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Integrative Analysis of Renal Ischemia/Reperfusion Injury and Remote Ischemic Preconditioning in Mice Kumsun Cho, Sang-il Min, Sanghyun Ahn, Seung-Kee Min, Curie Ahn, Kyung-Sang Yu, In-Jin Jang, Joo-Youn Cho, and Jongwon Ha J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00167 • Publication Date (Web): 19 Jun 2017 Downloaded from http://pubs.acs.org on June 20, 2017

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Journal of Proteome Research

Integrative Analysis of Renal Ischemia/Reperfusion Injury and Remote Ischemic Preconditioning in Mice

Kumsun Cho*,†, Sang-il Min*,‡, Sanghyun Ahn‡, Seung-Kee Min‡, Curie Ahn§, Kyung-Sang Yu*,†, In-Jin Jang*,†, Joo-Youn Cho*,† and Jongwon Ha*,‡

*

Metabolomics Medical Research Center (MMRC), Seoul National University College of

Medicine, Seoul, Republic of Korea †

Department of Clinical Pharmacology and Therapeutics, Seoul National University College

of Medicine and Hospital, Seoul, Republic of Korea ‡

Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of

Korea §

Department of Internal Medicine, Seoul National University College of Medicine, Seoul,

Republic of Korea

K.C. and S.-i.M. contributed equally to this work and are co-first authors.

Correspondence: Jongwon Ha, MD, PhD: Department of Surgery, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 110-799, Republic of Korea. Phone: 82.2.2072.2991; Fax: 82.2.766.3971; E-mail: [email protected] Co-correspondence: Joo-Youn Cho, PhD: Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, 103 Daehak-ro,

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Jongno-gu, Seoul 110-799, Republic of Korea. Phone: 82.2.740.8286; Fax: 82.2.742.9252; Email: [email protected]

Abbreviations: ADA, adenosine deaminase; ALDH, aldehyde dehydrogenase; BHMT, betaine-homocysteine S-methyltransferase; CHDH, choline dehydrogenase; C1P, ceramide1-phosphate; DG, diglyceride; DGF, delayed graft function; GPX1, glutathione peroxidase 1; HPLC, the high performance liquid chromatography; IHC, immunohistochemistry; IR, ischemia and reperfusion; LysoPC, lysophosphatidylcholine; MG, monoacylglyceride; PC, phosphatidylcholine; PCA, principal component analysis; PE, phosphatidylethanolamine; PIP, phosphatidylinositol phosphate; PNP, purine nucleoside phosphorylase; PS, phosphatidylserine; Q-ToF MS, quadrupole time-of-flight mass spectrometry; RIPC, remote ischemic preconditioning; SD, standard deviation; SM, sphingomyelin; S1P, sphingosine-1phosphate.

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ABSTRACT Remote ischemic preconditioning (RIPC) is a strategy to induce resistance in a target organ against the oxidative stress and injury caused by ischemia and reperfusion (IR). RIPC harnesses the body’s endogenous protective capabilities through brief episodes of IR applied in organs remote from the target. Few studies have analyzed this phenomenon in the kidney. Furthermore, the window of protection representing RIPC efficacy has not been fully elucidated. Here, we performed a multi-omics study to specify those associated with protective effects of RIPC against the IR injury. A total of 30 mice were divided to four groups: sham, IR only, late RIPC + IR, and early RIPC + IR. We found that IR clearly led to tubular injury, whereas both preconditioning groups exhibited attenuated injury after the insult. In addition, renal IR injury produced changes of the metabolome in kidney, serum, and urine specimens. Furthermore, distinctive mRNA and associated protein expression changes supported potential mechanisms. Our findings revealed that RIPC effectively reduces renal damage after IR and that the potential mechanisms differed between the two time windows of protection. These results may potentially be extended to humans to allow non- or minimally invasive diagnosis of renal IR injury and RIPC efficacy.

Keywords: Ischemia, reperfusion, Remote ischemic preconditioning, Kidney, Multi-omics

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INTRODUCTION In solid organ transplantation, ischemia and reperfusion (IR) followed by acute organ injury is inevitable process. Injured organ shows delayed graft function (DGF) and this is a major hindrance to allograft survival 1-3. Cold preservation of a donor’s organ before transplantation could decrease cellular metabolic rate of injury in the ischemic phase 4, however, also could be the most important risk factor of DGF and acute organ injury5,6. Remote ischemic preconditioning (RIPC) is a strategy to induce resistance in the target organ against the oxidative stress and injury caused by IR 7,8. RIPC harnesses the body’s endogenous protective capabilities through brief episodes of IR applied in organs remote from the target 9-11. There are several pathologic contributors of DGF derived from the donor and recipient 3, however, recent study addressed that recipient RIPC does not alleviate DGF 12

. IR injury is a multifactorial process detrimental to kidney graft function 13-16. Therefore, a

comprehensive understanding of the mechanisms involved in IR injury is essential for the development of therapeutic strategies to improve the outcome of kidney transplantation. RIPC is also a complex process 17, and few studies have been performed that address protective effects of RIPC in the kidney. In particular, although some studies have been published regarding the renoprotective action of RIPC in vivo 18, the window of protection representing RIPC efficacy and its mechanisms have not been fully elucidated. Progressive analytical technologies and data processing tools have been promoted to discover biomarkers regarding pathophysiology 10,19. To date, diverse omics-driven approaches (e.g., metabolomics and transcriptomics) and their integration have broadened our understanding of diseases and the effects of their treatment, a process termed “Multi-omics”.

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In turn, multi-omics studies may also be useful to unveil systemic alterations in metabolism (e.g., oxidative stress) and the mechanisms of their potential treatments (e.g., preconditioning). Accordingly, in this study we used a mouse model of kidney IR injuries to examine whether remote limb IR induces systemic preconditioning and subsequent resistance to IR injuries in the kidney. Additionally, we analyzed whole metabolites in kidney, serum, and urine to identify biomarkers representing IR injury and RIPC effects of the kidney. We also analyzed mRNA expression and performed immunohistochemistry (IHC) in the kidney to broaden our understanding of the pathophysiological effects of IR injury and RIPC and their potential mechanisms. This study can have translational implication that candidate biomarkers might be applied to diagnose acute renal tubular injury against IR.

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EXPERIMENTAL SECTION Animal Study Design Male, wild-type C57BL/6 mice (Central Lab. Animal Inc., Seoul, Korea) aged 9 weeks were utilized for this study. They were fed standard laboratory animal diet. Water and Wayne Lab Blox Pellets (Allied Mills, Chicago, IL, USA) were available ad libitum. Mice were anesthetized with xylazine (5 mg/kg, intraperitoneally) and zoletil (30 mg/kg, intramuscularly). All animal experiments were conducted in accordance with institutional guidelines and the protocol was approved by the Seoul National University Institutional Animal Care and Use Committee (IACUC No: 15-0062-S1A0). Unilateral lower limb ischemia was accomplished using microvascular clamping of the femoral vascular bundle through a longitudinal skin incision of the antero-medial thigh. The RIPC protocol consisted of three cycles of five minutes ischemia followed by five minutes reperfusion. Kidney ischemia was achieved through a midline laparotomy and microvascular clamping of the left renal vascular pedicle. After 45 minutes of warm ischemia, the clamp was removed initiating renal reperfusion. At the end of the reperfusion period (24 hours), mice were sacrificed and kidney tissue and blood were collected. For urine collection, we used metabolic cages, and urine samples were immediately stored at - 80℃ after 24 hours collection. Mice were randomly divided into four groups as follows: 1. Sham (n = 7): Laparotomy, mobilization of the left renal vascular pedicle, and mobilization of the right femoral vascular bundle, all without ischemia. 2. IR (n = 7): Left renal ischemia and reperfusion as described above. 3. Late RIPC + IR (n = 8): RIPC, 24 hours before left renal IR. 4. Early RIPC + IR (n = 8): RIPC, 2 hours before left renal IR. 6


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Tubulointerstitial Injury The kidney tissues were hematoxylin-eosin stained as described in detail in previous studies 10,20

. Stained tissues were scored according to the degree of tubulointerstitial injury, such as

tubular dilatation, cast deposition, brush border loss, and/or tubular necrosis 21. Tubulointerstitial injury was graded on a scale of 0 – 4. A greater score represented more severe damage (Figure 2). All slides were reviewed blindly by a single pathologist.

Kidney, Serum, and Urine Metabolomic Profiling Sample Preparation Kidney samples sized to 50.0 ± 8.6 mg (means ± standard deviation (SD), wet mass) were first pulverized at liquid nitrogen temperature by a manual grinding method with a mortar and pestle to disrupt the tissue. Pre-cooled 80% methanol was added (10 µl/mg), and the tissue was sonicated three times for 20 seconds. Subsequently, they were centrifuged at 18,341 × g for 20 minutes at 4°C. Aliquots of the frozen serum and 24-hour collected urine samples were thawed and diluted 5 times with pre-cooled 80% methanol and water, respectively. All the samples were mixed for 10 min and centrifuged at 18,341 × g for 20 minutes at 4°C.

Chromatographic and TOF-MS Conditions Each sample (5 µl) was loaded onto a Zorbax SB-C18, 50 x 2.1 mm, 1.8 µm (Agilent Technologies, Santa Clara, CA, USA) column held at 40°C

and eluted with 0.1% formic

acid and 20 mM ammonium formate in water (solvent A), and 0.1% formic acid in methanol 7



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(solvent B) with a constant flow rate of 0.4 ml/min and the following gradient conditions: 0 – 0.1 min, 2% B; 0.1 – 13 min, 2 - 98% B; 13 – 15 min, 98% B; 15 – 15.1 min, 98 – 2 % B; 15.1 - 17 min, 2% B. Subsequently, the eluent was introduced into an Agilent 6530 quadrupole time-of-flight (Q-TOF) mass spectrometer (Agilent Technologies). Device settings were as described in a previous study 22. The overall quality of the analysis procedure was monitored using repeat extracts of a pooled kidney, serum, or urine sample (Figure S1 and S2).

Data Processing and Principal Component Analysis (PCA) The intensity of each ion was normalized, scaled, z-transformed, and aligned according to retention time through the Mass Profiler Professional (MPP) software package B.12.01 (Agilent Technologies) to generate normalized data matrix consisting of the retention time, m/z value, and normalized peak area. Following the above data processing procedures, PCA was performed by using the MPP software for both positive and negative electrospray ionization (ESI) datasets, and showed sample clustering and distinguishing ions (filtered by P < 0.05) between sham and IR groups.

Identification of Metabolites The resulting metabolites were identified by ID Browser tool in the MPP software package B.12.01 (Agilent Technologies). This is a widely used annotation module of the software package by matching accurate mass database with 10 ppm mass tolerance, retention-time, and isotope-pattern based on following databases: Agilent-Metlin, human metabolome database (HMDB), Kyoto encyclopedia of genes and genomes (KEGG), and BioCyc 23. Also, if there

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are commercially available authentic standards, we compared the mass spectra fragment (MS/MS) pattern and chromatographic retention times of them to resultant metabolites.

Gene Expression Microarray Total Tissue RNA Preparation For each group, the kidneys were extracted and immersed separately in RNAlater (Ambion, Austin, TX, USA) for optimal RNA preservation according to the manufacturer’s instructions. Preserved samples were delivered to eBiogen (Seoul, Korea) for further gene expression microarray procedures.

Microarray Analysis Target complementary RNA (cRNA) probe synthesis and hybridization were performed using the Agilent Low RNA Input Linear Amplification kit. The fragmented cRNA was resuspended with 2× hybridization buffer and moved onto the Agilent Mouse GE 4x44K v2 Microarray containing probe sequences sourced from RefSeq, Ensembl, Unigene, GenBank, and RIKEN. The arrays were hybridized by incubation at 65°C for 17 hours using an Agilent microarray hybridization oven.

Data Acquisition and Analysis The hybridized images were scanned using an Agilent mRNA microarray scanner and quantified with Feature Extraction Software (Agilent). All data normalization and selection of fold-changed genes were performed using GeneSpringGX 7.3 (Agilent). Functional annotation of genes was performed according to the Gene OntologyTM Consortium

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(http://www.geneontology.org/index.shtml) using GeneSpringGX 7.3. Gene classification was based on searches done using BioCarta (http://www.biocarta.com/), GenMAPP (http://www.genmapp.org/), DAVID (http://david.abcc.ncifcrf.gov/), and Medline databases (http://www.ncbi.nlm.nih.gov/).

Interaction Network Analysis Pathway and network analyses were algorithmically constructed using QIAGEN Ingenuity® Pathway Analysis (IPA®) (QIAGEN Redwood City, CA, USA) which is commercial software (http://www.qiagen.com/ingenuity). The significance of the interaction networks was tested using Fisher’s exact test with significance P < 0.01.

Enrichment and Topology Analysis The possible biological roles were evaluated via enrichment and topology analysis using web-based MetaboAnalyst 3.0 (http://www.metaboanalyst.ca) 24. Statistical computing and visualization operations during the analyses were performed using functions from the R and Bioconductor packages via MetaboAnalyst.

Immunohistochemistry for Target Protein Expression Paraffin-embedded kidney sections (4 µm) were deparaffinized and rehydrated in a graded ethanol series. Endogenous peroxidase was blocked with 3% hydrogen peroxide for 4 minutes and the antibodies were incubated as follows: GPX1 (#3206; Cell Signaling Technologies, Beverly, MA, USA; dilution 1:25); CHDH (#ab198233; Abcam, Cambridge, MA, USA; dilution 1:25); ADA (#NBP1-87404; Novus Biologicals, Littleton, CO, USA; 10


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dilution 1:100); and PNP (#NBP1-82541; Novus Biologicals; dilution 1:50) for 32 minutes at room temperature. A Ventana Chromo Map Kit (Ventana Medical Systems, Tucson, AZ, USA) was utilized and the slides were counterstained with hematoxylin prior to microscopic confirmation. Kidney tissues of sham group were used as controls.

Chemicals Water and methanol were obtained from JT Baker (Center Valley, PA, USA). D-erythroSphinganine (catalogue no. 10007945) was obtained from Cayman Chemical (Ann Arbor, MI, USA), and other authentic standards were purchased from Sigma-Aldrich (St. Louis, MO, USA): betaine aldehyde chloride (catalogue no. B3650), inosine (catalogue no. I4125), hypoxanthine (catalogue no. H9377), adenosine (catalogue no. A9251), 5′-deoxy-5′(methylthio)adenosine (catalogue no. D5011), arachidonic acid (catalogue no. 23401), cis4,7,10,13,16,19-docosahexaenoic acid (catalogue no. D2534), and cis-8,11,14-eicosatrienoic acid (catalogue no. E4504).

Statistical Analysis All data were analyzed using IBM SPSS Statistics 21 (Chicago, IL, USA) and the results are expressed as the means and standard deviations. Data were statistically analyzed using the Mann-Whitney U test and the Wilcoxon signed ranks test. Differences were considered significant when P was < 0.05.

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RESULTS Assessment of IR Injury in the Kidney Based on the previous study regarding IR mouse model 21, a total of 30 mice were randomly divided into sham (n = 7), IR (n = 7), late RIPC + IR (n = 8), or early RIPC + IR (n = 8) groups (Figure 1), and all mice survived the intervention. First, we confirmed the histological spectrum of IR injury and its amelioration by RIPC in the kidney (Figure 2A). Scores of acute tubular injury were compared during the IR phase and the preconditioning followed by the IR phases (Figure 2B). There was a significant increase in acute tubular injury in the IR group compared to the sham group (P < 0.001). In addition, both the late RIPC and early RIPC treatments showed the same significant protective effects toward IR insults (P = 0.001) (Figure 2B).

Metabolomics Study of IR Injury In the Kidney We performed untargeted metabolome analyses using the high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (HPLC/Q-ToF MS) system to find indicative metabolites of renal IR injury as compared to sham treatment. Collected kidney, serum, and urine samples were acquired before and after treatment followed by untargeted metabolomics combined with multivariate data analyses. Principal component analysis (PCA) showed different

metabolome signatures between the sham and IR groups

with overall accuracies as follows: (A) 52.10%, (B) 47.83%, (C) 56.06%, (D) 69.93%, (E) 65.62%, and (F) 88.69% (Figure S1). Renal IR injuries produced changes of the metabolome in the kidney, serum, and urine specimens (Table 1). Identified metabolites from the kidney were aliphatic acyclic

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compounds (betaine aldehyde), nucleosides (inosine, adenosine, and 5′-deoxy-5(methylthio)adenosine), aromatic heteropolycyclic compounds (hypoxanthine), and lipids (Derythro-sphinganine, arachidonic acid, cis-4,7,10,13,16,19-docosahexaenoic acid, glucosylceramide(d18:1/16:0), PE(20:3/20:4), and ceramide(d18:1/16:0)). In serum or urine, distinctive metabolites included lipids (3-oxo-2-pentyl-cyclopentaneoctanoic acid, PIP2(22:2(13Z,16Z)/16:0), lysoPC(24:0), 3,5-dimethyl-tetradecanoic acid, cis-8,11,14eicosatrienoic acid, lysoPC(14:1(9Z)), MG(0:0/18:0/0:0), 4,14-dimethyl-hexadecanoic acid, 15-eicosenoic acid, MG(0:0/22:0/0:0), DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0), glucosylceramide(d18:1/16:0), MG(0:0/24:0/0:0), PE(18:1/20:1), PS(16:0/22:1(11Z)), and PS(22:2(13Z,16Z)/18:2(9Z,12Z))), as well as carnitines (2-hydroxylauroylcarnitine and 2octenoylcarnitine), organic acids (5-L-glutamyl-taurine), and nucleosides (inosine) (Table 1).

Metabolomics Study of RIPC Effects in the Kidney Untargeted metabolomics combined with multivariate data analyses showed significant changes of the endogenous metabolome among sham, IR, late RIPC + IR, and early RIPC + IR groups (Table 1). Betaine aldehyde, adenosine, and D-erythro-sphinganine showed RIPC effects in both time windows (P < 0.05). In addition, ceramide(d18:1/16:0) indicated the renoprotective effects of late RIPC and hypoxanthine revealed early RIPC effects (P < 0.05).

Metabolomics Study of RIPC Effects in the Serum and Urine To identify non-invasive markers representing RIPC renoprotective effects against IR injury, we utilized serum and urine samples in the metabolomic analyses. As observed for the kidney, their metabolomes exhibited different signatures (Figure S1C - F). 2-Hydroxylauroylcarnitine,

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3,5-dimethyl-tetradecanoic acid, cis-8,11,14-eicosatrienoic acid, lysoPC(14:1(9Z)), MG(0:0/18:0/0:0), 4,14-dimethyl-hexadecanoic acid, 15-eicosenoic acid, MG(0:0/22:0/0:0), DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0), MG(0:0/24:0/0:0), PE(18:1/20:1), PS(16:0/22:1(11Z)), PS(22:2(13Z,16Z)/18:2(9Z,12Z)), and inosine represented the renoprotective effects of late RIPC on renal injuries (Table 1). In addition, 2-hydroxylauroylcarnitine, 3,5-dimethyltetradecanoic acid, MG(0:0/18:0/0:0), 4,14-dimethyl-hexadecanoic acid, 15-eicosenoic acid, MG(0:0/22:0/0:0), DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0), PE(18:1/20:1), PS(22:2(13Z,16Z)/18:2(9Z,12Z)), 5-L-glutamyl-tarurine, 2-octenoylcarnitine, and inosine were indicative of early RIPC (Table 1).

Changes in mRNA Expression in the Kidney Next, we explored the mRNA expression levels of 14,205 genes in kidney tissue samples using mRNA microarray analysis (Figure S3). The averages of mRNA expression data of 2 samples from the IR-injured kidney group were compared with those of sham group, excluding those genes with missing data in at least one group. The frequency of increased or decreased gene expression changes was determined by comparing the average expression of 2 samples among the 4 groups. The cutoff values were > 2-fold sham for up-regulation and < 0.5-fold sham for down-regulation with significance at P < 0.05. We identified differentially expressed genes (DEGs) and classified them into biological functions. The pie chart represents all biological functions represented by DEGs (> 2-fold or < 0.5-fold with significance P < 0.05) among total 14,205 mRNAs explored (Figure S3). All the comparisons showed similar patterns. In particular, 206 genes that showed significantly different expression between the sham and IR groups (Figure S3A), and we further analyzed

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them with metabolomics data to find important pathways for IR injury and its alterations by RIPC.

Interaction Network of Differentially Expressed Metabolites and Genes We performed functional analysis of the top selected metabolites and genes identified by untargeted metabolomics and microarray, respectively. To integrate and construct networks regarding metabolomics and transcriptomics data, we used IPA® software package. IPA® is a database based on published literature concerning functions and relevant interactions among proteins and metabolites, and we applied our differentially expressed metabolites and mRNAs (Table 1 and Figure S3B) to this software. This interaction network analysis on the basis of the functional and biological connectivity of metabolites and genes revealed related networks and the most important enzymes including glutathione peroxidase 1 (Gpx1), choline dehydrogenase (Chdh), adenosine deaminase (Ada), and purine nucleoside phosphorylase (Pnp) in the IR and RIPC phenomena (Figure S4). Subsequently, we further examined their protein expression by IHC applied on the kidney tissues of sham, IR, late RIPC + IR, and early RIPC + IR groups.

Protein Expression in the Kidney We next confirmed the gene expression profile findings in the interaction network using IHC for the selected genes. Most genes showed protein expression results consistent with the findings obtained from mRNA arrays. Successful IHC staining was obtained for GPX1, CHDH, ADA, and PNP. Conversely, all samples consisting of histologically normal kidney tissue stained only weakly or not at all for these proteins (Figure 3A – D).

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Metabolic pathways important for donor kidney function We conducted integrated metabolic pathway analysis on the findings obtained from the combined metabolomics and mRNA expression studies via enrichment and topology analysis modules (Figure S5). From these analyses, we could infer pathways including [1] purine metabolism, [2] osmotic regulation, [3] inflammation, and [4] mitochondrial energy metabolism were important for donated kidney function.

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DISCUSSION IR injury constitutes a major problem during kidney transplantation because it inhibits the availability of oxygen and nutrients 16. In turn, RIPC represents an emerging strategy to induce resistance against the oxidative stress and injury caused by IR 25. However, it is uncertain whether there are significant metabolomic changes that characterize the kidney injury induced by IR and its amelioration by RIPC. Additionally, little is known regarding the potential mechanisms underlying IR injury and RIPC as determined through an integrative assessment of metabolite, mRNA, and protein analyses. In this study, we first revealed the kidney, serum, and urine profiles that distinguished the metabolomic signatures of the IR-injured mouse group compared to the sham group using untargeted metabolomic analysis. Subsequently, we performed mRNA microarray analysis followed by IHC for protein expression and localization studies of the identified differentially expressed genes in kidney tissues. Finally, network analysis allowed us to suggest potential mechanisms illustrating all of the findings from the combined analyses regarding the kidney, serum and urine metabolites, along with those of the kidney mRNAs and proteins. Figure 4 shows a schematic summary of our study using an integrative view of the metabolomic differences in the kidney, serum, and urine followed by renal gene and protein expression, as well as their potential mechanisms representing renal injury by IR and amelioration by RIPC: purine metabolism impairment, osmotic dysregulation, inflammation augmentation, and energy metabolism perturbation. [1] Purine Metabolism Impairment Adenosine is known to play a critical role in organ adaptation to ischemia 26; in particular, ischemia induces extracellular adenosine levels in many organs including the kidney 27.

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Extracellular adenosine mainly functions as a signaling molecule and exerts its biological roles through the activation of adenosine receptors localized on the surface of cell membranes 28

. In our study, the levels of adenosine and 5′-deoxy-5′-methylthioadenosine, a potent

agonist of adenosine receptors, were higher in the IR-injured kidney than in the sham group (Table 1). Adenosine can be metabolized to generate reactive oxygen species (ROS) through ADA and PNP; thus, the overexpression of these proteins in the IR kidney suggested that IR injuries may be attributable to ROS production (Figure 3A, 3B, and Figure 4). This was further supported by the high expression of GPX protein in the IR kidney (Figure 3C and Figure 4), which may underlie the observed low levels of urinary 5-L-glutamyl-taurine and inosine in the IR-injured group (Table 1). Furthermore, the low levels of renal adenosine and urinary inosine in the IR kidney (Table 1 and Figure 4) indicated that both late and early RIPC treatment may have ameliorated ROS production whereas only early RIPC appeared to impact urinary 5-L-glutamyl-taurine (Table 1 and Figure 4). [2] Osmotic Dysregulation Phosphatidylcholine (PC) is the most abundant constituent of kidney membranes and increased biosynthesis of PC represents one of the earliest responses for renal cell growth signaling 29. In renal cells, osmoregulation is regulated by osmolytes including betaine aldehyde, which is generated via CHDH from choline; notably, osmolyte concentration perturbation has been shown to occur during conditions of oxidative stress 30. In the current study, we found that the IR-injured kidney was characterized by decreased PE(20:3/20:4) and betaine aldehyde levels and increased CHDH representing osmoregulation in kidney samples, as well as increased cell membrane constituents, such as PE(18:1/20:1), LysoPC(24:0), LysoPC(14:1(9Z)), MG(0:0/24:0/0:0), MG(0:0/22:0/0:0), and MG(0:0/18:0/0:0), as well as 18


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PIP2(22:2(13Z,16Z)/16:0) in the serum (Table 1, Figure 3D and Figure 4). In comparison, RIPC may afford protection against IR injuries by stabilizing the levels of PE(18:1/20:1), LysoPC(14:1(9Z)), and monoacylglycerides in the serum, and of betaine aldehyde in the kidney (Table 1, Figure 3D and Figure 4). [3] Inflammation Augmentation Sphingolipids comprise crucial building blocks of cell membranes; furthermore, it is well known that sphingolipid metabolites act as important molecules with respect to diverse inflammation signaling 31,32. Throughout this study, we observed evidence for perturbation in sphingolipid signaling-related lipids, such as ceramide(d18:1/16:0), glucosylceramide(d18:1/16:0)), and PE(20:3/20:4), as well as in D-erythro-sphinganine in renal cell organelles (Table 1 and Figure 4). In addition, serum lipid metabolites, such as PS(16:0/22:1(11Z)), PS(22:2(13Z,16Z)/18:2(9Z,12Z)), and DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0) were decreased, whereas glucosylceramide(d18:1/16:0) was increased in IR-injured mice (Table 1). This might represent that cell membrane impairment and decreased levels of building blocks occurred consequent to an inflammatory response to IR injury (Figure 4). Our findings also suggested that both late and early RIPC treatments may have ameliorated membrane disruption following IR by stabilizing the levels of serum PS(22:2(13Z,16Z)/18:2(9Z,12Z)) and DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0) (Table 1 and Figure 4), whereas renal ceramide(d18:1/16:0) and serum PS(16:0/22:1(11Z)) were only affected by late RIPC treatment (Table 1 and Figure 4). [4] Energy Metabolism Perturbation

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IR stimulation may impair the energy supply in organs because of the associated lack of oxygen and nutrient supply 33. Thus, free fatty acids may represent an important energy source in these tissues through mitochondrial beta-oxidation 34. In accordance with this model, our study demonstrated increased serum levels of fatty acids; in particular, 4,14-dimethylhexadecanoic acid, 15-eicosenoic acid, 3,5-dimethyl-tetradecanoic acid, cis-8,11,14eicosatrienoic acid, and 3-oxo-2-pentyl-cyclopentaneoctanoic acid (Table 1 and Figure 4). Furthermore, the acyl-carnitines 2-octenoylcarnitine and 2-hydroxylauroyl-carnitine, which exhibit a strong relationship to incomplete fatty acid oxidation, were decreased in the urine and serum, respectively (Table 1). This suggested that the IR-injured kidney requires additional energy supplies to restore epithelial cell integrity 35. Furthermore, some studies have suggested that pro-resolving lipid mediators could protect against IR injuries 36. Here, we showed that the levels of arachidonic acid and cis-4,7,10,13,16,19-docosahexaenoic were lower following IR than in the sham group (Table 1), consistent with the model that these pro-resolving lipid mediators are required for the defense against IR injury (Figure 4). Taken together, our findings demonstrated that IR injury could affect the metabolome (Figure S1) and transcriptome (Figure S3). From the enrichment and topology analysis and interaction network analysis with metabolomics and transcriptomics data (Figure S4 and S5), we found that some metabolites and genes might disrupt cell survival and homeostasis by regulating purine metabolism (adenosine, inosine, hypoxanthine, 5'-deoxy-5'methylthioadenosine, 5-L-glutamyl-taurine, ADA, PNP, and GPX), osmotic regulation (betaine aldehyde, and CHDH), cell membrane integrity (PS(16:0/22:1(11Z)), PS(22:2(13Z,16Z)/18:2(9Z,12Z)), D-erythro-sphinganine, ceramide(d18:1/16:0),

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glucosylceramide(d18:1/16:0), and DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0)), and energy balance (2-hydroxylauroylcarnitine, and 2-octenoylcarnitine) (Figure 4). Furthermore, the results suggested that RIPC may ameliorate IR injury by the potential mechanisms suggested in Figure 4. RIPC did precondition the kidney, and that the timing of this does not seem to impact on the histological severity of the injury (Figure 2). In spite of this, changes in the metabolomic signatures were shown with overall accuracies as follows: (A) 46.47%, (B) 49.25%, (C) 51.08%, (D) 69.34%, (E) 62.68%, and (F) 89.70% (Figure S2). Both time windows of protection, late RIPC and early RIPC, showed sustained trends in the following metabolites: betaine aldehyde, adenosine, D-erythro-sphinganine, 2hydroxylauroylcarnitine, DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0), and PS(22:2(13Z,16Z)/18:2(9Z,12Z)) (Table 1). We also found that late RIPC might prevent inflammation-related cell membrane integrity against IR injury given that lipid metabolites such as ceramide(d18:1/16:0) and PS(16:0/22:1(11Z)) were not changed only in this RIPC time window group after IR. In the early RIPC + IR group, purine metabolism might be less perturbed because hypoxanthine and 5-L-glutamyl-taurine were not altered only in this RIPC time window group after IR. However, all these associated mechanisms are needed to be confirmed in future studies.

CONCLUSIONS These findings provide the foundation for a better understanding of the pathophysiologic effects of renal IR and RIPC and may facilitate the improvement of RIPC-based strategies or allow non- or minimally invasive diagnostics of acute renal tubular injury against IR for human clinical application.

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ASSOCIATED CONTENT Supporting Information The following files are available free of charge on the ACS Publications website http://pubs.acs.org. Figure S1 – S4 (PDF) Figure S1: PCA plots showing discrimination between the sham and IR groups; Figure S2: PCA plots showing discrimination between the late RIPC + IR and the early RIPC + IR groups; Figure S3: Classification of differentially expressed genes (DEGs) between the sham and IR groups into biological functions; Figure S4: Interaction network of differentially expressed metabolites and genes between (A) sham and IR groups, (B) late remote ischemiareperfusion preconditioning (late RIPC) and IR groups, and (C) early remote ischemiareperfusion preconditioning (early RIPC) and IR groups; Figure S5: Enrichment and topology analysis of the findings obtained from combined metabolomics and mRNA microarray studies for (A) sham and IR groups, (B) late remote ischemia-reperfusion preconditioning (late RIPC) and IR groups, and (C) early remote ischemia-reperfusion preconditioning (early RIPC) and IR groups.

AUTHOR INFORMATION Corresponding Authors * J.H.: E-mail: [email protected]; Phone: 82.2.2072.2991; Fax: 82.2.766.3971 * J.Y.C.: E-mail: [email protected]; Phone: 82.2.740.8286; Fax: 82.2.742.9252

Author Contributions

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K.C., S.-i.M., J.H., J.Y.C., S.A., C.A., K.-S.Y., and I.-J.J. participated in study concept and design. S.-i.M., S.-K.M., and J.H. supported animal study and sample collection. K.C. J.-Y.C. K.-S.Y., and I.-J.J. supported metabolomics study. K.C. and S.-i.M. participated in acquisition of data and interpretation of results, and drafted the article. All authors reviewed and revised the manuscript.

Notes The authors have no conflicts of interest to disclose.

ACKNOWLEDGEMENTS This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2014M3A9D3034034).

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(17) Szijarto, A.; Czigany, Z.; Turoczi, Z.; Harsanyi, L. Remote ischemic perconditioning--a simple, low-risk method to decrease ischemic reperfusion injury: models, protocols and mechanistic background. A review. J Surg Res. 2012, 178 (2), 797. (18) Kharbanda, R. K. Transient Limb Ischemia Induces Remote Ischemic Preconditioning In Vivo. Circulation. 2002, 106 (23), 2881. (19) Sabatine, M. S.; Liu, E.; Morrow, D. A.; Heller, E.; McCarroll, R.; Wiegand, R.; Berriz, G. F.; Roth, F. P.; Gerszten, R. E. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation. 2005, 112 (25), 3868. (20) Lee, J. P.; Bae, J. B.; Yang, S. H.; Cha, R. H.; Seong, E. Y.; Park, Y. J.; Ha, J.; Park, M. H.; Paik, J. H.; Kim, Y. S. Genetic predisposition of donors affects the allograft outcome in kidney transplantation; polymorphisms of stromal-derived factor-1 and CXC receptor 4. PLoS One. 2011, 6 (2), e16710. (21) Kim, S. M.; Kim, S. W.; Jung, Y. J.; Min, S. I.; Min, S. K.; Kim, S. J.; Ha, J. Preconditioning with thyroid hormone (3,5,3-triiodothyronine) prevents renal ischemiareperfusion injury in mice. Surgery. 2014, 155 (3), 554. (22) Cho, K.; Moon, J. S.; Kang, J. H.; Jang, H. B.; Lee, H. J.; Park, S. I.; Yu, K. S.; Cho, J. Y. Combined untargeted and targeted metabolomic profiling reveals urinary biomarkers for discriminating obese from normal-weight adolescents. Pediatr Obes. 2017, 12 (2), 93. (23) Sana, T. R.; Gordon, D. B.; Fischer, S. M.; Tichy, S. E.; Kitagawa, N.; Lai, C.; Gosnell, W. L.; Chang, S. P. Global mass spectrometry based metabolomics profiling of erythrocytes infected with Plasmodium falciparum. PLoS One. 2013, 8 (4), e60840. (24) Xia, J. G.; Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protoc. 2011, 6 (6), 743.

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(25) Jaeschke, H. Molecular mechanisms of hepatic ischemia-reperfusion injury and preconditioning. Am J Physiol Gastrointest Liver Physiol. 2003, 284 (1), G15. (26) Sitkovsky, M.; Lukashev, D. Regulation of immune cells by local-tissue oxygen tension: HIF1 alpha and adenosine receptors. Nat Rev Immunol. 2005, 5 (9), 712. (27) Grenz, A.; Zhang, H.; Eckle, T.; Mittelbronn, M.; Wehrmann, M.; Kohle, C.; Kloor, D.; Thompson, L. F.; Osswald, H.; Eltzschig, H. K. Protective role of ecto-5'-nucleotidase (CD73) in renal ischemia. J Am Soc Nephrol. 2007, 18 (3), 833. (28) Eltzschig, H. K.; Ibla, J. C.; Furuta, G. T.; Leonard, M. O.; Jacobson, K. A.; Enjyoji, K.; Robson, S. C.; Colgan, S. P. Coordinated adenine nucleotide phosphohydrolysis and nucleoside signaling in posthypoxic endothelium: role of ectonucleotidases and adenosine A2B receptors. J Exp Med. 2003, 198 (5), 783. (29) Toback, F. G. Phosphatidylcholine metabolism during renal growth and regeneration. Am J Physiol. 1984, 246 (3 Pt 2), F249. (30) Rosas-Rodriguez, J. A.; Valenzuela-Soto, E. M. Enzymes involved in osmolyte synthesis: how does oxidative stress affect osmoregulation in renal cells? Life Sci. 2010, 87 (17-18), 515. (31) Pengcheng Fan, M. L. Lipidomics in Health and Diseases - Beyond the Analysis of Lipids. Journal of Glycomics & Lipidomics. 2015, 05 (01). (32) Maceyka, M.; Spiegel, S. Sphingolipid metabolites in inflammatory disease. Nature. 2014, 510 (7503), 58. (33) Zhan, M.; Brooks, C.; Liu, F.; Sun, L.; Dong, Z. Mitochondrial dynamics: regulatory mechanisms and emerging role in renal pathophysiology. Kidney Int. 2013, 83 (4), 568.

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(34) Houten, S. M.; Wanders, R. J. A. A general introduction to the biochemistry of mitochondrial fatty acid beta-oxidation. J Inherit Metab Dis. 2010, 33 (5), 469. (35) Bonventre, J. V.; Yang, L. Cellular pathophysiology of ischemic acute kidney injury. J Clin Invest. 2011, 121 (11), 4210. (36) Serhan, C. N.; Yacoubian, S.; Yang, R. Anti-inflammatory and proresolving lipid mediators. Annu Rev Pathol. 2008, 3, 279.

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Table 1. Metabolites indicative of renal ischemia-reperfusion injuries and its amelioration by late remote ischemia-reperfusion preconditioning (late RIPC) and early remote ischemia-reperfusion preconditioning (early RIPC). Fold Change (P value1)) tR (min)

Observed mass (m/z)

Delta (± ppm)

Adduct

Formula

Identity

Identification

0.44

[135.1185]+

5.98

M+CH3OH+H

C5H12NO

Betaine aldehyde2)

─

0.46

[267.0732]

1.21

M-H

C10H12N4O5

0.68

[295.0670]+

2.65

2M+Na

C5H4N4O

Hypoxanthine2)

1.8

[268.1048]+

2.70

M+H

C10H13N5O4

Adenosine2)

3.6

[298.0982]

+

4.44

M+H

C11H15N5O3S

5′-Deoxy-5′-(methylthio)adenosine

11.0

[302.3060]+

2.06

M+H

C18H39NO2

D-erythro-Sphinganine2)

13.0

[303.2336]─

2.23

M-H

C20H32O2

Arachidonic Acid2)

─

13.0

[327.2341]

14.8

[682.5604]+

14.9

[788.5245]

─ ─

Inosine

2)

0.90 (0.001)

0.94 (0.004)

2.2 (0.035)

─

─

Database

1.9 (0.003)

─

─

Database

1.7 (0.006)

1.4 (0.049)

1.4 (0.049)

MS/MS

1.8 (0.048)

1.6 (0.021)

─

3)

Database

2.2 (0.002)

1.5 (0.037)

─

3)

Database

1.4 (0.009)

1.4 (0.004)

1.3 (0.028)

Database

1.5 (0.013)

1.8 (0.001)

1.7 (0.001)

Database

3.3 (0.003)

1.8 (0.021)

1.8 (0.037)

12.8

+

[283.2638]─

14.0

[309.2792]

─

3-Oxo-2-pentyl-cyclopentaneoctanoic acid3)

M-H

C16H32O2

3,5-Dimethyl-tetradecanoic acid

M-H

C20H34O2

cis-8,11,14-Eicosatrienoic acid3)

M+FA-H

C21H42O4

MG(0:0/18:0/0:0)

1.50

M-H

C18H36O2

4,14-Dimethyl-hexadecanoic acid3)

2.34

[415.3778]

0.91

[639.4959]+

0.03

14.8

[682.5604]+

1.87

14.9

[443.4096]+

0.28

[830.5079]

─

LysoPC(14:1(9Z))

2.97

14.7

14.9

3)

4.99

14.4

─

3)

1.27

+

50.83

M-H

─

0.96 (0.048)

2-Hydroxylauroylcarnitine

PIP2(22:2(13Z,16Z)/16:0)3)

[403.3077]

─

Database

C19H37NO5 C47H89O19P3

13.9

0.64 (0.025)

Database

M+H

13.6

MS/MS

─

Ceramide(d18:1/16:0)

C18H32O3

C22H44NO7P

2.6 (0.001)

3)

C34H67NO3

M-H

─

2.3 (0.001)

─

M+FA-H M-H2O+H

75.33

─

2.3 (0.002)

─

M+2H

─

1.5 (0.002)

MS/MS

0.42 (0.005)

47.26

[464.3132]

MS/MS

2.4 (0.003)

42.10

13.5

5.7 (0.001)

0.52 (0.009)

[526.2927]+

─

2.9 (0.003)

Database

[360.3617]

[305.2471]─

3.9 (0.004)

Database

12.4

[255.2326]

MS/MS

─

12.3

13.5

0.61 (0.028)

─

+

13.5

─

─

0.64 (0.025)

2.78

LysoPC(24:0)

─

0.41 (0.013)

Database 2)

1.03

C32H66NO7P

0.71 (0.006)

MS/MS

─

2)

2)

[279.2311]+

M+NH4

MS/MS

─

[582.5097]

58.71

0.32 (0.003)

─

11.8

─

0.37 (0.015)

─

Glucosylceramide(d18:1/16:0)2) PE(20:3/20:4)

0.39 (0.013)

0.34 (0.002)

cis-4,7,10,13,16,19-Docosahexaenoic acid

C40H77NO8 C45H76NO8P

MS/MS

0.51 (0.002)

C22H32O2

M-H2O+H M-H

IR vs. early RIPC + IR

MS/MS

M-H

1.87 1.11

IR vs. late RIPC + IR

Database

3.59

15.0

[625.4548]

2)

IR vs. sham

3)

C20H38O2

15-Eicosenoic acid

M+H

C25H50O4

MG(0:0/22:0/0:0)

3)

Database

2.0 (0.003)

1.6 (0.002)

1.4 (0.021)

M+Na

C39H68O5

DG(18:1(11Z)/18:3(9Z,12Z,15Z)/0:0)3)

Database

0.28 (0.002)

0.63 (0.001)

0.33 (0.001)

M-H2O+H

C40H77NO8

Glucosylceramide(d18:1/16:0)3)

Database

2.2 (0.004)

─

─

M+H

C27H54O4

MG(0:0/24:0/0:0)3)

Database

1.3 (0.048)

1.6 (0.002)

─

Database

3.3 (0.002)

2.5 (0.001)

1.9 (0.003)

M+Hac-H

C43H82NO8P

PE(18:1/20:1)

3)

15.6

[816.5766]

0.71

M-H

C44H84NO10P

PS(16:0/22:1(11Z))

Database

0.85 (0.002)

0.78 (0.003)

─

15.6

[862.5576]+

0.91

M+Na

C46H82NO10P

PS(22:2(13Z,16Z)/18:2(9Z,12Z))3)

Database

0.78 (0.002)

0.80 (0.021)

0.86 (0.028)

5.6

[299.0965]─

37.09

M+FA-H

C7H14N2O6S

5-L-glutamyl-taurine4)

Database

0.39 (0.002)

─

0.44 (0.002)

4)

Database

0.41 (0.002)

─

0.40 (0.002)

MS/MS

0.34 (0.003)

0.41 (0.025)

0.39 (0.021)

+

5.8

[303.1279]

5.8

[371.0830]─

3)

32.53

M+NH4

C15H27NO4

2-Octenoylcarnitine

3.91

M+Hac-H

C11H12N4O7

Inosine4)

- 1) Mann-Whitney U test; 2) Kidney metabolite; 3) serum metabolite; 4) urine metabolite. - PE: phosphatidylethanolamine; PIP: phosphatidylinositol bisphosphate; LysoPC: lysophosphatidylcholine; MG: monoacylglyceride; DG: diglyceride; PS: phosphatidylserine.

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FIGURE CAPTIONS Figure 1. Animal study design. (A) Sham, (B) Ischemic reperfusion only (IR), (C) Late remote ischemic preconditioning + Ischemic reperfusion (Late RIPC + IR), and (D) Early remote ischemic preconditioning + Ischemic reperfusion (Early RIPC + IR). Figure 2. Acute tubular injury as measured using hematoxylin-eosin staining. (A) Representative images (Scale bar, 100 µm) and (B) dot plots for acute tubular injury scores. Acute tubular injury score: tubule dilatation, cast deposition, brush border loss/necrosis (0: no injury, 1: mild (< 25% of tubules), 2: moderate (25–50% of tubules), 3: severe (50–75% of tubules), and 4: very severe (> 75%) of tubules). Figure 3. Protein expression of differentially expressed genes selected from the mRNA microarray. Immunohistochemical staining (dark brown) of paraffin-embedded kidney sections (upper panels). The images are of a representative section (Scale bar, 100 µm). Bar graphs of % of positive IHC staining for (A) ADA, (B) PNP, (C) GPX1, and (D) CHDH were normalized to the total number of cells (lower). Data represent the means ± SD; *, P < 0.05 (Mann-Whitney U test). Figure 4. Schematic summary of the integrative analyses regarding metabolites, mRNAs, and proteins, and explanatory pathways of purine metabolism impairment, osmotic dysregulation, inflammation augmentation, and energy metabolism perturbation. The detected and differentially expressed results are shown by colored triangles indicating up- or downregulation: violet (renal metabolite), red (serum metabolite), green (urinary metabolite), and blue (renal protein). PE: phosphatidylethanolamine; PS: phosphatidylserine; PC: phosphatidylcholine; LysoPC: lysophosphatidylcholine; DG: diglyceride; SM:

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sphingomyelin; S1P: sphingosine-1-phosphate; C1P: ceramide-1-phosphate; ALDH: aldehyde dehydrogenase; BHMT: betaine-homocysteine S-methyltransferase.

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Figure 1.

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Figure 2.

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Figure 3.

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Figure 4.

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For Table of Contents (TOC) only

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Reperfusion ... - ACS Publications

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