1) Describe what is meant by “host specificity of the ectomycorrhizal community” in this article. Why would a change in the aboveground tree community change the belowground fungal community? 2) Why w
Increased urinary osmolyte excretion indicates chronic kidney disease severity and progression rate Ryan B. Gil 1, Alberto Ortiz 2, Maria D. Sanchez-Ni~ no 2, Katerina Markoska 3, Eva Schepers 4, Raymond Vanholder 4, Griet Glorieux 4, Philippe Schmitt-Kopplin 1,5,6 and Silke S. Heinzmann 1 1Helmholtz Center Munich, German Research Center for Environment Health, Research Unit Analytical BioGeoChemistry, Neuherberg, Germany, 2IIS-Fundacion Jimenez Diaz UAM, Madrid, Spain, 3University of Skopje, Faculty of Medicine, Skopje, Macedonia, 4Department of Internal Medicine, Nephrology Division, Ghent University Hospital, Ghent, Belgium, 5German Center for Diabetes Research (DZD), Neuherberg, Germany and 6Technical University Munich, Chair of Analytical Food Chemistry, Freising-Weihenstephan, Germany Correspondence and offprint requests to: Silke S. Heinzmann; E-mail: [email protected] and Philippe Schmitt-Kopplin; E-mail: [email protected] ABSTRACT Background.Chronic kidney disease (CKD) is a recognized global health problem. While some CKD patients remain stable after initial diagnosis, others can rapidly progress towards end- stage renal disease (ESRD). This makes biomarkers capable of detecting progressive forms of CKD extremely valuable, espe- cially in non-invasive biofluids such as urine. Screening for metabolite markers using non-targeted metabolomic techni- ques like nuclear magnetic resonance spectroscopy is increas- ingly applied to CKD research. Methods.A cohort of CKD patients (n¼227) with estimated glomerular filtration rates (eGFRs) ranging from 9.4–130 mL/ min/1.73 m 2was evaluated and urine metabolite profiles were characterized in relation to declining eGFR. Nested in this cohort, a retrospective subset (n¼57) was investigated for prognostic metabolite markers of CKD progression, independent of baseline eGFR. A transcriptomic analysis of murine models of renal fail- ure was performed to validate selected metabolomic findings. Results.General linear modeling revealed 11 urinary metabo- lites with significant associations to reduced eGFR. Linear mod- elling specifically showed that increased urine concentrations of betaine (P<0.05) andmyo-inositol (P<0.05) are significant prognostic markers of CKD progression. Conclusions.Renal organic osmolytes, betaine andmyo- inositol play a critical role in protecting renal cells from hyperosmotic stress. Kidney tissue transcriptomics of murine preclinical experimentation identified decreased expression of Slc6a12 and Slc5a11 mRNA in renal tissue consistent with defective tubular transport of these osmolytes. Imbalances in renal osmolyte regulation lead to increased renal cell damage and thus more progressive forms of CKD. Increases in renal osmolytes in urine could provide clinical diagnostic and prog- nostic information on CKD outcomes.Keywords:CKD, disease progression, metabolomics, osmo- lytes, transcriptomics, urine INTRODUCTION Chronic kidney disease (CKD) is a term used to include a wide range of diseases that reduce kidney function. The National Kidney Foundation defines CKD as 3monthsof kidney damage or an estimated glomerular filtration rate (eGFR)<60 mL/min/1.73 m 2[1]. Disease aetiology in CKD is diverse, leading to variability in morbidity and mortality [2]. Further, not all populations have an equal risk for developing CKD and progressing towards end-stage renal disease (ESRD); therefore, high-risk populations with diabetes and/or hyperten- sion should receive targeted testing for CKD [2]. eGFR can be calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation or the Modification of Diet in Renal Disease (MDRD) study equation [1,2]. However, both measures are relatively insensitive in early stage CKD and their prognostic value is limited. Novel biomarkers that are more sensitive in early stage CKD are needed for diagnosis and prognosis of CKD pro- gression [3]. Metabolomics can analyse biofluids for specific metabolic signatures reflecting CKD status [4], offering prom- ising non-invasive methods to screen, diagnose and prognose patients for CKD [5–7]. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are the two main analytical plat- forms used in metabolomics [8]. Here we use NMR spectro- scopy as a quantitative state-of-the-art analytical platform that is widely used in metabolome studies [9]. NMR can detect a wide range of metabolites from various chemical classes, delivering robust transferable data under optimized conditions [10]. VCThe Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. ORIGINAL ARTICLE 1 Nephrol Dial Transplant (2018) 1–9 doi: 10.1093/ndt/gfy020 Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 The purpose of this study was to use NMR-based metabolo- mic strategies to elucidate novel urinary markers supportive of current methods for diagnosing CKD. Urine samples from a cohort of 227 patients in various stages of CKD were utilized. An additional aim was to identify prognostic markers that indi- cate patients at risk of CKD progression towards ESRD. A nested retrospective subgroup (n¼57) from the cohort was analysed using follow-up eGFR measurements and a calculated rate of CKD progression. Results of these analyses point to increased urine concentrations of renal osmolytes as markers of reduced eGFR. These compounds were also significantly elevated in the urine of CKD patients with progressive forms of CKD. These metabolite data were supported with messenger RNA (mRNA) data generated from independent murine kidney fibrosis and injury experimentation. MATERIALS AND METHODS Study cohort and sampling The cohort consisted of patients from the Nephrology Outpatient Clinic at Ghent University Hospital, Ghent, Belgium. Samples were collected in the period 17 January 2011 to 7 July 2015. eGFR was determined using the CKD-EPI equa- tion. Important metadata used in the analysis can be found in Table 1. Sample collection was performed in accordance with local ethics requirements and in accordance with the current revision of the Helsinki Declaration. A total of 57 patients had sufficient follow-up data to moni- tor their respective rate of CKD progression (i.e. 3eGFR measurements, minimum of 2-years duration). To reduce con- founding factors, only samples from patients with systemic dis- eases or glomerular diseases were used, and patients with renal transplantation or receiving renal replacement therapy were not considered (Supplementary Table S1). The percent annual eGFR slope change from baseline was calculated using thefollowing equation, an approach that has also been previously applied [11]: % annual eGFR slope¼ linear coef f icient baseline eGFR 100% Sample preparation for NMR and instrumental protocol Upon collection, urine samples were immediately centri- fuged (20800gfor 10 min), aliquoted per 1 mL and stored at 80 C. Following defrosting, a buffering approach designed for NMR analysis in urine was applied [12]. This included a deute- rium oxide phosphate buffer (1.5 M PO 4,pH7.4;Armar Chemicals, Leipzig, Germany) with potassium fluoride (KF, 300 mM) and (trimethylsilyl)propionic acid (TSP, 0.1%). Phosphate salts, KF and TSP were purchased from Sigma Aldrich Chemie (Steinheim, Germany). Urine samples were analysed on a Bruker 800-MHz spec- trometer operating at 800.35 MHz with a quadrupole inverse cryogenic probe. A standard one-dimensional pulse sequence [recycle delay (RD) 90 ,t190 ,tm90 , acquire FID] was acquired, with water suppression irradiation during RD of 2 s, mixing time (tm) set on 200 ms and a 90 pulse set to 10.13ls, collecting 512 scans into 64 000 data points with a spectral width of 12 ppm. All spectra were manually phased, baseline corrected and calibrated to TSP (d0.00) with TopSpin 3.2 (Bruker BioSpin, Rheinstetten, Germany). Data were imported to MATLAB (MathWorks, Natick, MA, USA) and further proc- essed, i.e., water and urea region removed (d4.7–5.6). Daily quality control samples (i.e. pooled urine sample) were meas- ured throughout the cohort measurement to monitor instru- ment performance, data processing (i.e. spectra pre-processing, normalization and alignment) and metabolite stability. The structural identity of metabolite compounds was deter- mined by two-dimensional (2D) NMR methods; total correla- tion spectroscopy (TOCSY) and heteronuclear single quantum coherence (HSQC) spectroscopy. For 2D TOCSY spectra, phase-sensitive and sensitivity-improved 2D TOCSY with WATERGATE (3-9-19) and DIPSI-2 were acquired. For each spectrum 19228 1024 data points were collected using 32 scans per increment, 16 dummy scans and 1 s acquisition time. The spectral widths were set to 12 ppm for both the F1 and F2 dimensions. For 2D HSQC spectra, phase-sensitive ge-2D HSQC using PEP and adiabatic pulses for inversion and refo- cusing with gradients was used. Data points of 4788 1280 per spectra were collected with 256 scans per increment, 16 dummy scans and an acquisition time of 0.25 s. Spectral widths were set to 12 ppm for the F2 and 230 ppm for F1. NMR-based statistical tools Data processing and statistics were performed in MATLAB 2015b, RStudio and the MetaboAnalyst platform (http://www. metaboanalyst.ca) [13]. Probabilistic quotient normalization [14] was used for normalization of spectral data to account for biological variation in urine dilution. This method is an improved normalization technique compared with single- metabolite normalization [15], which is superior in the context of CKD, as it avoids normalization to metabolites affected by Table 1. Urine sample cohort (n¼227) Male,n(%) 137 (60.4) Age (years) 62.3616.8 (17–88) Disease groups (n, as defined by ERA-EDTA Primary Renal Disease Registry) Tubulointerstitial diseases 35 Systemic diseases affecting the kidney a 96 Familial/hereditary nephropathies 23 Miscellaneous renal disorders 41 Glomerular diseases 32 Body mass index (kg/m 2) 28.065.5 (16.3–47.9) CKD stage (CKD-EPI, mL/min/1.73 m 2),n Stage 1 (eGFR>90) 25 Stage 2 (eGFR 89–60) 45 Stage 3 (eGFR 59–30) 115 Stage 4 (eGFR 29–15) 34 Stage 5 (eGFR<15) 8 eGFR (mL/min/1.73 m 2)51625.7 (9.4–130) This table provides an overview of cohort metadata used for biomarker discovery. Age, gender, BMI and CKD disease groups were determined to be the most important meta- data in linear modelling of metabolites. For age, BMI and eGFR, the mean and standard deviation are shown with the range. aSystemic diseases includes diabetes type 2 and hypertension. 2R.B. Gilet al. Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 the disease. Spectral data were aligned using the recursive seg- ment-wise peak alignment (RSPA) algorithm [16]. An orthogo- nal partial least squares (OPLS) regression analysis [17]using eGFR as theyvariable was performed for spectral feature selection. Spectral regions most affected by CKD display high covariance andr 2values. Selected spectral features were struc- turally identified using 2D NMR and matched with the human metabolite database (HMDB) [18]. Identified metabolites were quantified with the R-package BATMAN [19], measuring the area under the curve. Data were then log transformed and mean centered. General linear modelling was applied to each identified metabolite with baseline eGFR or percent annual eGFR slope asyvariables, adjusted for age, gender, body mass index (BMI) and disease group. Kidney transcriptomics Total RNA was isolated using the PureLink RNA Mini Kit (Invitrogen, Paisley, UK). Affymetrix transcriptomics arrays of kidney tissue (n¼3 control andn¼3 kidney injury, obtained 24 h after induction of kidney injury by a folic acid injection or injection of vehicle) were performed at Unidad Geno´mica Moncloa, Fundacio´n Parque Cientı´fico de Madrid, Madrid, Spain, following the manufacturer’s protocol [20]. Image files were initially obtained through Affymetrix GeneChip Command Console software. Robust multichip analysis was performed using the Affymetrix Expression Console software. Starting from the normalized robust multichip analysis, the sig- nificance analysis of microarrays was performed using the limma package (Babelomics, http://www.babelomics.org), with a false discovery rate (FDR) of 5% to identify genes significantly dysregulated. Experimental kidney injury These studies were approved by the IIS-FJD animal ethics committee and followed Directive 2010/63/EU on the protec- tion of animals used for scientific purposes. For experimental kidney injury, female 12- to 14-week-old C57/BL6 mice received a single intraperitoneal injection of folic acid (Sigma Aldrich) 250 mg/kg in sodium bicarbonate 0.3 mol/L (acute kidney injury,n¼3) or a vehicle alone (controls,n¼3) and were sacrificed 24 h later as previously described [20]. Kidneys were cold saline perfusedin situbefore removal. One kidney from each mouse was fixed in buffered formalin, embedded in paraffin and stained with haematoxylin and eosin. The other kidney was snap frozen in liquid nitrogen for ribonucleic acid (RNA) studies. Samples from this experiment were used for kidney transcriptomics. A second set of six mice per group with folic acid nephropathy or vehicle-infused controls were proc- essed in the same manner at 24 and 72 h following the induc- tion of kidney injury and used for real-time quantitative polymerase chain reaction (qPCR) validation. Unilateral ureteral obstruction (UUO) was performed under isoflurane-induced anaesthesia. The left ureter was ligated with silk at two locations and cut to prevent urinary tract infection (obstructed kidney) [21]. A total of five male 12- to 14-week- old C57/BL6 mice were sacrificed 14 days after surgery. UUO kidneys were compared with the healthy contralateral kidneys.Real-time qPCR validation Human metabolomics and murine transcriptomics results were validated by real-time qPCR performed using mRNA obtained from five UUO mice sacrificed on Day 14 and from folic acid nephropathy mice vehicle-infused controls obtained at 24 and 72 h following induction of kidney injury. RNA of 1mg isolated by Trizol (Invitrogen) was reverse transcribed with a High Capacity cDNA Archive Kit and real-time qPCR was per- formed on a ABI Prism 7500 PCR system (Applied Biosystems, Foster City, CA, USA) using theDDCt method [22]. Expression levels are displayed as ratios to glyceraldehyde-3-phosphate dehydrogenase. Pre-developed primer and probe assays were also from Applied Biosystems. Data mining The Nephroseq [http://www.nephroseq.org/ (11 October 2017, date last accessed)] database was searched for human renal biopsy transcriptomics data comparing patients with diverse aetiologies of CKD with controls. The database was searched for gene expression data forSlc5a11,Slc6a12andSlc5A3. Supplementary Figure S1shows the experimental workflow. RESULTS Urine metabolite markers related to eGFR An OPLS regression analysis of the entire cohort data set measured by 1H NMR revealed spectral features with significant linear relationship to eGFR (Figure 1). Spectral features with the largest absolute covariance andr 2values (Figure 1C) were targeted for identification and quantification (Figure 1A). A list of the identified metabolites can be found inSupplementary Table S2. General linear modelling of all quantified metabolites showed 11 urinary metabolites having significant linear associa- tions with eGFR, adjusted for age, gender, BMI and disease group (Table 2). A positive association with decreased eGFR reflects a decrease in the relative concentration of each respec- tive metabolite as eGFR declines. Metabolites from various metabolic pathways were altered with reduced eGFR. Citric acid from the tricarboxylic acid cycle, amino acids (e.g. threonine), lipid metabolism (e.g. ethanol- amine), gut microbiome–derived uraemic toxins (e.g. indoxyl sulphate andp-cresol sulphate), uracil and glycolic acid were significantly lower with reduced eGFR. Many of these metabo- lites have also been reported as significant markers of CKD from previous studies [23–25], further strengthening them as reliable biomarkers. Additionally, two metabolites,myo-inositol and betaine, had negative coefficients, indicating a respective urinary increase with reduced eGFR. Metabolite markers related to CKD progression A nested retrospective subgroup (n¼57) of the cohort was analysed for urine metabolites predictive of CKD progression. These CKD patients were well distributed throughout the range of progression (r 2¼0.96), which was from 5% per year (i.e. improve- ment in eGFR) to 14.1% per year (i.e. strong deterioration) (Supplementary Figure 2). The percent annual eGFR slope was independent of baseline eGFR (P¼0.44) (Supplementary Figure Increased urinary osmolyte excretion in CKD progression 3 Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 S3). Linear modelling was again applied using the percent annual eGFR slope change as theyvariable. A total of eight metabolites showed significant associations (Table 3). Citric acid, glycolic acid and ethanolamine were again seen to be positively associated with a declining percent annual eGFR slope. Creatinine and dimethyl- amine were also positively associated. The highestbcoefficients in the modelling were seen for betaine andmyo-inositol, which had negative correlations (Figure 2). These osmolytes again showed opposite trends from all other significant metabolites. Duetothelongitudinalnatureofthesamplegroup,theprevi- ously mentioned metabolites may act as prognostic urinary bio- markers of CKD outcome. We focused on to examining the renal osmolytes betaine andmyo-inositol, as their respective trends were contrary to the other metabolites and their physiological relevance in kidney health is unique. Using receiver operatingcharacteristics (ROC) analysis, the values of betaine andmyo- inositol were examined in the first and last quartiles of the CKD progression subgroup. Here, the first quartile (i.e. ‘stable’) showed no CKD progression, with an eGFR change ofþ5% to 1% per year. The last quartile (i.e. ‘rapid’ progression) showed an eGFR change of 8% to 15% per year. The area under the curve (AUC) of the ROC analysis for betaine was 0.814 and formyo- inositol was 0.781. Combined, both betaine andmyo-inositol showed an AUC of 0.84 (Supplementary Figure S4). Decreased kidney expression of betaine andmyo- inositol transporters in murine models To determine if osmolyte transporters were a factor for increased urine osmolyte concentrations, kidney transcriptom- ics of a murine CKD model were analysed for gene expression 5 . 11 5 . 22 5 . 33 5 . 44 0 1 2 3 4 5 6 7 8 9 65. 5 75. 6 85. 7 95. 8 9.5 0 0.51 1.52 2.53 3.54 4.55 2 1 3 3 4 5 6 7 32 9 11 13 12 14 15 16 17 18 19 20 21 22 23 24 25 25 25 26 27 28 29 30 38 31 35 35 36 3738 38 39 41 42 43 8 8 28 34 40 Signal intensity (a.u.) 65. 5 75. 6 85. 7 95. 8 9.5 -1 1 0.8 0.6 0.4 0.6 0 -0.2 -0.4 -0.6 -0.8 OPLS-coefficient (a.u.) 5 . 11 5 . 22 5 . 33 5 . 44 ppm 0 0.05 0.1 0.15 0.2 0.25 R2 A C 24 2413 -100 -80 -60 -40 -20 0 20 40 60 80 100-150 -100-500 50 100 150 20 30 40 50 60 70 80 90 100 11 0 120 eGFR T[1] TYosc B 3.14 3.16 3.15 4.092 4.08 4.086 2.6 2.7 2.5 x10-3 44 10 40 33 24 FIGURE 1: OPLS regression analysis for feature selection in NMR spectra.(A)Sample spectrum with identified metabolites labelled: (1) 2-furoyl- glycine, (2) 2-hydroxyisobutyric acid, (3) 3-hydroxyisovaleric acid, (4) 4-deoxyerythronic acid, (5) 4-deoxytheonic acid, (6) acetic acid, (7) acetone, (8) trigonelline, (9) betaine, (10)cis-aconitic acid, (11) citric acid, (12) creatine, (13) creatinine, (14) D-glucose, (15) dimethylamine, (16) dimethly- glycine, (17) ethanolamine, (18) formic acid, (19) fumaric acid, (20) glycine, (21) glycolic acid, (22) guanidoacetic acid, (23) pseudouridine, (24) hippuric acid, (25) indoxyl sulfate, (26) L-acetylcarnitine, (27) L-alanine, (28) trimethylamine, (29) L-lactic acid, (30) L-phenylalanine, (31)L- threonine, (32) trimethylamine-N-oxide, (33) L-tyrosine, (34) uracil, (35)myo-inositol, (36)N-methylnicotinamide, (37)p-cresol sulfate, (38) phe- nylacetylglutamine, (39) phosphorylcholine, (40)p-hydroxyphenylacetic acid, (41) proline betaine, (42) succinic acid, (43) tartaric acid and (44) taurine.(B)Loadings plot of OPLS regression analysis with eGFR in theyvariable. This plot represents a ‘statistical’ pseudo-spectrum that displays the OPLS model covariance on they-axis and chemical shift on thex-axis. The colour bar represents ther 2value for that spectral region to eGFR. Plots in (A) and (B) are aligned on thex-axis, which shows which spectral regions may best describe CKD-related changes and warrant structural identification.(C)Scores plot of the OPLS model showing data separation based on sample eGFR (colour bar). 4R.B. Gilet al. Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 changes. The kidney gene expression ofSlc5a3,Slc5a11and Slc6a12(seeFigure 3) was assessed by real-time qPCR in experi- mental kidney fibrosis induced by UUO, a classic murine model of renal fibrosis and inflammation that recapitulates all the keyevents in CKD leading to fibrosis [26]. Significant downregula- tion of the genes encoding themyo-inositol transporter (Slc5a11) and the betaine transporter (Slc6a12) were observed (Figure 4A). The candidate genes (Slc5a3,Slc5a11,Slc6a12) tested in experimental CKD were also observed in a transcriptomics analysis of kidney injury using a murine model injected with folic acid.Supplementary Table S3shows all differentially expressed genes in the transcriptomics array with a fold change increasing or decreasing in expression>25% and FDR<5%, as previously reported [27]. Our hypothesis-driven analysis of the transcriptomics database identified decreased expression of Slc6a12,Slc5a3andSlc5a11(Figure 4B). Downregulation of these genes did not appear to result from the downregulation of the transcription factor tonicity-responsive enhancer-binding protein (TonEBP/NFAT5), a master regulator of osmolyte transporters, since TonEBP/NFAT5 expression was preserved (fold change 0.96, FDR¼ns). Additionally,Myox, the gene encodingmyo-inositol oxygenase, was also downregu- lated in kidney injury (Supplementary Figure S5). Real-time qPCR confirmed in an independent validation experiment of the decreased expression ofSlc5a3,Slc5a11andSlc6a12mRNA at 24 h of kidney injury induction and persisting after 72 h (Figure 4C). These data suggest that these gene expression changes are shared by both CKD and acute kidney injuries. DISCUSSION In the present study we investigated the urine metabolome signatures of patients with CKD and CKD progression. Most significant metabolites decrease in urinary concentration with reduced eGFR and in patients with progressing CKD. However, in these same patients we also observed significant linear increases ofmyo-inositol and betaine. Both metabolites had the highest absolutebcoefficients in our modelling of percent annual eGFR slope and showed prognostic value in predicting patients who rapidly progress from those who remained stable. Kidney transcriptomics identified defective gene expressions ofmyo-inositol and betaine transporters in Table 3. Significant prognostic metabolites of CKD progression MetabolitebSE P-value Creatinine 0.059 0.011 3.57E-06 Citric acid 0.079 0.027 0.005 Ethanolamine 0.039 0.014 0.010 Betaine 0.153 0.057 0.010 TrimethylamineN-oxide 0.053 0.022 0.022 Dimethylamine 0.025 0.011 0.025 Glycolic acid 0.09 0.041 0.031 myo-Inositol 0.091 0.042 0.035 This table shows general linear modelling results for prognostic markers of CKD pro- gression. These metabolites show significant linear associations with percent annual eGFR slope of the selected progression subset. Thebcoefficient, standard error (SE) and P-value of each metabolite are reported and are adjusted for age, gender, BMI and CKD disease group A negative association (i.e. negativeb) indicates an increase in the metab- olite concentration as the percent annual eGFR slope decreases (i.e. CKD progression rate increases). Table 2. Significant diagnostic metabolites of eGFR MetabolitebSE P-value Citric acid 0.023 0.003 3.42E-10 Uracil 0.02 0.004 3.25E-07 Formic acid 0.01 0.002 4.76E-05 L-threonine 0.016 0.005 3.15E-03 Ethanolamine 0.004 0.001 3.61E-03 myo-Inositol 0.014 0.005 6.12E-03 Glycolic acid 0.014 0.005 1.14E-02 Indoxyl sulphate 0.009 0.004 1.53E-02 Hippuric acid 0.011 0.004 1.57E-02 p-Cresol sulphate 0.014 0.006 3.03E-02 Betaine 0.013 0.006 4.79E-02 This table shows general linear modelling results for diagnostic markers of baseline eGFR. These metabolites show significant linear associations with eGFR. Thebcoeffi- cient, standard error (SE) and P-value for each metabolite are reported and are adjusted for age, gender, BMI and CKD disease group. Metabolites with a negative correlation (i.e. negativeb) indicate metabolites that have increased urinary concentration as eGFR declines. FIGURE 2: Plots of adjusted generalized linear model for renal osmolytes. A negative percent annual eGFR slope indicates patients that are progressing in CKD. The linear model equation, 95% confidence intervals and P-values are defined in the legend. Increased urinary osmolyte excretion in CKD progression 5 Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 FIGURE 3: Function of gene products in tubular cells.Slc6a12encodes the betaine transporter bgt-1.Slc5a3encodes the sodiummyo-inositol transporter Smit.Slc5a11encodes the sodiummyo-inositol transporter Smit2.Myoxencodesmyo-inositol oxygenase, which is involved inmyo- inositol synthesis. Transcription factor TonEBP/Nfat5 regulates gene expression of all osmolyte transporter genes; (Slc6a11,Slc5a3,andSlc5a11). Vehicle 24h 72h Kidney injury mRNA (% increase over vehicle control) * ** 0 20 40 60 80 100 120 140 Vehicle 24h72h Kidney injury * ** 0 20 40 60 80 100 120 Ve h i c l e24h 72h Kidney injury * * 0 20 40 60 80 100 120 140 C 0 50 100 150 200 250 300 350 400 1 0 200 400 600 800 1000 1200 1400 1600 0 50 100 150 200 250 Vehicle Vehicle Kidney Injury Arbitrary units Kidney Injury Vehicle Kidney Injury B * Contralateral KidneyObstructed Kidney Contralateral KidneyObstructed Kidney ** Contralateral KidneyObstructed Kidney mRNA Slc6a12Slc5a3 Slc5a11 A * * ** 0 204060 80 100120 140 0 2040 60 80 100120140160 0 2040 60 80 100 120 (% increase over vehicle control) FIGURE 4: Gene expression data. (A) Changes in experimental kidney fibrosis assessed by real-time qPCR. Kidney mRNA was measured 14 days after induction of UUO leading to kidney fibrosis. Obstructed kidneys were compared with contralateral unobstructed kidneys. (B)Changesin experimental kidney injury transcriptomics. Kidney mRNA was measured 24 h after induction of kidney injury by a single injection of folic acid and in vehicle-injected controls. (C) Changes in experimental kidney injury assessed by real-time qPCR. Kidney mRNA was measured 24 h and 72 h after induction of kidney injury by a single injection of folic acid and in vehicle-injected controls. *P 0.05, **P 0.01. 6R.B. Gilet al. Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 murine kidney disease models, providing convincing evi- dence that elevated osmolyte concentrations seen in the urine are partially a result of perturbed renal osmolyte transport. Figure 5provides a proposed model of osmolyte dysregula- tion during CKD. This may be the first demonstration that links urinary osmolyte concentrations with clinical CKD outcomes using a metabolomic analysis supported with transcriptomics. The osmolality of mammalian blood is normally kept con- stant at 290 mosmol/kg [28] and maintained by regulatory mechanisms such as the renin–angiotensin–aldosterone and antidiuretic hormone systems. However, normal kidney func- tion requires an interstitial osmotic gradient in renal medulla four times that of plasma osmolality. The hyperosmotic condi- tions created by NaCl and urea are required for urine concen- tration. However, these conditions can be damaging to cells and proteins [28,29] and cause water to diffuse out of cells, resulting in cellular shrinkage [30]. Osmolytes have evolved in renal tis- sues as protective compounds against osmotic stress [31], and studies have explored mechanisms for how osmolytes provide protein stability [32,33]. Five protective osmolytes are used in renal cells: sorbitol, betaine,myo-inositol, taurine and glycero- phosphocholine (GPC), with a large proportion being betaine andmyo-inositol [28,29]. The concentrations of these osmo- lytes are regulated through the TonEBP/NFAT5 transcription factor [28,34], which regulates the transcription of alltransporter proteins responsible for osmolyte uptake in renal cells. This transcription factor is critical to kidney health and has been proven essential in knockout models [30,35]. Mechanisms for betaine uptake in medullary cells are well studied [28,36]. As osmolality increases, so too do medullary betaine levels [28,37]. Uptake comes primarily from extracellu- lar sources and not from increased local cellular synthesis [28]. Betaine is freely filtered by glomeruli and actively reabsorbed by renal tubules at levels>95% of the filtered load [38,39] (Figure 5A). Therefore, non-perturbed reabsorption mecha- nisms for betaine should not allow for an increase in urinary levels as seen in our study. The primary transporter of betaine in medullary cells is betaine/GABA transporter 1 (Bgt-1) [28,40], which is encoded by theSlc6a12gene and regulated by TonEBP/NFAT5 [30]. Hypertonicity leads to increased TonEBP/NFAT5 expression and localization of Bgt-1 to apical and basolateral plasma membranes in tubular cells (Figure 5B). It is estimated that betaine could account for 25% of the total osmolyte content [40, 41], and a recent study found that betaine ranked high in protein-stabilizing properties [32]. Its high abundance and stabilizing properties provide evidence that betaine is one of the more important organic osmolytes in medullary tissues. A recent study of acute renal ischaemia found kidney tissue had reduced levels of betaine andmyo-inositol when compared with controls [42,43]. Therefore, increased betaine in the urine most likely reflects CKD-related renal stress FIGURE 5: Hypertonicity response mechanism in CKD. A proposed model of the osmolyte mechanism in CKD.(A)Model of osmolyte reab- sorption in a healthy nephron.(B)Tubular cell response mechanism to increased hypertonicity in medullary tissue.(C)Blood and urine osmo- lyte concentrations in response to hypertonicity. Patients with progressive forms of CKD have increased osmolyte concentrations in urine. Increased urinary osmolyte excretion in CKD progression 7 Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 (Figure 5C). Our data show an increase of betaine in human urine during CKD, most likely due to Bgt-1 transporter downregulation, given the lower gene expression in experimental kidney fibrosis and injury. However, the release of osmolytes into the urine following renal cell damage may also contribute. Myo-inositol also increases in healthy medullary cells when interstitial osmolality increases, and reducedMyoxgene expres- sion in experimental kidney injury argues against increased localmyo-inositol production being a major driver of higher urinary levels in CKD. Sodiummyo-inositol transporters (Smit and Smit2) encoded by theSlc5a3andSlc5a11genes are pri- marily responsible for renalmyo-inositol uptake [28] and are also regulated by TonEBP/NFAT5 [30]. Downregulation of Slc5a11was observed in experimental kidney fibrosis, and Slc5a3andSlc5a11were also downregulated during kidney injury, suggestive of a general decrease inmyo-inositol uptake capacity by stressed tubular cells. Furthermore, a recent study foundmyo-inositol significantly elevated in the urine of mice with renal vasculitis [44]. We hypothesize that increased urinary levels of renal osmo- lytes reflect damage to kidney tissue from hyperosmolar stress, especially in diseases such as diabetic and hypertensive nephrop- athy, where osmotic and hypertensive stresses cause microvascu- lature damage [35,45]. In support, gene expression data of osmolyte transporters point to a dysregulated transporter system as a major factor of osmolytes lost into the urine. Data mining of human kidney biopsy gene expression using the Nephroseq data- base (www.Nephroseq.org) showed a study exploring gene expression in human CKD with diverse aetiologies [46]. These data identified the downregulated kidney expression ofSlc5a11 (foldchange 3.15 discovery, 1.91 validation) andSlc6a12 (foldchange 1.63 discovery, 6.98 validation) mRNA in human CKD kidneys compared with control kidneys. However, no difference inSlc5a3mRNA expression was observed, as was similarly seen in our experimental kidney fibrosis data. The present data are consistent with the increased recogni- tion of osmotic stress as a driving force behind CKD progres- sion. For example, repeated dehydration and osmotic stress are thought to be a key driver of kidney injury in Meso-American nephropathy patients [47]. Moreover, tolvaptan, an antagonist of antidiuretic hormone receptors that promotes polyuria and decreases medullary hyperosmolarity, has been recently approved to treat polycystic kidney disease. Experimental evi- dence suggests that tolvaptan or high water intake may be pro- tective in other nephropathies [48]. Finally, data consistent with impaired kidney responses to osmotic stress in CKD are in line with clinical observations that CKD patients are more sen- sitive to the toxic effects of high-osmolality iodinated contrast media [49]. There are certain limitations in our study. The number of samples in the CKD progression analysis is somewhat low, and a larger, multicentre, validation study is warranted. However, betaine andmyo-inositol showed the same significant direction of alteration in the larger-scale analysis of the total cohort eGFR. Furthermore, gene expression data from independent experimental murine models provides interdiscipline validation of the pathophysiological feasibility of osmolyte abnormalitiesacting as CKD progression biomarkers. Therefore, the increased urinary output of betaine andmyo-inositol reflects abnormal tubular transport of osmolytes and an impaired renal medullary response to osmotic stress in CKD progression. SUPPLEMENTARY DATA Supplementary dataare available at ndt online. FUNDING The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7/ 2007-2013 under grant agreement FP7-PEOPLE-2013-ITN- 608332. A.O. and M.D.S.N. were supported by Spanish govern- ment FEDER funds RETIC REDINREN RD016/0019. FIS PI16/ 02057, PI15/00298, CP14/00133, Sociedad Espa~ nola de Nefrologı´a, Programa Intensificacion Actividad Investigadora (ISCIII/Agencia Lain-Entralgo/CM), Miguel Servet MS14/00133. CONFLICT OF INTEREST STATEMENT A.O. reports grants from the Spanish government during the conduct of the study. REFERENCES 1. Inker LA, Astor BC, Fox CHet al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD.Am J Kidney Dis2014; 63: 713–735 2. Vassalotti JA, Centor R, Turner BJet al. Practical approach to detection and management of chronic kidney disease for the primary care clinician.Am J Med2016; 129: 153–162.e7 3. Slocum JL, Heung M, Pennathur S. Marking renal injury: can we move beyond serum creatinine?Transl Res2012;159:277–289 4. Wettersten HI, Weiss RH. Applications of metabolomics for kidney disease research.Organogenesis2013; 9: 11–18 5. Weiss RH, Kim K. Metabolomics in the study of kidney diseases.Nat Rev Nephrol2011; 8: 22–33 6. Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease.Nat Rev Nephrol2017; 13: 269–284 7. Zhao Y-Y, Xu Q. Metabolomics in chronic kidney disease.Clin Chim Acta 2013; 422: 59–69 8. Zhao YY, Cheng XL, Vaziri NDet al. UPLC-based metabonomic applica- tions for discovering biomarkers of diseases in clinical chemistry.Clin Biochem2014; 47: 16–26 9. Heinzmann SS, Merrifield C. A, Rezzi Set al. Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J Proteome Res2012; 11: 643–655 10. Dona AC, Jimenez B, Scha¨fer Het al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale met- abolic phenotyping.Anal Chem2014; 86: 9887–9894 11. Schanstra JP, Zu¨rbig P, Alkhalaf Aet al. Diagnosis and prediction of CKD progression by assessment of urinary peptides.J Am Soc Nephrol2015; 26: 1999–2010 12. Gil RB, Lehmann R, Schmitt-Kopplin Pet al. 1H NMR-based metabolite profiling workflow to reduce inter-sample chemical shift variations in urine samples for improved biomarker discovery.Anal Bioanal Chem2016; 408: 4683–4691 13. Xia J, Wishart DS. Using MetaboAnalyst 3.0 for comprehensive metabolo- mics data analysis.Curr Protoc Bioinform2016; 55: 14.10.1–14.10.91 14. Dieterle F, Ross A, Schlotterbeck Get al. Probabilistic quotient normaliza- tion as robust method to account for dilution of complex biological mix- tures. Application in 1H NMR metabonomics.Anal Chem2006; 78: 4281–4290 8R.B. Gilet al. Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018 15. Kohl SM, Klein MS, Hochrein Jet al. State-of-the art data normalization methods improve NMR-based metabolomic analysis.Metabolomics2012; 8: 146–160 16. Veselkov KA, Lindon JC, Ebbels TMDet al. Recursive segment-wise peak alignment of biological 1H NMR spectra for improved metabolic biomarker recovery.Anal Chem2009; 81: 56–66 17. Cloarec O, Dumas M, Craig Aet al. Statistical total correlation spectroscopy : an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets statistical total correlation spectroscopy : an exploratory approach for latent biomarker identification from metabolic. Anal Chem2005; 77: 1282–1289 18. Wishart DS, Jewison T, Guo ACet al. HMDB 3.0—the human metabolome database in 2013.Nucleic Acids Res2012; 41: D801–D807 19. Hao J, Liebeke M, Astle Wet al. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN.Nat Protoc 2014; 9: 1416–1427 20. Izquierdo MC, Sanz AB, Mezzano Set al. TWEAK (tumor necrosis factor– like weak inducer of apoptosis) activates CXCL16 expression during renal tubulointerstitial inflammation.Kidney Int2012; 81: 1098–1107 21. Ucero AC, Benito-Martin A, Fuentes-Calvo Iet al. TNF-related weak inducer of apoptosis (TWEAK) promotes kidney fibrosis and Ras- dependent proliferation of cultured renal fibroblast.Biochim Biophys Acta 2013; 1832: 1744–1755 22. Ortiz A, Husi H, Gonzalez-Lafuente Let al. Mitogen-activated protein kin- ase 14 promotes AKI.J Am Soc Nephrol2017; 28: 823–836 23. Chen DQ, Cao G, Chen Het al. Gene and protein expressions and metabo- lomics exhibit activated redox signaling and wnt/b-catenin pathway are associated with metabolite dysfunction in patients with chronic kidney dis- ease.Redox Biol2017; 12: 505–521 24. Posada-Ayala M, Zubiri I, Martin-Lorenzo Met al. Identification of a urine metabolomic signature in patients with advanced-stage chronic kidney dis- ease.Kidney Int2014; 85: 103–111 25. Raffler J, Friedrich N, Arnold Met al. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality.PLoS Genet2015; 11: e1005487 26. Ucero AC, Benito-Martin A, Izquierdo MCet al. Unilateral ureteral obstruction: beyond obstruction.Int Urol Nephrol2014; 46: 765–776 27. Martin-Lorenzo M, Gonzalez-Calero L, Ramos-Barron Aet al.Urine metabolomics insight into acute kidney injury point to oxidative stress dis- ruptions in energy generation and H 2S availability.J Mol Med2017; 95: 1399–1409 28. Burg MB, Ferraris JD. Intracellular organic osmolytes: function and regula- tion.JBiolChem2008; 283: 7309–7313 29. Yancey PH. Organic osmolytes as compatible, metabolic and counteracting cytoprotectants in high osmolarity and other stresses.JExpBiol2005; 208: 2819–2830 30. Lopez-Rodriguez C, Antos CL, Shelton JMet al. Loss of NFAT5 results in renal atrophy and lack of tonicity-responsive gene expression.Proc Natl Acad Sci USA2004; 101: 2392–239731. Yancey PH, Clark ME, Hand SCet al. Living with water stress: evolution of osmolyte systems.Science1982; 217: 1214–1222 32. Street TO, Bolen DW, Rose GD. A molecular mechanism for osmolyte- induced protein stability.Proc Natl Acad Sci USA2006; 103: 13997–14002 33. Katayama H, McGill M, Kearns Aet al. Strategies for folding of affinity tagged proteins using GroEL and osmolytes.J Struct Funct Genomics2009; 10: 57–66 34. Day CR, Gordon SS, Vaughn CLet al. A single amino acid substitution in the renal betaine/GABA transporter prevents trafficking to the plasma membrane.Physiol J2013; 2013: 1 35. Brocker C, Thompson DC, Vasiliou V. The role of hyperosmotic stress in inflammation and disease.Biomol Concepts2012; 3: 345–364 36. Burg MB. Coordinate regulation of organic osmolytes in renal cells.Kidney Int1996; 49: 1684–1685 37. Nakanishi T, Turner RJ, Burg MB. Osmoregulation of betaine transport in mammalian renal medullary cells.Am J Physiol1990; 258: F1061–F1067 38. Lever M, Sizeland PCB, Bason LMet al. Glycine betaine and proline betaine in human blood and urine.Biochim Biophys Acta1994; 1200: 259–264 39. Pummer S, Dantzler WH, Lien Y-HHet al. Reabsorption of betaine in Henle’s loops of rat kidney in vivo.Am J Physiol Renal Physiol2000; 278: F434–F439 40. Kempson S. A, Montrose MH. Osmotic regulation of renal betaine trans- port: transcription and beyond.Pflugers Arch2004; 449: 227–234 41. Grunewald RW, Oppermann M, Schettler Vet al. Polarized function of thick ascending limbs of Henle cells in osmoregulation.Kidney Int2001; 60: 2290–2298 42. Jouret F, Leenders J, Poma Let al. Nuclear magnetic resonance metabolo- mic profiling of mouse kidney, urine and serum following renal ischemia/ reperfusion injury.PLoS One2016; 11: e0163021 43. Wei Q, Xiao X, Fogle Pet al. Changes in metabolic profiles during acute kidney injury and recovery following ischemia/reperfusion.PLoS One2014; 9: e106647 44. Al-Ani B, Fitzpatrick M, Al-Nuaimi Het al. Changes in urinary metabolo- mic profile during relapsing renal vasculitis.Sci Rep2016; 6: 38074 45. Fowler MJ. Microvascular and macrovascular complications of diabetes. Clin Diabetes 2008; 26: 77–82 46. Nakagawa S, Nishihara K, Miyata Het al. Molecular markers of tubulointer- stitial fibrosis and tubular cell damage in patients with chronic kidney dis- ease.PLoS One2015; 10: e0136994 47. Martı´n-Cleary C, Ortiz A. CKD hotspots around the world: where, why and what the lessons are. A CKJ review series.Clin Kidney J2014; 7: 519–515 48. Clark WF, Sontrop JM, Huang S-Het al. Hydration and chronic kidney dis- ease progression: a critical review of the evidence.Am J Nephrol2016; 43: 281–292 49. Eng J, Wilson RF, Subramaniam RMet al. Comparative effect of contrast media type on the incidence of contrast-induced nephropathy a systematic review and meta-analysis.Ann Intern Med2016;164:417–424 Received: 27.10.2017; Editorial decision: 22.12.2017 Increased urinary osmolyte excretion in CKD progression 9 Downloaded from https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfy020/4937848 by guest on 28 November 2018