 10:1 split were analyzed at a 50:1 split ratio. The gas chromatograph had an initial temperature of 60°C for 1 min followed by a 10°C/min ramp to 325°C and a hold time of 10 min. A 30-m Agilent Zorbax DB-5MS with 10-m Duraguard capillary column was used for chromatographic separation. Helium was used as the carrier gas at a rate of 1 mL/min. For metabolomics, the Agilent MH Workstation and software packages MH Qual and MH Quant were used. The pooled QC (n = 4) and process blank (n = 3) were injected throughout the sample queue to ensure the reliability of acquired metabolomics data. Metabolites were identified, and their peak area was recorded using MH Quant. Metabolite identity was established using a combination of an in-house metabolite library developed using pure purchased standards, the National Institute of Standards and Technology library, and the Fiehn library.

For both lipid and metabolite analyses, parsed Excel data tables were normalized based on the ratio to internal standards and then to tissue mass and sum before statistical analysis.

Protein Extraction and Analysis

Whole kidneys were homogenized in 8 M urea in 50 mM trienylammonium bicarbonate1:9 (wt/vol). Chromatin was degraded by sonication followed by the addition of benzonase HC (New England Biolabs, Ipswitch, MA) for 30 min at 37°C. The sample was centrifuged at 20,000 g to remove cell debris. A BCA assay was performed to measure protein concentration followed by reduction with 5 mM dithiothreitol and alkylation with 10 mM indole-3-acetic acid. A methanol-chloroform precipitation was performed, and the sample pellet was dried. This was suspended in 100 µL of 50 mM ammonium bicarbonate and digested overnight at 38°C by Tryp/LysC (Promega) at a 1:100 ratio. The digestion was quenched by acidification with 1% formic acid to a pH of 2–3. The peptides were desalted using a Pierce peptide desalting spin column (ThermoFisher Scientific).

Reversed-phase nano-LC/MS/MS was performed on an UltiMate 3000 RSLCnano system (Dionex) with a PharmaFluidics mPAC microchipbased trapping column and a 50-cm equivalent PharmaFluidics m PAC microchip-based column (Pharmafluidics, Ghent, Belgium). Peptide fractions were reconstituted in 100 µL of 0.1% formic acid in water. Five microliters of the samples were injected onto the liquid chromatograph. A gradient of reversed-phase buffers (buffer A: 0.1% formic acid in water; buffer B: 0.1% formic acid in acetonitrile) at a flow rate of 0.5 mL/min was set up. The LC run lasted for 90 min with a starting concentration of 1% buffer B increasing to 28% over the initial 72 min with a further increase in concentration to 50% over 18 min. A final ramp up to 95% took place over 5 min and was held for 10 min before ramping down to 1% buffer B over 2 min and reequilibrating for 10 min. A QExactive HF (ThermoFisher Scientific) coupled to a Flex nanospray source was used with the following settings for MS1: resolution: 120,000; AGC target: 1e⁵; maximum IT: 50 ms; and scan range: 375–1,400 m/z. MS2 settings were as follows: resolution: 60,000; AGC target 1e⁵: maximum IT: 100 ms; and isolation window: 1.2 m/z. Top 15 DDA analysis was performed with NCE set to 32.

Proteome Discoverer (version 2.4) was used for database searching and protein identification. For these samples, the Uniprot database was searched with Mus musculus taxonomy selected. The parameters used for the Mascot searches included no enzyme digest, two missed cleavages, carbamidomethylation of cysteine set as fixed modification, oxidation of methionine, acetylation of the NH₂ terminus, amidation of the COOH terminus, and deamidation of NQ were set as variable modifications, and the maximum allowed mass deviation was set at 11 ppm.

RNA Extraction, Sequencing, and Analysis

Whole kidney RNA was isolated using a PureLink RNA Mini Kit (ThermoFisher) and DNase treated. Total RNA samples (100–500 ng) were hybridized with Ribo-Zero Gold (Illumina, San Diego, CA) to substantially deplete cytoplasmic and mitochondrial rRNA; all RNA samples were processed in parallel. Stranded RNA sequencing (RNA-seq) libraries were prepared using the Illumina TruSeq Stranded Total RNA Library Prep Gold kit (no. 20020598) with TruSeq RNA UD Indexes (no. 20022371). Purified libraries were qualified on an Agilent Technologies 2200 TapeStation using a D1000 ScreenTape assay (nos. 5067-5582 and 5067-5583). The molarity of adapter-modified molecules was defined by quantitative PCR using the Kapa Biosystems Kapa Library Quant Kit (KK4824, Indianapolis, IN). Individual libraries were normalized to 1.30 nM in preparation for Illumina sequence analysis. Sequencing libraries (1.3 nM) were chemically denatured and applied to an Illumina NovaSeq flow cell using the NovaSeq XP chemistry workflow (no. 20021664). Following transfer of the flow cell to an Illumina NovaSeq instrument, a 2 × 51 cycle paired-end sequence run was performed using a NovaSeq S1 reagent Kit (no. 20027465). Sequencing produced 48–74 million reads per sample after removal of duplicates.

The mouse GRCm38 genome and gene feature files were downloaded from Ensembl release 98, and a reference database was created using STAR version 2.7.2c with splice junctions optimized for 50-bp reads (8). Optical duplicates were removed from the paired end FASTQ files using Clumpify v38.34, and reads were trimmed of adapters using Cutadapt 1.16 (9). The trimmed reads were aligned to the reference database using STAR in two pass mode to output a BAM file sorted by coordinates. Mapped reads were assigned to annotated genes using FeatureCounts version 1.6.3 (10). Differentially expressed genes were identified using DESeq2 version 1.26.0 (11). Significant genes were identified using a Benjamini-Hochberg false discovery rate α of 0.05. Pathways were analyzed using Reactome (release 77, reactome.org) and performing the “Analyse gene list” function using Ensembl stable IDs (ENSMUSG).

Quantitative Real-Time PCR

Total kidney RNA from age-matched male and female Ift88 KO and control mice was isolated according to the manufacturer’s protocol (PureLink RNA Mini Kit, ThermoFisher Scientific) and reverse transcribed using SuperScript III (Invitrogen). Quantitative real-time PCR conducted using Taqman Gene Expression Assays (Applied Biosystems, Waltham, MA). TaqMan primers were as follows: Mm99999915_g1 (mouse GAPDH), Mm00777368_m1 [mouse Ca²⁺-activated Cl⁻ channel regulator 3 A-1 (Clca3a1)], Mm00802584_m1 [mouse fibrinogen α-chain (Fga)], Mm00513575_m1 [mouse fibrinogen γ-chain (Fgg)], Mm00476206_m1 [mouse hyaluronidase-1 (Hyal1)], Mm00452079_m1 [mouse stromal cell-derived factor 2-like 1 (Sdf2l1)], Mm00479456_m1 [mouse BTG3-associated nuclear protein (Banp)], Mm00456128_m1 [mouse β-1,3-N-acetylglucosyaminyltransferase lunatic fringe (Lfng)], Mm00497539_m1 [mouse D box binding PAR BZIP transcription factor (Dbp)], Mm00501493_m1 [mouse ST3 β-galactoside α-2,3-sialytransferase (St3gal1)], Mm01285623_m1 [mouse period 2 (Per2)[, and Mm00726417_s1 [mouse TSC domain family member 3 (Tsc22d3)]. Levels of mRNA were normalized to GAPDH.

Statistics

Western analysis data were analyzed by an unpaired Student’s t test. Lipidomics, metabolomics, quantitative real-time PCR, and proteomics data were analyzed using two-factor ANOVA with four experimental groups (female control, female KO, male control, and male KO), yielding two categories: sex (male vs. female) and treatment (control vs. KO). Statistical models were created from normalized data (Z-score).

For transcriptomics, differential gene expression modeling with DESeq2 v1.26.0 (11) tested for the effects of treatment and sex using pairwise comparison (Figs. 1, 3, and 6) or a negative binomial good linear model test with an interaction design [sex and treatment (control vs. KO)] (Figs. 2, 3, and 6, P adjusted values and the input list for Table 1). Biomarker analysis (Fig. 6) used DESeq2 pairwise comparisons to identify transcripts that were similar in male and female controls and then uniquely up- or downregulated in males and not females. Specifically, DESeq2 pairwise comparison values fit the following criteria: 1) male KO vs. female control, base mean >100, log₂ fold change between −0.1 and 0.1; 2) male KO versus male control P adjusted value ≤0.05 and log₂ fold change ≥0.5 (upregulated) or less than or equal to −0.5 (downregulated); and 3) not female significant, i.e., not on the list of female KO versus female control P adjusted value ≤0.05 and log₂ fold change ≥0.5 (upregulated) or less than or equal to −0.5 (downregulated).

Figure 1. — Volcano plots of RNA-sequencing results comparing male control vs. male intraflagellar transport protein 88 (Ift88) knockout (KO) mouse kidneys (n = 4 each genotype) and female control vs. female Ift88 KO mouse kidneys (n = 4 each genotype). Numbers next to the graph labels indicate the number of genes differentially expressed between control and KO mice for the given sex. The arrows indicate Ift88 mRNA.

Figure 2. — Heatmap of differentially expressed mRNA in male control (*MC1−MC4*), female control (*FC1−FC4*), male intraflagellar transport protein 88 (Ift88) knockout (KO) (*MK1−MK4*), and female Ift88 KO (*FK1−FK4*) mice. Z-scores are shown. Only mRNA transcripts are reported that met a P adjusted value of <0.05 using a 5% false discovery rate cutoff for the interaction [sex and treatment (control vs. KO)]. P values were determined using the negative binomial general linear model test obtained from DESeq2.

Table 1.

Pathways significantly enriched using Reactome analysis of the 148 significantly altered transcripts in RNA-sequencing data

Pathway	mRNA	P Adjusted
Collagen biosynthesis and modifying enzymes	ADAMTS2, COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL5A1, COL6A1, COL6A2, COL6A3, COL6A6, COL8A1, COL14A1, COL15A1, COL18A1, P3H1, PCOLCE, PCOLCE2, PLOD1, PLOD2, SERPINH1	2.09e⁻⁹
Integrin cell surface interactions	COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A3, COL5A1, COL6A1, COL6A2, COL6A3, COL6A6, COL8A1, COL18A1, FBN1, FGA, FGB, FGG, FN1, ICAM1, ITGA6, ITGB2, ITGB6, JAM3, KDR, SPP1	3.4e⁻⁸
Extracellular matrix organization	A2M, ADAM12, ADAMTS2, ADAMTS8, BGN, BMP2, COL1A1, COL1A2, COL3A1, COL4A2, COL4A3, COL5A1, COL6A1, COL6A2, COL6A3, COL6A6, COL4A1, COL8A1, COL14A1, COL15A1, COL18A1, CTSK, CTSS, DCN, DDR2, EFEMP2, ELN, EMILIN1, FBLN1, FBLN2, FBLN5, FBN1, FGA, FGB, FGF2, FGG, FN1, ICAM1, ITGA6, ITGA9, ITGB2, ITGB6, JAM3, KDR, KLKB1, LAMA2, LAMA5, LAMC2, LOX, LOXL1, LOXL2, LOXL3, LUM, MMP2, MMP12, MMP14, MMP17, NID1, NID2, NTN4, P3H1, P4HA1, PCOLCE, PCOLCE2, PLOD1, PLOD2, SDC1, SERPINH1, SPARC, SPP1, TGFB3, TIMP2, TNXB, VCAM1	6.04e⁻⁶
Signaling by PDGF	COL3A1, COL4A1, COL4A2, COL5A1, COL6A1, COL6A2, COL6A3, COL6A6, PDGFRB, SPP1, STAT5A, THBS2	1.28e⁻⁶
Degradation of extracellular matrix	A2M, ADAMTS8, COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL5A1, COL6A1, COL6A2, COL6A3, COL6A6, COL8A1, COL14A1, COL15A1, COL18A1, CTSK, CTSS, DCN, ELN, FBN1, FN1, MMP2, MMP12, MMP17, NID1, SPP1	4.87e⁻⁶
Platelet activation, signaling, and aggregation	A2M, F2R, F5, FGA, FGB, FGG, PROS1	0.0256

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Transcripts with an adjusted P interaction value of <0.05 are shown.

Figure 6. — Potential RNA biomarkers for male intraflagellar transport protein 88 (Ift88) knockout (KO) mice. RNA-sequencing data were analyzed for RNA transcripts with similar male and female control values (defined by DESeq2 baseMean >100 and pairwise log₂ fold change between −0.1 and 0.1). RNA transcripts meeting these criteria were then analyzed for a significant effect of Ift88 loss in males but not females [P adjusted ≤0.05 and log₂ fold change ≥0.5 (up; A) or less than or equal to −0.5 (down; B)] between male KO vs. male control and not in female KO vs. female control). Results from quantitative real-time PCR of the 11 RNA sequencing identified biomarkers are shown in C and D. P_int, interaction P value. Fga, fibrinogen α-chain; Hyal1, hyaluronidase 1; Clca3a1, Ca²⁺-activated Cl⁻ channel regulator 3 A-1; Sdf2l1, stromal cell-derived factor 2-like 1; Fgg, fibrinogen γ-chain; Lfng, β-1,3-N-acetylglucosyaminyltransferase lunatic fringe; Tsc22d3, TSC domain family member 3; Banp, BTG3-associated nuclear protein; Per2, period circadian regulator 2; St3gal1, ST3 β-galactoside α-2,3-sialytransferase; Dbp, D box binding PAR BZIP transcription factor.

P < 0.05 was taken as significant for sex, treatment, and interaction effects before (raw) and after (adjusted) multiple testing correction using a false discovery rate of <5%. Means ± SE are shown in line graphs.

RESULTS

Effect of Ift88 KO on Mouse Kidney mRNA Expression

Previous studies have shown Ift88 gene recombination primarily in the kidney with modest recombination in the liver and stomach (7). There was ∼95% reduced total kidney Ift88 mRNA expression in 4-mo-old male and female Ift88 KO mice compared with sex-matched controls (7). The reduction in Ift88 mRNA in 4-mo-old KO mice was confirmed in the present study by the highly significant reduction in Ift88 mRNA (Fig. 1).

Of the 21,015 transcripts detected in kidneys, 2,017 transcripts were differentially expressed (vs. the same-sex control) in male Ift88 KO mice and 1,279 transcripts in female Ift88 KO mice (Fig. 1). Of these, 148 transcripts had a statistically significant adjusted P_Int value of <0.05 [interaction between sex and treatment (control vs. KO) and after adjustment for multiple comparisons; Fig. 2] Pathway analysis of these 148 mRNA species revealed a significant enrichment in transcripts related to extracellular matrix (ECM) proteins, including synthesis of collagens and other ECM components, integrin cell surface interactions, and degradation of ECM (Table 1). In addition, pathways associated with signaling by platelet-derived growth factor (PDGF) and factors involved in platelet activation were significantly enriched (Table 1); Supplemental Table S1 (https://doi.org/10.6084/m9.figshare.17091968.v1) provides a list of the log₂ fold changes for all transcripts shown in Table 1. The specific effects of Ift88 KO on several of these key individual mRNA transcripts involved in the identified pathways are shown in Fig. 3 (transcripts demonstrating some of the largest Ift88 KO effects were selected). Male Ift88 KO mice displayed upregulation (compared with male controls) of mRNA transcripts for ECM synthesis and degradation-related proteins [ADAM metallopeptidase with thrombospondin type 1 motif 2 (Adamts2), collagen type XV-α₁ (Col15a1), collagen type IV-α (Col4a1), collagen type IV-α (Col4a2), collagen type V-α (Col5a1), collagen type VI-α (Col6a3), collagen type VI-α (Col6a6), serpin H1 (Serpinh1), and fibrillin 1 (Fbn1)], receptors for PDGF [PDGF receptor-β (Pdgfrb)] and VEGF (Flt1 and Kdr), and fibrinogen α-chain (Fga); all these transcripts were either unchanged or downregulated in female Ift88 KO mice compared with female control mice. The one exception was Serpinh1, which significantly increased more in males than females (adjusted P_int = 0.033, adjusted P value for female KO vs. female control = 0.028, and adjusted P value for male KO vs. male control = 1.81e⁻¹³). Finally, the clock gene transcripts Per2, Per3, and Nrld2 were decreased by Ift88 KO in males but not females (Fig. 2). The relationships between many of these factors is illustrated in the STRING diagram shown in Fig. 3.

Figure 3. — *Left*: examples of relative levels of mRNA in male and female control and intraflagellar transport protein 88 (Ift88) knockout (KO) mice (n = 4 each genotype) in one of the most highly significant pathways (collagen biosynthesis and modifying enzymes) differentially affected by Ift88 KO in male vs. female mice. *Right*: interactions between these mRNA transcripts using STRING. Adamts2, ADAM metallopeptidase with thrombospondin type 1 motif 2; Col, collagen; Fbn1, fibrillin-1; Fga, fibrinogen α-chain; Flt1, FMS-related tyrosine kinase 1 or VEGF receptor 1; Kdr, kinase insert domain receptor or VEGF receptor 2; Pdgfrb, platelet-derived growth factor receptor-β; Serpinh1, serpin family H member 1; Stat5a, signal transducer and activator of transcription 5 A.

Effect of Ift88 KO on Key Renal ECM Components

As previously reported (7), cysts were not present in 4-mo-old Ift88 KO kidneys, although mild tubule vacuolization was present in male Ift88 KO kidneys (Fig. 4 and Table 2). There was no evidence of renal fibrosis by trichrome or periodic acid-Schiff staining (Fig. 4 and Table 2). Western blot analysis of several ECM components (fibronectin and collagen types I, IV, and VI) revealed significant decreases in whole kidney fibronectin and collagen type I content selectively in male Ift88 KO mice (Fig. 5). Collagen type IV was unchanged by Ift88 KO in both sexes, while collagen type VI tended to be decreased in male Ift88 KO mice and was significantly reduced in female Ift88 KO mice. Taken together, these data suggest that key ECM components were relatively reduced in male Ift88 KO mice.

Figure 4. — Representative images from male and female control and intraflagellar transport protein 88 knockout (KO) kidneys stained with hematoxylin and eosin (H&E), trichrome, or periodic acid-Schiff (PAS). A minimum of 3 kidneys from different mice were used for each genotype. PAS and trichrome images are ×40 and H&E images are ×200. Arrows indicate areas of tubule cell vacuolization.

Table 2.

Tubule epithelial cell vacuolization and renal fibrosis in kidney sections from male and female control and intraflagellar transport protein 88 KO mice

Genotype	Tubular Epithelial Vacuoles	Fibrosis
Male control
Control 1	0	0
Control 2	0	0
Control 3	0	0
Female control
Control 1	0	0
Control 2	0	0
Control 3	0	0
Male KO
KO 1	2	0
KO 2	0	0
KO 3	1	0
Female KO
KO 1	0	0
KO 2	0	0
KO 3	0	0

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KO, knockout. 0 = none; 1 = 25%; 2 = >25–50%.

Figure 5. — Western analysis of fibronectin and collagen types I, IV, and VI in whole mouse kidneys from male and female control and intraflagellar transport protein 88 knockout (KO) mice. Representative immunoblots are shown on the *left*; graphs of male and female immunoblot results are shown on the *right* (n = 5-6 each genotype). P values are shown where relevant. Data were normalized to control mean values and analyzed by a Student’s unpaired t test.

Identification of Putative mRNA Biomarkers for Male Ift88 KO

Given that 148 mRNA transcripts were identified that had an adjusted P_Int value <0.05, it was possible that some of these might serve as noncystic kidney biomarkers for eventual cystic kidney disease in Ift88 KO mice. To this end, RNA-seq data were analyzed for transcripts with similar male and female control values (defined by baseMean >100 and log₂ fold difference between −0.1 and 0.1). Transcripts meeting these criteria were then analyzed for a significant effect of Ift88 loss to identify male-specific up- and downregulated transcripts (P adjusted ≤0.05 and log₂ fold difference ≥0.5 or less than or equal to −0.5). Thus, without knowing the specific genotype, one might use levels of these mRNAs as predictors of cystogenesis. As shown in Fig. 6A, mRNAs selectively increased in male Ift88 KO mice were Fga, Fgg, Hyal1, Clca3a1, and Sdf2l1. mRNAs meeting these criteria that were selectively decreased in male Ift88 KO mice were Lfng, Tsc22d3, Banp, Per2, St3gal1, and Dbp (Fig. 6B). To validate the RNA-seq findings, quantitative real-time PCR was performed on the putative mRNA biomarkers. Two of the transcripts (Fga and Sdf2l1) met upregulated biomarker criteria: similar male and female control values and upregulated by Ift88 KO in males with no effect of Ift88 KO on females. One of the transcripts (Banp) met downregulated biomarker criteria: similar male and female control values and downregulated by KO in males with no effect of KO in females. Two other transcripts (Dbp and Per2) were downregulated by Ift88 KO in males and either unchanged or upregulated by Ift88 KO in females; however, female control values exceeded male control values.

Effect of Ift88 KO on the Renal Proteomic Profile

Of the 2,387 proteins identified in the whole kidney, 89 proteins had an adjusted P value of <0.05 for sex and/or treatment (control vs. KO) as determined by two-way ANOVA (Fig. 7). Of these 89 proteins, only eukaryotic translation initiation factor 2 subunit 3 X-linked (Eif2s3x) had an adjusted P_Int value of <0.05, while 14 other proteins had a raw P_Int value of <0.05 (unadjusted for multiple comparisons) (Fig. 8). Of these total 15 proteins, 9 proteins had an identified human homolog; pathway analysis revealed no substantial commonalities among these proteins. However, the pathway analysis did not include enoyl-CoA hydratase domain containing 2 (Echdc2), which, together with medium chain acyl-CoA dehydrogenase (Acadm), is involved in fatty acid β-oxidation. No ECM proteins were detected in meaningful amounts in the proteomics profile.

Figure 7. — Heatmap of differentially expressed proteins in male control (*MC1−MC6*), female control (*FC1−FC6*), male intraflagellar transport protein 88 (Ift88) knockout (KO) (*MK1−MK6*), and female Ift88 KO (*FK1−FK5*) mice. Z-scores are shown. Only proteins with a P adjusted value of <0.05 for sex and/or treatment (control vs. KO) as determined by two-way ANOVA and adjusted for multiple comparisons are included.

Figure 8. — Proteins with a raw P value of <0.05 for interaction {sex and treatment [control vs. knockout (KO)]} in male and female control and KO mice (n = 5-6 each data point) as determined by two-way ANOVA. Graphs show mean values ± SE with raw and adjusted P values for sex, treatment, and interaction. Eif2s3x, eukaryotic translation initiation factor 2 subunit 3 X-linked; Lnpep, leucyl, and cystinyl aminopeptidase; Pnpla6, patatin-like phospholipase domain containing 6; Afm, afamin; Serpina6, serpin family A member 6; Ces1c, carboxylesterase 1 C; Poldip2, DNA polymerase δ interacting protein 2; Psma2, proteasome subunit α type 2; Hint2, histidine triad nucleotide binding protein 2; Tkt, transketolase; Npl, N-acetylneuraminate pyruvate lyase; Tagln, transgelin; Ndufb8, NADH dehydrogenase 1β subcomplex subunit 8; Acadm, medium chain acyl-CoA dehydrogenase; Echdc2, enoyl-CoA hydratase domain containing 2.

Comparison of transcriptomic and proteomic data did not reveal an overlap in significantly altered mRNA and protein. However, of the 148 identified transcripts with an adjusted P_Int value of <0.05, only 34 transcripts of the encoded proteins were present in the proteomic analysis. In addition, of the 15 transcripts identified in Fig. 3, only 2 transcripts were present in the proteomic analysis, while of the 10 biomarker transcripts identified in Fig. 6, only 2 biomarker transcripts were present in the proteomic analysis. Thus failure to detect a correlation between transcriptomic and proteomic data was in large part due to insufficient resolution of the proteomic assessment.

Effect of Ift88 KO on the Renal Metabolomic Profile

Relative levels of the 79 measured whole kidney metabolites are shown in Fig. 9. No metabolite achieved a statistically significant adjusted P_Int value. There were nine significantly altered metabolites (P adjusted value < 0.05) when we compared the effect of sex or treatment independently (Fig. 10 and Table 3). Of these nine metabolites, two metabolites had a raw P_Int value of <0.05: 3-hydroxybutryate (β-hydroxybutyrate) and 2-hydroxybutyrate (α-hydroxybutyrate) (Table 3). In both cases, levels numerically increased with Ift88 KO in males and either numerically decreased or did not significantly change in Ift88 KO females (Fig. 10). While hydroxyproline was not significantly impacted by Ift88 KO, both proline and glycine, two other amino acids present in relatively large amounts in collagen, were among the nine metabolites identified above. Proline was increased by Ift88 KO, although no sex effect was observed (P adjusted for sex = 0.98; P adjusted for treatment = 0.04). Glycine was not affected by KO, although a strong sex-dependent effect was present (P adjusted for sex = 0.001; P adjusted for treatment = 0.17).

Figure 9. — Heatmap of all tested nonlipid metabolites in male control (*MC1−MC6*), female control (*FC1−FC6*), male intraflagellar transport protein 88 (Ift88) knockout (KO) (*MK1−MK6*), and female Ift88 KO (*FK1−FK6*) mice.

Figure 10. — Nonlipid metabolites with P adjusted <0.05 for treatment [control vs. knockout (KO)] as analyzed by two-way ANOVA with correction for multiple comparisons. A: heatmap for these metabolites in male control (*MC1−MC6*), female control (*FC1−FC6*), male intraflagellar transport protein 88 (Ift88) KO (*MK1−MK6*), and female Ift88 KO (*FK1−FK6*) mice. B: graphs of the relative levels of 2-hydroxybutryic acid and 3-hydroxybutryic acid in the four groups (n = 6 each data point).

Table 3.

Metabolites with sex, treatment (control or intraflagellar transport protein 88 knockout), and/or interaction P values of <0.05

Metabolite	Sex		Treatment		Interaction
Metabolite	Raw P	Adjusted P	Raw P	Adjusted P	Raw P	Adjusted P
3-Hydroxybutyric acid	0.0035	0.037	0.029	0.171	0.033	0.845
2-Hydroxybutyric acid	0.00024	0.0041	0.028	0.171	0.039	0.845
l-Valine	0.587	0.913	0.0003	0.029	0.154	0.958
Hypotaurine	0.00037	0.0052	0.046	0.213	0.292	0.958
β-Alanine	8.1e⁻⁹	6.8e⁻⁷	0.163	0.416	0.306	0.958
l-Threonine	0.514	0.831	0.0019	0.041	0.332	0.963
DHAP	0.408	0.732	0.0011	0.041	0.376	0.986
Glycine	5.5e⁻⁵	0.0011	0.0283	0.170	0.466	0.986
l-Proline	0.959	0.981	0.0014	0.041	0.475	0.986

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Effect of Ift88 KO on the Renal Lipidomics Profile

Of the 583 lipids measured in the whole kidney, 30 lipids had a P_Int value of <0.05 (Fig. 11A and Table 4). Of these, the predominant forms were acylcarnitines (ACar). Examples of three significantly altered ACar are shown in Fig. 11B ACar 16:0, 20:1, and 20:2. In general, almost all ACar levels were reduced by Ift88 KO in females and were either unchanged or tended to increase by Ift88 KO in males.

Figure 11. — Relative lipid levels in male and female control and intraflagellar transport protein 88 (Ift88) knockout (KO) mice. A: heatmap of lipids with P adjusted <0.05 for interaction (sex and treatment) in male control (*MC1−MC6*), female control (*FC1−FC6*), male Ift88 KO (*MK1−MK6*), and female Ift88 KO (*FK1−FK6*) mice. Data were analyzed by two-way ANOVA with correction for multiple comparisons. B: graphs of the relative levels of acylcarnitine (ACar) 16:0, 20:1, and 20:2 in the four groups (n = 6 each data point). CE, cholesterol ester; PC, phosphocholine; PE, phosphoethanolamine; ShexCer, sulfatide hexosylceramide; TG, triglyceride.

Table 4.

Lipids with raw P values of <0.05 for an interaction between sex and treatment (control or intraflagellar transport protein 88 knockout)

Lipid	Sex		Treatment		Interaction
Lipid	Raw P	Adjusted P	Raw P	Adjusted P	Raw P	Adjusted P
ACar 16:0	1.3e⁻⁷	1.3e⁻⁶	5.23e⁻⁵	0.016	6.7e⁻⁵	0.039
ACar 14:0	1.2e⁻⁷	1.2e⁻⁶	5.40e⁻⁵	0.016	3.7e⁻⁴	0.077
ACar 20:1	2.8e⁻⁶	1.9e⁻⁵	2.01e⁻⁴	0.020	4.8e⁻⁴	0.077
ACar 20:2	0.0024	0.0061	1.78e⁻⁴	0.020	5.3e⁻⁴	0.077
ACar 18:1	3.7e⁻⁸	4.5e⁻⁷	5.49e⁻⁴	0.042	7.7e⁻⁴	0.090
ACar 16:1	1.7e⁻⁵	8.3e⁻⁵	7.52e⁻⁴	0.049	9.5e⁻⁴	0.093
ACar 18:0	6.1e⁻⁹	1.1e⁻⁷	1.95e⁻⁴	0.088	0.0011	0.095
ACar 18:2	0.0016	0.0043	1.97e⁻⁴	0.088	0.0028	0.203
CE 22:4	0.0004	0.0012	6.45e⁻⁴	0.139	0.0051	0.303
ACar 16:2	0.0043	0.0099	1.05e⁻⁴	0.020	0.0052	0.303
ACar 14:1	2.1e⁻⁶	1.5e⁻⁵	1.64e⁻⁴	0.020	0.0061	0.304
EtherPE 16:1e_16:1	0.0008	0.0022	0.459	0.802	0.0063	0.304
ACar 12:0	2.2e⁻⁵	0.0001	0.0006	0.042	0.0094	0.370
PC 14:0_16:1	1.1e⁻⁷	1.1e⁻⁶	0.067	0.390	0.0099	0.370
TG 20:4_20:4_22:6	0.0041	0.009	0.053	0.363	0.011	0.370
TG 18:1_18:2_22:6	0.0188	0.036	0.192	0.604	0.011	0.370
TG 18:0_18:2_22:6	0.0021	0.0054	0.342	0.736	0.012	0.374
ACar 20:3	0.0048	0.011	0.019	0.254	0.015	0.443
SHexCer d34:1	1.1e⁻¹⁴	1.7e⁻¹²	0.082	0.421	0.018	0.467
CE 22:6	0.0023	0.0059	0.036	0.325	0.018	0.467
TG 16:0_18:1_22:6	0.0058	0.013	0.456	0.800	0.020	0.495
CE 20:5	0.0046	0.011	0.977	0.986	0.026	0.540
ACar 20:0	8.4e⁻⁸	8.8e⁻⁷	0.041	0.325	0.028	0.540
TG 18:1_22:6_22:6	0.0064	0.0138	0.082	0.421	0.028	0.540
CE 20:2	0.0253	0.0466	0.381	0.754	0.030	0.540
TG 18:0_18:1_22:6	0.0051	0.0112	0.652	0.874	0.031	0.540
CE 20:4	8.7e⁻⁵	0.0003	0.355	0.750	0.032	0.575
ACar 20:4	3.3e⁻⁶	2.1e⁻⁵	0.106	0.475	0.036	0.575
PE 20:1_22:6	1.7e⁻⁶	1.2e⁻⁵	0.032	0.325	0.046	0.655

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Data are ranked by interaction P values.

DISCUSSION

The present study was designed to identify renal substances that serve as candidate biomarkers for and/or play a putative role in the development of kidney cysts in nephron-specific Ift88 KO mice. Homozygous Ift88 KO mice lack cilia; while this model has been widely studied, it remains incompletely understood how ciliary absence or dysfunction promote cystogenesis in ORPK mice (6). More generally, it also remains unclear how disruption of other key ciliary proteins involved in anterograde (base to tip) cilia transport (IFT-B), retrograde (tip to base) cilia transport (IFT-A), Bardet-Biedl syndrome complexes, or cilia transport motors [e.g., kinesin 3 A (Kif3A)] can promote renal cystogenesis (12–14). Cilia have been implicated in the regulation of multiple signaling systems, including Hedgehog, PDGF receptors, Wnt, and others, although how cilia directly modulate these pathways has not been fully elucidated (13, 14). In addition, mutations in proteins localized to cilia or the basal body cause PKD, including polycystin-1 and polycystin-2 [autosomal dominant PKD (ADPKD)], fibrocystin [autosomal recessive PKD (ARPKD)], and inversin [nephronophthisis 2 (NPHP2)] (12, 13). However, the relationship between these proteins and cilia, particularly with respect to cystogenesis, is complicated. Polycystin-1 and polycystin-2 are not solely localized to cilia, and it is not certain that fibrocystin localizes exclusively to cilia (13). Furthermore, human renal cystic disease is rarely due to mutations in IFT (e.g., IFT-43, IFT-80, IFT-121, IFT-122, IFT-140, or IFT-144) or Bardet-Biedl syndrome proteins; the kidneys typically are small and fibrotic with few cysts and only rarely are polycystic (12, 13). Another important issue is that, while single gene targeting of Pkd1, Pkd2, or Ift88 in mice causes cystic kidney disease, combined gene targeting uncovered complex interactions. A recent study demonstrated that Ift88 KO in mice with coincident Pkd1 or Pkd2 KO had shortened elongated cilia, decreased cell proliferation, and reduced renal cystogenesis seen with Pkd1 or Pkd2 KO alone (15). Furthermore, cilia disruption by Kif3A or Ift20 KO ameliorates cystic kidney disease in Pkd1 or Pkd2 KO mice (13, 16, 17). Taken together, the above considerations indicate that the inducible Ift88 KO mouse serves as a useful model for studying cystogenesis but that results from such studies should not be generalized to clinical PKD and particularly not to ADPKD.

As previously described, experimental and clinical studies have reported enhanced male susceptibility to cyst formation and/or end-stage kidney disease (in addition to Ift88 KO), including Han:SPRD rats (mutation in the Anks6 gene) (18), Jck mice (mutation in the Nek8 gene) (19), PCK rats (mutation in Pkhd1 gene) (20), and patients with ADPKD with Pkd2 gene mutations or underdetermined genetics (21–24). The causes of increased male susceptibility to cystogenesis are not well understood; testosterone may promote acute and chronic renal injury through increasing vasoconstriction, fibrosis, oxidative stress, apoptosis, and inflammation, while estrogen may be protective (25). Beyond the sex hormone differences, studies that identified predictors of cystic kidney disease course have largely involved assessment of factors associated with the rate of ADPKD disease progression in individuals who already have cystic kidneys; factors that predict more rapid renal deterioration include elevated urine kidney injury molecule-1 (26), urine proteomic profile (discussed below), male sex, early onset of hypertension, early and frequent gross hematuria, ≥3 pregnancies, and increased total kidney volume (23). The above findings suggest that while the underlying mechanisms responsible for enhanced male susceptibility to PKD may well vary depending on the mutated gene, some common mechanisms may be involved across the spectrum of cystic kidney disease.

One of the major findings of the present study was identification of potential ECM alterations in precystic Ift88 KO as predictors, and possibly facilitators, of cystogenesis. We found upregulated expression of multiple transcripts for ECM components selectively in Ift88 KO male mice, suggesting that ECM biosynthetic pathways are stimulated in the precystic phase. Despite this, no increase in renal fibrosis was observed, and, in fact, major ECM components (fibronectin and collagen type I) were reduced selectively in precystic Ift88 KO male kidneys (unfortunately, proteomic analysis had far less coverage than RNA-seq, resulting in failure to detect most proteins encoded by the RNA-seq identified ECM-related transcripts). Since ECM degradation transcripts were also upregulated in these Ift88 KO male kidneys, the possibility arises that ECM turnover is enhanced. While direct analysis of ECM turnover was not conducted, it is interesting to speculate that augmented turnover of the ECM, if it does indeed occur, assists with eventual tubule cell proliferation and cyst formation. It is also relevant to note that many of the upregulated ECM transcripts in male Ift88 KO kidneys were downregulated by Ift88 KO in females. The reasons for this are speculative, but it may be that these responses in females are somehow protective. Furthermore, while cystic kidney disease due to Ift88 and Pkd1 gene mutations are likely mediated by substantially differing mechanisms, it may be relevant to note that ECM metabolism is also altered in ADPKD models. A previous study that examined precystic Pkd1-deficient mouse kidneys found changes in collagen metabolism; the most highly flow-stimulated transcripts in cultured renal epithelial cells were ECM related [serpin E1 (Serpine1) and collagen type I-α (Col1a1), both of which had enhanced transcript abundance in Pkd1-deficient cells] (27). Other studies that have examined ADPKD models and the ECM involved cystic kidneys. Fibrosis is present in ADPKD; ADPKD cyst epithelial cells have increased expression of ECM components and integrins (28–30) as well as profibrotic factors, including transforming growth factor-β, activin A, epidermal growth factor, fibroblast growth factor-1, and hepatocyte growth factor (31). Furthermore, urinary proteomic score (which mostly reflected urinary collagen fragments) predicted ADPKD severity (32). Perhaps more relevant to the pathogenesis of cysts is the finding that abnormal ECM composition and turnover are seen in early stage ADPKD (31). Along these lines, ECM collagen fragments can be proinflammatory; ADPKD is characterized by the presence of inflammation, including macrophages, cytokines, chemokines, and activation of proinflammatory pathways (e.g., JAK-STAT and NF-κB) (31). Finally, it is worth noting that loss of polycystin-1 alone, albeit not resulting in cilia loss, enhances transforming growth factor-β signaling in vascular smooth muscle cells (33), while global Ift88 KO mice have defects in skeletal patterning associated with abnormal ECM deposition in the growth plate (34). Taken together, these considerations raise the possibility that while the relevant mechanisms likely substantially differ between genotypes, cystic kidney disease, whether due to Ift88 or Pkd1 KO in mice, may be characterized by early alterations in ECM metabolism.

A second finding in the present study was evidence suggesting a differential effect of Ift88 KO on factors involved in fatty acid oxidation. Knockout of Ift88 tended to increase Acadm protein levels in males. KO of Ift88 markedly reduced levels of multiple ACar in females but either did not change or tended to increase ACar levels in males. Finally, β-hydroxybutyrate levels tended to decrease in Ift88 KO females and increase in Ift88 KO males. Since ACar are required for fatty acids to enter mitochondria, Acadm is involved in mitochondrial fatty acid β-oxidation, and β-hydroxybutyrate is a metabolite of fatty acid β-oxidation, these findings suggest that Ift88 KO has a relatively modest effect on fatty acid β-oxidation in males while potentially reducing fatty acid β-oxidation in females. Since most tubule epithelial cells metabolize fatty acids preferentially over glucose for energy generation, the possibility is raised that reduced renal metabolic activity in females inhibits cystogenic mechanisms, such as cell proliferation, enhanced ECM metabolism, and others. A few studies have previously examined fatty acid oxidation in ADPKD. Menenzes et al. (35) performed urinary metabolic profiling on Pkd1 mutant mice during normal, early, and late cystic stages; acetylcarnitine (transports acetyl groups out of mitochondria) most accurately predicted ADPKD status (direct relationship). Interestingly, this same group found that cells lacking the Pkd1 gene have defective fatty acid oxidation (36). The reasons for these findings, as well as the possible differences in fatty acid metabolism between precystic and cystic Ift88 KO kidneys, are speculative. One obvious difference is that Pkd1 and Ift88-deficient kidneys, as discussed earlier, behave differently; however, another intriguing possibility is that kidney injury (as occurs when cysts, inflammation, and fibrosis are present) is strongly associated with impaired fatty acid oxidation (37), Along these lines, it is notable that shortening of cilia or loss of Ift88 (with attendant cilia loss) are associated with enhanced susceptibility to kidney injury (38, 39). Perhaps once sufficient injury occurs following ciliary loss, fatty acid oxidation undergoes a shift to decreased activity.

RNA-seq analysis revealed male-specific Ift88 KO-induced changes in transcript levels beyond those described above. First, Ift88 KO increased Pdgfrb, Fga, and Fgg transcripts selectively in male mice. While not directly investigated, the source of these platelet-related transcripts may be endothelial cells. While endothelial cells can exhibit pro-platelet activation characteristics in early stage ADPKD (40), the present study did not directly target endothelial cell Ift88; it is possible that nephron Ift88 KO leads to altered tubule epithelium-derived signals that modulate endothelial cell function (including changes in the ECM). Second, Ift88 KO caused selective decreases in circadian clock protein Per2, Per3, and Nrld2 transcript levels. We are unaware of any previous reports describing altered clock gene expression in any form of PKD; however, since clock genes regulate up to 50% of protein-coding genes and have been implicated in multiple forms of cancer, it is perhaps not surprising that clock gene transcript levels are altered in precystic male Ift88 KO mice (41). Finally, it should be noted that RNA-seq analysis has been conducted on precystic mutant Pkd1 and Pkd2 mice; while mechanisms of cystogenesis likely substantially differ between Ift88 and Pkd1/2 KO mice, analysis of both precystic Ift88 and Pkd1/2 KO RNA-seq data may reveal important commonalities and/or differences in signaling pathways. In this regard, Zhang et al. (17) conducted RNA-seq on kidneys from Pkd2 KO, combined Pkd2/Ift88 KO, and control mice. They found 435 differentially expressed genes when they compared Pkd2 versus Pkd2/Ift88 KO kidneys and 241 differentially expressed genes when they compared Pkd2 versus Pkd2/Ift88 and control kidneys. Cell cycle pathways in general, and cyclin-dependent kinase-1 in particular, were uniquely activated in Pkd2 KO kidneys and were of central importance in Pkd2 KO cystogenesis; in addition, several ECM-related transcripts were differentially expressed in Pkd2 KO kidneys (17). In renal RNA-seq studies that examined precystic Pkd1 KO mice, the involvement of core pathways including phosphatidylinositol 3-kinase-AKT, MAPK, Ras, hypoxia-inducible factor-1, Wnt, transforming growth factor-β, tumor necrosis factor, and others was reported (27). Finally, RNA-seq studies of pre-cystic Pkd1 KO mouse kidneys found that dysregulated metabolic pathways was a prominent feature (42).

A goal of the present study was to identify putative biomarkers of cystogenesis in Ift88 KO mice. Such biomarkers would ideally not be different between healthy male and female controls and would be selectively up- or downregulated in individuals who progress to PKD (i.e., in male Ift88 KO mice). While 11 transcripts were identified by RNA-seq analysis that met these criteria, quantitative real-time PCR validated 3 of these as potential biomarkers. The PCR and RNA-seq analyses generally showed similar effects of Ift88 KO on transcript levels; however, control male and control female transcript levels were typically not equivalent. The reasons for the differences between RNA-seq and PCR results may relate, at least in part, to sample size and inherent variability. Of the three validated potential biomarkers, Fga and Sdf2l1 were upregulated and Banp was downregulated. What role Sdf2l1 (part of a large multiprotein chaperone complex in the endoplasmic reticulum), Fga (discussed above), and Banp (binds to matrix attachment regions and regulates the cell cycle) might have in PKD development remains speculative; however, they do suggest further research directions.

Perspectives and Significance

In conclusion, the present study used a multiomic approach, combined with Western blot analysis, to identify factors that were specifically altered in precystic kidneys of male Ift88 KO mice. Analysis of these potential biomarkers and/or causative factors of PKD due to Ift88 KO revealed alterations in several pathways, suggesting selective effects in male Ift88 KO mice on ECM metabolism, fatty acid oxidation, platelet activation, clock gene expression, and others. Notably, the present study focused on factors within the kidney; future studies using urine and/or blood multiomic profiling may yield important additional insights.

GRANTS

The Huntsman Cancer Institute High-Throughput Genomics and Bioinformatic Analysis Shared Resource at the University of Utah performed RNA-seq and is supported by National Cancer Institute Grant P30CA042014. Mass spectrometry equipment was obtained through National Center for Research Resources Shared Instrumentation Grants 1S10OD016232-01, 1S10OD018210-01A1, and 1S10OD021505-01. This work was supported by National Institutes of Health Grants P01HL136267 (to D.E.K) and DK123727 (to Y.H.).

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

AUTHOR CONTRIBUTIONS

C.H. and D.E.K. conceived and designed research; C.H. and M.P.R. performed experiments; C.H., K.B., E.J.H., J.M.L.-G., M.P.R., Y.H., J.A.M., J.E.C., and D.E.K. analyzed data; C.H., K.B., E.J.H., J.M.L.-G., M.P.R., Y.H., J.A.M., J.E.C., and D.E.K. interpreted results of experiments; C.H., K.B., J.A.M., and D.E.K. prepared figures; C.H., K.B., and D.E.K. drafted manuscript; C.H., K.B., E.J.H., J.M.L.-G., M.P.R., Y.H., J.A.M., J.E.C., and D.E.K. edited and revised manuscript; C.H., K.B., E.J.H., J.M.L.-G., M.P.R., Y.H., J.A.M., J.E.C., and D.E.K. approved final version of manuscript.

ENDNOTE

At the request of the authors, readers are herein alerted to the fact that additional materials involving RNA-seq, proteomic, lipidomic, and metabolomic data sets related to this article may be found at https://doi.org/10.6084/m9.figshare.16607960.v1. These materials are not a part of this manuscript and have not undergone peer review by the American Physiological Society. The American Physiological Society and journal editors take no responsibility for these materials, for the website address, or for any links to or from it.

ACKNOWLEDGMENTS

Proteomics analysis was performed at the Proteomics Core Facility at the University of Utah. Metabolomics analysis was performed at the Metabolomics Core Facility at the University of Utah.

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9.Marcel M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal 17: 10–12, 2011. doi: 10.14806/ej.17.1.200. [DOI] [Google Scholar]
10.Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923–930, 2014. doi: 10.1093/bioinformatics/btt656. [DOI] [PubMed] [Google Scholar]
11.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550, 2014. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Dell KM. The role of cilia in the pathogenesis of cystic kidney disease. Curr Opin Pediatr 27: 212–218, 2015. doi: 10.1097/MOP.0000000000000187. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Pazour GJ, Quarmby L, Smith AO, Desai PB, Schmidts M. Cilia in cystic kidney and other diseases. Cell Signal 69: 109519, 2020. doi: 10.1016/j.cellsig.2019.109519. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Veland IR, Awan A, Pedersen LB, Yoder BK, Christensen ST. Primary cilia and signaling pathways in mammalian development, health and disease. Nephron Physiol 111: p39–53, 2009. doi: 10.1159/000208212. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Shao L, El-Jouni W, Kong F, Ramesh J, Kumar RS, Shen X, Ren J, Devendra S, Dorschel A, Wu M, Barrera I, Tabari A, Hu K, Haque N, Yambayev I, Li S, Kumar A, Behera TR, McDonough G, Furuichi M, Xifaras M, Lu T, Alhayaza RM, Miyabayashi K, Fan Q, Ajay AK, Zhou J. Genetic reduction of cilium length by targeting intraflagellar transport 88 protein impedes kidney and liver cyst formation in mouse models of autosomal polycystic kidney disease. Kidney Int 98: 1225–1241, 2020. doi: 10.1016/j.kint.2020.05.049. [DOI] [PubMed] [Google Scholar]
16.Ma M, Tian X, Igarashi P, Pazour GJ, Somlo S. Loss of cilia suppresses cyst growth in genetic models of autosomal dominant polycystic kidney disease. Nat Genet 45: 1004–1012, 2013. doi: 10.1038/ng.2715. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Zhang C, Balbo B, Ma M, Zhao J, Tian X, Kluger Y, Somlo S. Cyclin-dependent kinase 1 activity Is a driver of cyst growth in polycystic kidney disease. J Am Soc Nephrol 32: 41–51, 2021. doi: 10.1681/ASN.2020040511. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Stringer KD, Komers R, Osman SA, Oyama TT, Lindsley JN, Anderson S. Gender hormones and the progression of experimental polycystic kidney disease. Kidney Int 68: 1729–1739, 2005. doi: 10.1111/j.1523-1755.2005.00589.x. [DOI] [PubMed] [Google Scholar]
19.Smith LA, Bukanov NO, Husson H, Russo RJ, Barry TC, Taylor AL, Beier DR, Ibraghimov-Beskrovnaya O. Development of polycystic kidney disease in juvenile cystic kidney mice: insights into pathogenesis, ciliary abnormalities, and common features with human disease. J Am Soc Nephrol 17: 2821–2831, 2006. doi: 10.1681/ASN.2006020136. [DOI] [PubMed] [Google Scholar]
20.Lager DJ, Qian Q, Bengal RJ, Ishibashi M, Torres VE. The pck rat: a new model that resembles human autosomal dominant polycystic kidney and liver disease. Kidney Int 59: 126–136, 2001. doi: 10.1046/j.1523-1755.2001.00473.x. [DOI] [PubMed] [Google Scholar]
21.Cornec-Le Gall E, Audrezet MP, Renaudineau E, Hourmant M, Charasse C, Michez E, Frouget T, Vigneau C, Dantal J, Siohan P, Longuet H, Gatault P, Ecotiere L, Bridoux F, Mandart L, Hanrotel-Saliou C, Stanescu C, Depraetre P, Gie S, Massad M, Kersale A, Seret G, Augusto JF, Saliou P, Maestri S, Chen JM, Harris PC, Ferec C, Le Meur Y. PKD2-related autosomal dominant polycystic kidney disease: Prevalence, clinical presentation, mutation spectrum, and prognosis. Am J Kidney Dis 70: 476–485, 2017. doi: 10.1053/j.ajkd.2017.01.046. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Johnson AM, Gabow PA. Identification of patients with autosomal dominant polycystic kidney disease at highest risk for end-stage renal disease. JASN 8: 1560–1567, 1997. doi: 10.1681/ASN.V8101560. [DOI] [PubMed] [Google Scholar]
23.Schrier RW, Brosnahan G, Cadnapaphornchai MA, Chonchol M, Friend K, Gitomer B, Rossetti S. Predictors of autosomal dominant polycystic kidney disease progression. J Am Soc Nephrol 25: 2399–2418, 2014. doi: 10.1681/ASN.2013111184. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Stewart JH. End-stage renal failure appears earlier in men than in women with polycystic kidney disease. Am J Kidney Dis 24: 181–183, 1994. doi: 10.1016/s0272-6386(12)80179-x. [DOI] [PubMed] [Google Scholar]
25.Lima-Posada I, Bobadilla NA. Understanding the opposite effects of sex hormones in mediating renal injury. Nephrology (Carlton) 26: 217–226, 2021. doi: 10.1111/nep.13806. [DOI] [PubMed] [Google Scholar]
26.Griffin BR, You Z, Noureddine L, Gitomer B, Perrenoud L, Wang W, Chonchol M, Jalal D, The HI, the HALT Investigators. KIM-1 and kidney disease progression in autosomal dominant polycystic kidney disease: HALT-PKD results. Am J Nephrol 51: 473–479, 2020. doi: 10.1159/000508051. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Kunnen SJ, Malas TB, Formica C, Leonhard WN, 't Hoen PAC, Peters DJM. Comparative transcriptomics of shear stress treated Pkd1(-/-) cells and pre-cystic kidneys reveals pathways involved in early polycystic kidney disease. Biomed Pharmacother 108: 1123–1134, 2018. doi: 10.1016/j.biopha.2018.07.178. [DOI] [PubMed] [Google Scholar]
28.Joly D, Morel V, Hummel A, Ruello A, Nusbaum P, Patey N, Noël LH, Rousselle P, Knebelmann B. Beta4 integrin and laminin 5 are aberrantly expressed in polycystic kidney disease: role in increased cell adhesion and migration. Am J Pathol 163: 1791–1800, 2003. doi: 10.1016/s0002-9440(10)63539-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Klingel R, Ramadori G, Schuppan D, Knittel T, Meyer Zum Buschenfelde KH, Kohler H. Coexpression of extracellular matrix glycoproteins undulin and tenascin in human autosomal dominant polycystic kidney disease. Nephron 65: 111–118, 1993. doi: 10.1159/000187451. [DOI] [PubMed] [Google Scholar]
30.Zeltner R, Hilgers KF, Schmieder RE, Porst M, Schulze BD, Hartner A. A promoter polymorphism of the alpha 8 integrin gene and the progression of autosomal-dominant polycystic kidney disease. Nephron Clin Pract 108: c169–175, 2008. doi: 10.1159/000116887. [DOI] [PubMed] [Google Scholar]
31.Song CJ, Zimmerman KA, Henke SJ, Yoder BK. Inflammation and fibrosis in polycystic kidney disease. Results Probl Cell Differ 60: 323–344, 2017. doi: 10.1007/978-3-319-51436-9_12. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Kistler AD, Serra AL, Siwy J, Poster D, Krauer F, Torres VE, Mrug M, Grantham JJ, Bae KT, Bost JE, Mullen W, Wuthrich RP, Mischak H, Chapman AB. Urinary proteomic biomarkers for diagnosis and risk stratification of autosomal dominant polycystic kidney disease: a multicentric study. PLoS One 8: e53016, 2013. doi: 10.1371/journal.pone.0053016. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Liu D, Wang CJ, Judge DP, Halushka MK, Ni J, Habashi JP, Moslehi J, Bedja D, Gabrielson KL, Xu H, Qian F, Huso D, Dietz HC, Germino GG, Watnick T. A Pkd1-Fbn1 genetic interaction implicates TGF-ß signaling in the pathogenesis of vascular complications in autosomal dominant polycystic kidney disease. JASN 25: 81–91, 2014. doi: 10.1681/ASN.2012050486. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Zhang Q, Murcia NS, Chittenden LR, Richards WG, Michaud EJ, Woychik RP, Yoder BK. Loss of the Tg737 protein results in skeletal patterning defects. Dev Dyn 227: 78–90, 2003. doi: 10.1002/dvdy.10289. [DOI] [PubMed] [Google Scholar]
35.Menezes LF, Zhou F, Patterson AD, Piontek KB, Krausz KW, Gonzalez FJ, Germino GG. Network analysis of a Pkd1-mouse model of autosomal dominant polycystic kidney disease identifies HNF4alpha as a disease modifier. PLoS Genet 8: e1003053, 2012. doi: 10.1371/journal.pgen.1003053. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Menezes LF, Lin CC, Zhou F, Germino GG. Fatty acid oxidation is impaired in an orthologous mouse model of autosomal dominant polycystic kidney disease. EBioMedicine 5: 183–192, 2016. doi: 10.1016/j.ebiom.2016.01.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Console L, Scalise M, Giangregorio N, Tonazzi A, Barile M, Indiveri C. The link between the mitochondrial fatty acid oxidation derangement and kidneyinjury. Front Physiol 11: 794, 2020. doi: 10.3389/fphys.2020.00794. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Wang S, Dong Z. Primary cilia and kidney injury: current research status and future perspectives. Am J Physiol Renal Physiol 305: F1085–F1098, 2013. doi: 10.1152/ajprenal.00399.2013.. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Wang S, Zhuang S, Dong Z. IFT88 deficiency in proximal tubular cells exaggerates cisplatin-induced injury by suppressing autophagy. Am J Physiol Renal Physiol 321: F269–F277, 2021. doi: 10.1152/ajprenal.00672.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Ramunni A, Brescia P, Quaranta D, Bianco MS, Ranieri P, Dolce E, Coratelli P. Cutaneous microcirculation is impaired in early autosomal dominant polycystic kidney disease. Nephron Clin Pract 113: c71–75, 2009. doi: 10.1159/000228537. [DOI] [PubMed] [Google Scholar]
41.Astone M, Santoro MM. Time to fight: targeting the circadian clock molecular machinery in cancer therapy. Drug Discov Today 26: 1164–1184, 2021. doi: 10.1016/j.drudis.2021.01.023. [DOI] [PubMed] [Google Scholar]
42.Terabayashi T, Germino GG, Menezes LF. Pathway identification through transcriptome analysis. Cell Signal 74: 109701, 2020. doi: 10.1016/j.cellsig.2020.109701. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

[B1] 1.Hovater MB, Olteanu D, Hanson EL, Cheng NL, Siroky B, Fintha A, Komlosi P, Liu W, Satlin LM, Bell PD, Yoder BK, Schwiebert EM. Loss of apical monocilia on collecting duct principal cells impairs ATP secretion across the apical cell surface and ATP-dependent and flow-induced calcium signals. Purinergic Signal 4: 155–170, 2008. doi: 10.1007/s11302-007-9072-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[B3] 3.Siroky BJ, Ferguson WB, Fuson AL, Xie Y, Fintha A, Komlosi P, Yoder BK, Schwiebert EM, Guay-Woodford LM, Bell PD. Loss of primary cilia results in deregulated and unabated apical calcium entry in ARPKD collecting duct cells. Am J Physiol Renal Physiol 290: F1320–F1328, 2006. doi: 10.1152/ajprenal.00463.2005. [DOI] [PubMed] [Google Scholar]

[B4] 4.Yoder BK. Role of primary cilia in the pathogenesis of polycystic kidney disease. J Am Soc Nephrol 18: 1381–1388, 2007. doi: 10.1681/ASN.2006111215. [DOI] [PubMed] [Google Scholar]

[B5] 5.Davenport JR, Watts AJ, Roper VC, Croyle MJ, van Groen T, Wyss JM, Nagy TR, Kesterson RA, Yoder BK. Disruption of intraflagellar transport in adult mice leads to obesity and slow-onset cystic kidney disease. Curr Biol 17: 1586–1594, 2007. doi: 10.1016/j.cub.2007.08.034. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[B7] 7.Hu C, Lakshmipathi J, Binning E, Hyndman KA, Stuart D, Kohan DE. Sex-dependent effects of nephron Ift88 disruption on BP, renal function and cystogenesis. J Am Soc Nephrol 32: 2210–2222, 2021. doi: 10.1681/ASN.2020111571. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B8] 8.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29: 15–21, 2013. doi: 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B9] 9.Marcel M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal 17: 10–12, 2011. doi: 10.14806/ej.17.1.200. [DOI] [Google Scholar]

[B10] 10.Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923–930, 2014. doi: 10.1093/bioinformatics/btt656. [DOI] [PubMed] [Google Scholar]

[B11] 11.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550, 2014. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B12] 12.Dell KM. The role of cilia in the pathogenesis of cystic kidney disease. Curr Opin Pediatr 27: 212–218, 2015. doi: 10.1097/MOP.0000000000000187. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B13] 13.Pazour GJ, Quarmby L, Smith AO, Desai PB, Schmidts M. Cilia in cystic kidney and other diseases. Cell Signal 69: 109519, 2020. doi: 10.1016/j.cellsig.2019.109519. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B14] 14.Veland IR, Awan A, Pedersen LB, Yoder BK, Christensen ST. Primary cilia and signaling pathways in mammalian development, health and disease. Nephron Physiol 111: p39–53, 2009. doi: 10.1159/000208212. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B15] 15.Shao L, El-Jouni W, Kong F, Ramesh J, Kumar RS, Shen X, Ren J, Devendra S, Dorschel A, Wu M, Barrera I, Tabari A, Hu K, Haque N, Yambayev I, Li S, Kumar A, Behera TR, McDonough G, Furuichi M, Xifaras M, Lu T, Alhayaza RM, Miyabayashi K, Fan Q, Ajay AK, Zhou J. Genetic reduction of cilium length by targeting intraflagellar transport 88 protein impedes kidney and liver cyst formation in mouse models of autosomal polycystic kidney disease. Kidney Int 98: 1225–1241, 2020. doi: 10.1016/j.kint.2020.05.049. [DOI] [PubMed] [Google Scholar]

[B16] 16.Ma M, Tian X, Igarashi P, Pazour GJ, Somlo S. Loss of cilia suppresses cyst growth in genetic models of autosomal dominant polycystic kidney disease. Nat Genet 45: 1004–1012, 2013. doi: 10.1038/ng.2715. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B17] 17.Zhang C, Balbo B, Ma M, Zhao J, Tian X, Kluger Y, Somlo S. Cyclin-dependent kinase 1 activity Is a driver of cyst growth in polycystic kidney disease. J Am Soc Nephrol 32: 41–51, 2021. doi: 10.1681/ASN.2020040511. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B18] 18.Stringer KD, Komers R, Osman SA, Oyama TT, Lindsley JN, Anderson S. Gender hormones and the progression of experimental polycystic kidney disease. Kidney Int 68: 1729–1739, 2005. doi: 10.1111/j.1523-1755.2005.00589.x. [DOI] [PubMed] [Google Scholar]

[B19] 19.Smith LA, Bukanov NO, Husson H, Russo RJ, Barry TC, Taylor AL, Beier DR, Ibraghimov-Beskrovnaya O. Development of polycystic kidney disease in juvenile cystic kidney mice: insights into pathogenesis, ciliary abnormalities, and common features with human disease. J Am Soc Nephrol 17: 2821–2831, 2006. doi: 10.1681/ASN.2006020136. [DOI] [PubMed] [Google Scholar]

[B20] 20.Lager DJ, Qian Q, Bengal RJ, Ishibashi M, Torres VE. The pck rat: a new model that resembles human autosomal dominant polycystic kidney and liver disease. Kidney Int 59: 126–136, 2001. doi: 10.1046/j.1523-1755.2001.00473.x. [DOI] [PubMed] [Google Scholar]

[B21] 21.Cornec-Le Gall E, Audrezet MP, Renaudineau E, Hourmant M, Charasse C, Michez E, Frouget T, Vigneau C, Dantal J, Siohan P, Longuet H, Gatault P, Ecotiere L, Bridoux F, Mandart L, Hanrotel-Saliou C, Stanescu C, Depraetre P, Gie S, Massad M, Kersale A, Seret G, Augusto JF, Saliou P, Maestri S, Chen JM, Harris PC, Ferec C, Le Meur Y. PKD2-related autosomal dominant polycystic kidney disease: Prevalence, clinical presentation, mutation spectrum, and prognosis. Am J Kidney Dis 70: 476–485, 2017. doi: 10.1053/j.ajkd.2017.01.046. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B22] 22.Johnson AM, Gabow PA. Identification of patients with autosomal dominant polycystic kidney disease at highest risk for end-stage renal disease. JASN 8: 1560–1567, 1997. doi: 10.1681/ASN.V8101560. [DOI] [PubMed] [Google Scholar]

[B23] 23.Schrier RW, Brosnahan G, Cadnapaphornchai MA, Chonchol M, Friend K, Gitomer B, Rossetti S. Predictors of autosomal dominant polycystic kidney disease progression. J Am Soc Nephrol 25: 2399–2418, 2014. doi: 10.1681/ASN.2013111184. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B24] 24.Stewart JH. End-stage renal failure appears earlier in men than in women with polycystic kidney disease. Am J Kidney Dis 24: 181–183, 1994. doi: 10.1016/s0272-6386(12)80179-x. [DOI] [PubMed] [Google Scholar]

[B25] 25.Lima-Posada I, Bobadilla NA. Understanding the opposite effects of sex hormones in mediating renal injury. Nephrology (Carlton) 26: 217–226, 2021. doi: 10.1111/nep.13806. [DOI] [PubMed] [Google Scholar]

[B26] 26.Griffin BR, You Z, Noureddine L, Gitomer B, Perrenoud L, Wang W, Chonchol M, Jalal D, The HI, the HALT Investigators. KIM-1 and kidney disease progression in autosomal dominant polycystic kidney disease: HALT-PKD results. Am J Nephrol 51: 473–479, 2020. doi: 10.1159/000508051. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B27] 27.Kunnen SJ, Malas TB, Formica C, Leonhard WN, 't Hoen PAC, Peters DJM. Comparative transcriptomics of shear stress treated Pkd1(-/-) cells and pre-cystic kidneys reveals pathways involved in early polycystic kidney disease. Biomed Pharmacother 108: 1123–1134, 2018. doi: 10.1016/j.biopha.2018.07.178. [DOI] [PubMed] [Google Scholar]

[B28] 28.Joly D, Morel V, Hummel A, Ruello A, Nusbaum P, Patey N, Noël LH, Rousselle P, Knebelmann B. Beta4 integrin and laminin 5 are aberrantly expressed in polycystic kidney disease: role in increased cell adhesion and migration. Am J Pathol 163: 1791–1800, 2003. doi: 10.1016/s0002-9440(10)63539-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B29] 29.Klingel R, Ramadori G, Schuppan D, Knittel T, Meyer Zum Buschenfelde KH, Kohler H. Coexpression of extracellular matrix glycoproteins undulin and tenascin in human autosomal dominant polycystic kidney disease. Nephron 65: 111–118, 1993. doi: 10.1159/000187451. [DOI] [PubMed] [Google Scholar]

[B30] 30.Zeltner R, Hilgers KF, Schmieder RE, Porst M, Schulze BD, Hartner A. A promoter polymorphism of the alpha 8 integrin gene and the progression of autosomal-dominant polycystic kidney disease. Nephron Clin Pract 108: c169–175, 2008. doi: 10.1159/000116887. [DOI] [PubMed] [Google Scholar]

[B31] 31.Song CJ, Zimmerman KA, Henke SJ, Yoder BK. Inflammation and fibrosis in polycystic kidney disease. Results Probl Cell Differ 60: 323–344, 2017. doi: 10.1007/978-3-319-51436-9_12. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B32] 32.Kistler AD, Serra AL, Siwy J, Poster D, Krauer F, Torres VE, Mrug M, Grantham JJ, Bae KT, Bost JE, Mullen W, Wuthrich RP, Mischak H, Chapman AB. Urinary proteomic biomarkers for diagnosis and risk stratification of autosomal dominant polycystic kidney disease: a multicentric study. PLoS One 8: e53016, 2013. doi: 10.1371/journal.pone.0053016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B33] 33.Liu D, Wang CJ, Judge DP, Halushka MK, Ni J, Habashi JP, Moslehi J, Bedja D, Gabrielson KL, Xu H, Qian F, Huso D, Dietz HC, Germino GG, Watnick T. A Pkd1-Fbn1 genetic interaction implicates TGF-ß signaling in the pathogenesis of vascular complications in autosomal dominant polycystic kidney disease. JASN 25: 81–91, 2014. doi: 10.1681/ASN.2012050486. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B34] 34.Zhang Q, Murcia NS, Chittenden LR, Richards WG, Michaud EJ, Woychik RP, Yoder BK. Loss of the Tg737 protein results in skeletal patterning defects. Dev Dyn 227: 78–90, 2003. doi: 10.1002/dvdy.10289. [DOI] [PubMed] [Google Scholar]

[B35] 35.Menezes LF, Zhou F, Patterson AD, Piontek KB, Krausz KW, Gonzalez FJ, Germino GG. Network analysis of a Pkd1-mouse model of autosomal dominant polycystic kidney disease identifies HNF4alpha as a disease modifier. PLoS Genet 8: e1003053, 2012. doi: 10.1371/journal.pgen.1003053. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B36] 36.Menezes LF, Lin CC, Zhou F, Germino GG. Fatty acid oxidation is impaired in an orthologous mouse model of autosomal dominant polycystic kidney disease. EBioMedicine 5: 183–192, 2016. doi: 10.1016/j.ebiom.2016.01.027. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B37] 37.Console L, Scalise M, Giangregorio N, Tonazzi A, Barile M, Indiveri C. The link between the mitochondrial fatty acid oxidation derangement and kidneyinjury. Front Physiol 11: 794, 2020. doi: 10.3389/fphys.2020.00794. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B38] 38.Wang S, Dong Z. Primary cilia and kidney injury: current research status and future perspectives. Am J Physiol Renal Physiol 305: F1085–F1098, 2013. doi: 10.1152/ajprenal.00399.2013.. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B39] 39.Wang S, Zhuang S, Dong Z. IFT88 deficiency in proximal tubular cells exaggerates cisplatin-induced injury by suppressing autophagy. Am J Physiol Renal Physiol 321: F269–F277, 2021. doi: 10.1152/ajprenal.00672.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B40] 40.Ramunni A, Brescia P, Quaranta D, Bianco MS, Ranieri P, Dolce E, Coratelli P. Cutaneous microcirculation is impaired in early autosomal dominant polycystic kidney disease. Nephron Clin Pract 113: c71–75, 2009. doi: 10.1159/000228537. [DOI] [PubMed] [Google Scholar]

[B41] 41.Astone M, Santoro MM. Time to fight: targeting the circadian clock molecular machinery in cancer therapy. Drug Discov Today 26: 1164–1184, 2021. doi: 10.1016/j.drudis.2021.01.023. [DOI] [PubMed] [Google Scholar]

[B42] 42.Terabayashi T, Germino GG, Menezes LF. Pathway identification through transcriptome analysis. Cell Signal 74: 109701, 2020. doi: 10.1016/j.cellsig.2020.109701. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Multiomic identification of factors associated with progression to cystic kidney disease in mice with nephron Ift88 disruption

Chunyan Hu

Katherine Beebe

Edgar J Hernandez

Jose M Lazaro-Guevara

Monica P Revelo

Yufeng Huang

J Alan Maschek

James E Cox

Donald E Kohan

Abstract

INTRODUCTION

METHODS

Animal Care

Generation of Inducible Nephron-Specific Ift88 KO Mice

Genotyping

Histology

Western Blot Analysis

Lipid and Metabolite Extraction and Analysis

Protein Extraction and Analysis

RNA Extraction, Sequencing, and Analysis

Quantitative Real-Time PCR

Statistics

Figure 1.

Figure 2.

Table 1.

Figure 6.

RESULTS

Effect of Ift88 KO on Mouse Kidney mRNA Expression

Figure 3.

Effect of Ift88 KO on Key Renal ECM Components

Figure 4.

Table 2.

Figure 5.

Identification of Putative mRNA Biomarkers for Male Ift88 KO

Effect of Ift88 KO on the Renal Proteomic Profile

Figure 7.

Figure 8.

Effect of Ift88 KO on the Renal Metabolomic Profile

Figure 9.

Figure 10.

Table 3.

Effect of Ift88 KO on the Renal Lipidomics Profile

Figure 11.

Table 4.

DISCUSSION

Perspectives and Significance

GRANTS

DISCLOSURES

AUTHOR CONTRIBUTIONS

ENDNOTE

ACKNOWLEDGMENTS

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