The tryptophan-metabolizing enzyme indoleamine 2,3-dioxygenase 1 regulates polycystic kidney disease progression

Autosomal dominant polycystic kidney disease (ADPKD), the most common monogenic nephropathy, is characterized by phenotypic variability that exceeds genic effects. Dysregulated metabolism and immune cell function are key disease modifiers. The tryptophan metabolites, kynurenines, produced through indoleamine 2,3-dioxygenase 1 (IDO1), are known immunomodulators. Here, we study the role of tryptophan metabolism in PKD using an orthologous disease model (C57BL/6J Pkd1RC/RC). We found elevated kynurenine and IDO1 levels in Pkd1RC/RC kidneys versus wild type. Further, IDO1 levels were increased in ADPKD cell lines. Genetic Ido1 loss in Pkd1RC/RC animals resulted in reduced PKD severity, as measured by cystic index and percentage kidney weight normalized to body weight. Consistent with an immunomodulatory role of kynurenines, Pkd1RC/RC;Ido1–/– mice presented with significant changes in the cystic immune microenvironment (CME) versus controls. Kidney macrophage numbers decreased and CD8+ T cell numbers increased, both known PKD modulators. Also, pharmacological IDO1 inhibition in Pkd1RC/RC mice and kidney-specific Pkd2-knockout mice with rapidly progressive PKD resulted in less severe PKD versus controls, with changes in the CME similar to those in the genetic model. Our data suggest that tryptophan metabolism is dysregulated in ADPKD and that its inhibition results in changes to the CME and slows disease progression, making IDO1 a therapeutic target for ADPKD.


Histomorphometric analysis
Image acquisition and analysis was performed as previously described (4). Cystic index, cyst size, and cyst number were analyzed using a custom-built NIS-Elements AR v4.6 macro (Nikon) using three cross-sections per kidney (two poles and center/pelvis). A cyst was defined as having a minimum feret diameter of 50μm. The cystic index was defined as the percentage of cystic area per kidney cross section, and cyst number was normalized to area. Fibrotic area was analyzed from picrosirius red stained kidney sections and visualized using an Olympus BX41 microscope with a linear polarizer. Ten random cortical 40x images were analyzed per animal and the percent fibrotic area was calculated from total kidney tissue area. Fibrillar collagen (birefringent area) was quantified using ImageJ. Computed volumes were calculated by multiplying the obtained indices (cystic or fibrotic) with the kidney weight of the same animal.

Western blotting
Kidney tissue was homogenized in lysis buffer containing RIPA and Protease Inhibitor Cocktail (Sigma Aldrich, #P8340) using a Qiagen TissueLyser LT homogenizer. Protein concentration was measured using Protein Assay Dye Reagent Concentrate (Bio-Rad, #5000006) and 30µg of samples were loaded onto 4-12% acrylamide gels and ran for 1 hour at 200 V. Cultured cell lysates and human cyst lysates were loaded at 30µg protein per well. Gels were transferred to PVDF membranes at 400 mAmps for 3 hours followed by blocking in 5% BSA. Grove, PA) were used. Primary antibodies were prepared in 5% nonfat dry milk and membranes incubated overnight with gentle agitation in a 4°C refrigerator. Secondary antibodies were prepared in 5% BSA and membranes incubated for 1h at room temperature. Membranes were washed three times in 1X TBST and exposed to Western Lightning Plus-ECL substrate (PerkinElmer, #NEL104001EA). Blots were developed by chemiluminescence and densitometry on X-ray films was quantified using ImageJ software.

Data analysis
Flow cytometry data were analyzed using the Kaluza Analysis v2.1 software (Beckman Coulter).
First, compensation for each channel was performed using single-stained beads and confirmed using single stained pooled cell mix. All samples were then analyzed using the gated workflow shown in Supplemental Figure 9 in a blinded manner.

Metabolomics -Liquid chromatography tandem mass spectrometry (LC/MS-MS)
Semi-quantitative targeted metabolomics Sample analysis was performed based on a validated approach (15,16). Kidneys were perfused with ice cold PBS/heparin, and kidneys were dissected and snap frozen in liquid nitrogen.
Following, kidney tissue samples (~50-100 mg) were homogenized in an adequate volume of 80% (v/v) cooled methanol, incubated for protein precipitation, dried in a SpeedVac concentrator centrifuge (Savant, ThermoFisher), and reconstituted in water/methanol. 8 μL of sample was injected onto an Amide XBridge HPLC column (3.5 μm; 4.6 mm inner diameter (i.d.) × 100 mm length; Waters). The mobile phases consisted of HPLC buffer A (pH = 9.0: 95% [vol/vol] water, 5% [vol/vol] acetonitrile, 20 mM ammonium hydroxide, 20 mM ammonium acetate) and HPLC buffer B (100% acetonitrile). The HPLC settings were as follows: from 0 to 3 minutes, the mobile phase was kept at 85% B; from 3 to 22 minutes, the percentage of solvent B was decreased from 85% to 2% and was kept at 2% for an additional 3 minutes. At minute 26, solvent B was increased again back to 85% and the column flushed for an additional 7 minutes at 85% solvent B.
The Q1 (precursor ion) and Q3 (fragment ion) transitions, the metabolite names, dwell times and the appropriate collision energies (CEs) for both positive and negative ion modes were adapted from(15) with several additional transitions. Q1 and Q3 transitions were set to unit resolution for optimal metabolite ion isolation and selectivity. In addition, the polarity switching (settling) time was set to 50 ms; in 1.42 s using a 3-ms dwell time, we were able to obtain 6-14 scans per metabolite peak. The source temperature was set at 500°C, curtain gas (CUR, nitrogen) at 20, collision gas (CAD, nitrogen) at high, ion source gases 1 and 2 at 33, declustering potential (DP) at +93/-93, entrance potential (EP) at +10/-10, and collision cell exit potential (CXP) at +10/-10 for positive and negative ion modes, respectively. Positive identification of the metabolites of interest was performed through injection of pure compound standards onto the above-described   (18). Relative peak intensities were initially normalized to the deuterated internal standards followed by the sum of all integrals and tissue weight. After that, data were log transformed and then Paretto-scaled (mean centered and divided by the square root of the SD of each variable). ANOVA with post-hoc Tukey HSD was used to compare group differences.

Analysis of changes in metabolites between different animal groups was performed by utilizing
Partial Least Squares-Discriminant Analysis (PLS-DA). False discovery rate (FDR) correction was applied to correct for multiple comparisons (FDR < 0.05 for statistical significance).

Statistical analysis
All analyses were performed using PRISM9 (Graphpad Software). Data are depicted as mean ±