The incidence of AKI is steadily increasing, leading to relevant morbidity and mortality and causing a growing economic burden to Western health care systems.1 Despite extensive research, therapies for AKI are still lacking in clinical practice. In contrast, preventive strategies using various so-called preconditioning protocols, including caloric restriction (CR) and interventions activating hypoxia signaling (ischemic preconditioning [IP] and hypoxic preconditioning [HP]),2–4 have been found to be extraordinarily effective in animal models. Although it has long been known that CR5 and activation of hypoxia signaling6,7 lead to an extension of healthy lifespan, more recent experiments demonstrated that a short-term application of CR or IP/HP induces robust protection of various organs, including heart,8 kidney,9,10 brain, and liver.9
So far, a comprehensive understanding of the molecular mechanisms underlying the beneficial effect of short-term CR and oxygen deficiency is still lacking. Strikingly, these effects can be observed in a wide range of species from invertebrates to mammals,10–13 and the protective effect is not confined to distinct organs but rather affects whole organisms. This strongly points to evolutionary conserved mechanisms that are paramount for the cellular defense against various injuries.5
The fact that CR and HP share their protective potential has led us to hypothesize that they utilize common pathways. Here, we report our findings of a detailed comparative expression analysis in kidneys of preconditioned mice.
All animal procedures were conducted in accordance with European (European Union directive 86/609/EEC), national and institutional guidelines and approved by local governmental authorities (LANUV 84–02.04.2013.A158).
Male C57BL6/J mice aged 8–12 weeks were housed under identical specific-pathogen-free conditions in group cages (five animals per cage) at a relative humidity of 50%–60% and with a 12-hour light/dark rhythm. All mice received water ad libitum and all except the caloric restricted mice received food ad libitum. Food was obtained from ssniff (Art. V1534–703; Soest, Germany).
Average food intake was determined by daily weighing of remaining food pellets for a period of 2 weeks. For experiments, CR was applied for 4 weeks and mice were fed 70% of observed average food consumption. Mice were weighed on a weekly basis to monitor weight loss. Neither increased mortality nor morbidity were observed during CR.
For HP, mice were put in a sealed chamber with free access to water and food on three consecutive days. To achieve a normobaric, hypoxic environment, oxygen was gradually replaced by nitrogen over a period of 20 minutes, yielding a final oxygen concentration of 8% (Oxygen sensor, Greisinger GMH 3690; GMH Meßtechnik GmbH, Remscheid, Germany). The animals were exposed to hypoxia for 2 hours on the first day and 4 and 8 hours on the second and third day, respectively. No increase in mortality or morbidity was observed after HP.
Renal Ischemia-Reperfusion Injury Model
A warm renal ischemia-reperfusion injury (IRI) model was used as described elsewhere, with slight modifications.12 Briefly, after anesthesia with intraperitoneal application of ketamine/xylazine, the right kidney was removed and the left renal pedicle was clamped for 40 minutes. Postsurgical recovery was assessed on the basis of weight loss, activity (nesting, flight behavior, movement), and appearance (grooming, tachypnoea, dehydration) on a daily basis. Mice were euthanized at 4, 24, or 72 hours after ischemia.
Analysis of Renal Function and Overall Health Performance
Blood samples were collected via puncture of a buccal vein or by final bleeding. Serum urea and creatinine levels were measured on a Cobas C 702 and Creatinine Plus-Test version 2 (both Roche Diagnostics, Mannheim, Germany). Mortality and a postoperative recovery score (see Supplemental Appendix 1) were assessed over a period of 72 hours after IRI.
Acute tubular damage was evaluated in a blinded fashion by an experienced renal pathologist (H.G.) using sections stained with periodic acid–Schiff (five visual fields per section) and categorized using the scoring system proposed by Tirapelli and Goujon14,15 on the basis of the presence of vacuolization, epithelial flattening, loss of brush border, loss of nuclei, and necrosis. Results were graded 0–4 according to the affected area (1: 0%–25%, 2: 25%–50%, 3: 50%–75%, 4: 75%–100%).
Terminal Deoxynucleotidyl Transferase–Mediated Digoxigenin-Deoxyuridine Nick-End Labeling Staining
DeadEnd Fluorometric TUNEL System (Promega) was used on formalin-fixed paraffin sections (2 µm) according to the manufacturer’s protocol. Pictures were taken with a Zeiss Meta 710 confocal microscope for documentation.
mRNA and MicroRNA Sequencing
RNeasy mini Kit (Qiagen, Hilden, Germany) was used to isolate total RNA from snap-frozen kidneys. After removal of ribosomal RNA using biotinylated target-specific oligos combined with Ribo-Zero ribosomal RNA removal beads, RNA was fragmented into small pieces and copied into first-strand complementary DNA (cDNA) followed by second-strand cDNA synthesis. Products were purified and enriched with PCR to create the final cDNA library. After library validation and quantification (2100 Bioanalyzer; Agilent), equimolar amounts of five to six pooled libraries were quantified by using the Peqlab KAPA Library Quantification Kit and the Applied Biosystems 7900HT Sequence Detection System and sequenced on a Hiseq2000 sequencer.16
MicroRNA (miRNA) libraries were prepared using the Illumina TruSeq Small RNA Sample Preparation Kit, as described elsewhere.17
Mapping, trimming, and adapter removal are described in Supplemental Appendix 1. mRNA and miRNA expression were analyzed using DESeq2 (version 1.8.2)18 package of R (version 3.2.0) software (https://www.R-project.org/) (Supplemental Figure 1). Count data were normalized using the size factor to estimate the effective library size.19 For duplicated miRNAs the mean value was taken after calculating dispersion across all samples. Pairwise comparison of different conditions resulted in a list of differentially expressed RNAs applying a P value cut-off of <0.05. P values were adjusted for multiple testing to reduce false discovery rate.
Data Accessibility and Data Sharing
For interactive online accessibility of RNA-sequencing (RNA-seq) data, a database was created with the “shiny” package in R and is available at http://shiny.cecad.uni-koeln.de:3838/IRaP/. RNA-seq primary data can be found at https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-7982.
Gene Ontology and Pathway Analyses
Gene ontology (GO) and KEGG20 pathway analyses were done with the DAVID version 6.8 functional annotation tool (https://david.ncifcrf.gov). An EASE score (modified Fisher exact P value) of <0.05 was defined as significance threshold. The GO analyses of the outliers in the principal component analysis (PCA) were done with the R package topGO, version 2.30.0,21 using the elim algorithm with Fisher exact test. All measured genes (transcripts per million [tpm] of >0 in at least one sample) were used as the background. The KEGG analyses of the outliers in the PCA were done with the R package clusterProfiler (version 3.6.0), using the function enrichKEGG.22 Signaling pathway impact analysis (SPIA) was done with the SPIA package for R, version 2.30.0,23 following the standard workflow of the manual, using all measured genes (tpm of >0 in at least one sample) as the background. From the output tables we used observed total preturbation accumulation (tA) and the False Discovery Rate adjusted global p-values (pGFdr) of the pathways, with a pGFdr<0.05 for plotting.
mitoXplorer was used to analyze and visualize mitochondrial expression dynamics.24 For visualization of the data, access http://mitoxplorer.ibdm.univ-mrs.fr/, then go to “analysis,” choose “Mouse” as organism and select project “Kidney_Injury” to view the data.
Cell-Specific Marker Analysis
To gain more insight into cell type–specific changes we used a published list of marker genes by Clark et al.25 The matrix of tpm values (generated with kallisto, version 0.43.1, and processed with tximport, version 1.6.0, see also section “Pseudotime analysis”) was Z-transformed for each gene separately across all samples. The resulting Z-scores were averaged over all marker genes for each cell type for each group (type of preconditioning and time point). These mean Z-scores were plotted as box-whisker plots for specific cell types and as a bubble plot for all cell types.
Pseudotime analysis was performed as described before using the Monocle workflow in R26 (version 2.6.4). Here, a method that was originally created for single-cell data were applied to our bulk RNA-seq dataset by treating each sample like a single cell. Library tximport, version 1.6.0,27 was used to process the kallisto, version 0.43.1,28 output files, resulting in tpm values for each gene. Genes were classified as being expressed with a tpm >0.1 in at least two samples. The differential expression analysis of Monocle was on the basis of the groups (type of preconditioning and time point of nephrectomy). Sample ordering (“cell ordering” in the original Monocle workflow), dimension reduction, and pseudotime trajectory calculation was done according to the standard Monocle workflow.
A standard PCA was done for all genes that monocle uses to reconstruct the pseudotime trajectories using the R-function prcomp. The matrix of tpms per gene was transformed into a matrix of fold changes (each measurement versus the mean over all samples). The loadings of the first and second principal component were visualized in a scatterplot. We defined upper, lower, and right outliers on the basis of a cut-off of ±0.05, i.e., defining the outer 2.13% of genes as outliers. For these gene sets, GO and KEGG enrichments were calculated.
Outcome Score and Outcome-Related Gene Clustering
An outcome score on the basis of weight loss and serum urea levels 24 and 72 hours after IRI was implemented rating from 1 to 8 for each item, with 8 being the most severe damage or death (see Supplemental Appendix 1). As a caveat for this analysis, CR mice showed a lower weight and higher urea at baseline (Supplemental Figure 2, B and C), which was accounted for by using fractional instead of absolute changes. For a normalized baseline gene expression and to prevent bias through expression outliers, the mean expression for each gene was calculated. Spearman correlation was calculated between normalized expression intensities and quantified score values. The calculation was used to the complete dataset and a coefficient threshold value of rS>0.95 was set to define significantly correlated genes. Standard univariate linear regression analyses were performed to estimate the association of each gene with the outcome score. Because of the small sample size and a high degree of multicollinearity, we used a mean-centered dataset for multivariate regression analysis but included only two genes at a time.
Quantitative Real-Time PCR
Quantitative PCR was performed using TaqMan Custom Arrays on an ABI 7900 HT thermocycler (Applied Biosystems, Life Technologies Cooperation, Carlsbad, CA). mRNA was transcribed with a high-capacity reverse transcription kit (Applied Biosystems). Expression levels were normalized to housekeeping genes (Polr2a, Ubc, Gapdh, Rn18s-rs5) and calculated with the comparative threshold cycle method. For primer identifiers see Supplemental Appendix 1. Analysis was done with Expression Suite software from Thermo Fisher Scientific.
Statistical Analysis of Clinical and Laboratory Parameters
Statistical analysis was done with GraphPad Prism Software version 6.0c. Results are presented as means±SD. For each experiment, at least three biologic replicates were examined. To calculate differences between multiple groups we used two-way ANOVA and a Tukey multiple comparisons test. Significance of weight differences between CR and nonpreconditioned animals was calculated with multiple t tests.
Functional and Phenotypical Characterization
The experimental setup is depicted in Figure 1A. Both HP and CR led to a significantly attenuated rise of creatinine and urea after 24 hours and a trend toward lower values after 72 hours (Figure 1, B and C). Although none of the CR mice (n=13) and only two of 12 (17%) HP mice died within 72 hours after IRI, eight of 14 (57%) nonpreconditioned animals died or had to be euthanized according to previously defined criteria (Figure 1D). Preconditioned mice showed a markedly improved general state of health after IRI, reaching significance in CR mice (Supplemental Figure 2D).
A histology scoring system revealed statistically significant differences in nonpreconditioned animals and HP mice 24 hours after IRI, whereas kidneys in the CR group were not different from unharmed organs (Figure 2B). This result was primarily driven by the extent of tubular necrosis (Figure 2C). Although other common signs of AKI (i.e., brush border loss, tubular cell flattening with prominent tubular lumina, and focal formation of tubular casts) were observed in all animals at 4 hours (Supplemental Figure 3) and 24 hours after IRI (Figure 2, D–F), kidney architecture was markedly improved in HP and fully restored in CR kidneys after 72 hours. Nonpreconditioned animals still showed cast formation and loss of nuclei at that time (Supplemental Figure 3). Attenuated cell death in preconditioned animals was confirmed by terminal deoxynucleotidyl transferase–mediated digoxigenin-deoxyuridine nick-end labeling (TUNEL) staining (Figure 2, G–I).
We performed transcriptional analyses from preconditioned (HP, CR) and nonpreconditioned animals at three times: after preconditioning (0 hours, directly before IRI), and 4 and 24 hours after IRI (Figure 3A). As shown in the PCA (Figure 3B), samples at 0 and 4 hours cluster closely together. At 24 hours after damage, samples are more spread out. Both modes of preconditioning lead to profiles more similar to unharmed animals, with CR animals showing the largest effect. These results are confirmed using pseudotime trajectories29 (Supplemental Figure 4).
We then examined the effect of IRI on the expression of recently published cell-specific markers25 as a surrogate parameter for cellular composition. This analysis showed a marked reduction of markers specific for the proximal tubule 24 hours after IRI in nonpreconditioned animals as compared with CR and HP (Figure 3C). Likewise, macrophage markers increased markedly after IRI in nonpreconditioned animals (Figure 3D), whereas there were only minor changes seen in CR and HP. For an overview of all cell-specific changes, see Supplemental Figure 5.
Transcriptional Changes Induced by Preconditioning
CR and HP induced the differential regulation of 3599 and 321 genes, respectively, with a significant overlap of 230 genes displaying concordant transcriptional changes (i.e., only two genes were not regulated in the same direction) (Figure 4B, see also Supplemental Table 1 or the online repository). These findings could be confirmed for 40 of 44 randomly chosen genes by quantitative PCR (Supplemental Figure 6). When we compared the genes most strongly regulated by either CR or HP, we found an overlap of six genes among the top ten upregulated genes and one gene in the top ten downregulated genes (Figure 4C, online repository).
A comparison of HP and CR on the basis of separate GO term analyses revealed three overlapping biologic processes, “lipid metabolic processes,” “metabolic processes,” and “oxidation-reduction processes,” that were enriched in response to both modes of preconditioning (Figure 4D, Supplemental Table 2). KEGG pathway analyses showed “metabolic pathways,” “peroxisome,” and “glutathione metabolism” to be significant in both groups (Figure 4E, Supplemental Figure 7, Supplemental Table 3). To allow for a better insight into the actual effect on glutathione metabolism and the peroxisome, we provide a visualization of the changes on the basis of KEGG maps in Supplemental Figures 8 and 9. In addition, we performed a SPIA of genes regulated by preconditioning resulting in seven significantly regulated pathways in response to CR (Figure 4F), of which “Alzheimer’s, Parkinson’s and Huntington’s disease,” “NAFLD (non-alcoholic fatty liver disease),” and “Protein processing in endoplasmic reticulum” were also found via KEGG pathway analysis (Supplemental Table 3). The largest group of genes contributing to overrepresentation of neurodegenerative disease terms and nonalcoholic fatty liver disease were genes encoding for mitochondrial proteins (Supplemental Table 4). HP did not show any significant terms in SPIA, most likely because of smaller overall gene expression changes induced by HP. Apart from the mRNA analyses, we also sequenced miRNAs to shed light on this potential additional layer of regulation. However, no miRNAs were regulated after HP. Related information is provided in Supplemental Figure 10.
Transcriptional Changes after IRI
IRI induced major changes in gene expression with 8006 (4 hours) and 10,206 (24 hours) genes being differentially regulated in nonpreconditioned animals (Supplemental Figure 11, Supplemental Table 1, online repository). In line with published data, Keratin 20 was the top upregulated gene at both postischemic timepoints.30 Furthermore, we found transcription factors Atf3, Fosb, Maff, Dusp5, and well known markers of AKI (Havcr-1 [aka Kim1],31Lcn-2 [encoding NGAL],32 and Timp133; Supplemental Figure 11B) among our top upregulated genes, as well as numerous other genes previously described to be associated with early damage, including Jun, Fos, Btg2, Egr1, Zfp36, Klf4, Klf6, and Csrnp1 (Supplemental Table 1). SPIA of the two damage timepoints in nonpreconditioned animals showed a transient pattern of pathway activation with FOXO, C-type lectin receptor, and IL-17 signaling pathway being part of an early response to IRI and NF-κB, PI3K-Akt, AGE-RAGE, and TNF-signaling activated after 24 hours (Supplemental Figure 11C, Supplemental Table 5). Of note, 203 of the 230 genes altered by preconditioning were also significantly regulated 24 hours after IRI in nonpreconditioned animals. Six of the seven significant SPIA terms identified in CR animals before damage were also found significant in SPIA of nonpreconditioned animals 24 hours after IRI, with four of them being regulated in the opposite direction (Supplemental Figure 11D). Because post-transcriptional mechanisms are likely to contribute to our gene expression patterns, we analyzed the expression of a set of genes known to affect mRNA stability and decay during stress.34 Seven stabilizing factors and nine decay factors were significantly dysregulated at 4 or 24 hours after damage, with only Rnpc1 and Mex3d reaching a log2 fold-change >1 (Supplemental Table 6).
Because a mere comparison of gene lists would not have yielded conclusive results, a more global approach was chosen to further analyze the interaction between preconditioning and IRI. Using the PCA shown in Figure 3B, we visualized the loadings of the first and second principal component in a scatterplot to examine the degree to which specific genes contribute (Figure 5, A and B). On the basis of the clustering of the experimental groups and the corresponding loadings, we identified three gene sets of outliers that were assigned with the following functional implications: “early damage,” “late damage,” and “preconditioning-mediated protection.” Using a GO analysis, “early damage” was enriched for terms associated with embryogenesis, inhibition of transcription, and the unfolded protein response. “Late damage” contained genes that can be associated with adaptation and repair. “Preconditioning-mediated protection” is primarily associated with metabolic processes. Interestingly, oxidation-reduction processes were overrepresented in both the “late damage” as well as the “preconditioning-mediated protection” gene set. Figure 5C shows a histogram of all significant GO terms (all gene sets and corresponding GO and KEGG analyses are provided in Supplemental Table 7).
Focused Analysis of Mitochondrial Processes
Because the KEGG, SPIA, and GO term analyses described above hinted, in line with the literature, toward mitochondria being a key player in both damage35,36 and preconditioning,37,38 we used mitoXplorer24 to obtain a more detailed view on mitochondrial processes. Preconditioning via CR affected mitochondrial pathways much more than HP (Supplemental Figure 12). Both preconditioning interventions had a strong effect on mitochondrial reactive oxygen species (ROS) defense (Figure 6A, see mitoXplorer); most regulated genes in mitochondrial ROS defense were identical in CR and HP (Figure 6A). IRI affected almost all mitochondrial processes, some of which were also induced by CR, with most of the genes affected being regulated in the same direction. Again, the most prominent changes were observed in ROS defense (upregulated: Gsta1, Gsta2, Mgst1; downregulated: Mpvl17L, Sod1, Prdx5) (Figure 6, B and C) and to a lesser extent in mitochondrial dynamics (Tcaim), metabolism of lipids and lipoproteins (Nudt19, Hsd3b2), fatty acid degradation (Acadm, Acad9), and fatty acid biosynthesis (Acsm3). In contrast to this, CR led to downregulation of genes involved in apoptosis, e.g., Bik and Dynll1 (Figure 6D), whereas IRI upregulated most genes in this process.
Correlation of the Baseline Expression of Single Genes with Outcome
To further characterize the effect of transcriptional changes on clinical end points we correlated the transcriptome data of all 14 animals at baseline irrespective of the experimental group with a predefined outcome score (calculated from weight loss, change in urea, and death; see Supplemental Appendix 1). The expression of 30 genes was strongly positively correlated with outcome (rS>0.95) and the expression of four genes was strongly negatively correlated with outcome (rS<−0.95) (Supplemental Figures 13 and 15). Of note, 16 of the 30 positively correlated (but none of the negatively correlated) genes had already been identified in our overlap analysis of genes regulated by both modes of preconditioning before IRI (Figure 7A). At 24 hours after IRI, preconditioned animals uniformly showed less downregulation or stronger upregulation of these 16 outcome-correlated genes (Supplemental Table 1, online repository). These effects were most prominent in CR animals, which regarding five genes even showed an opposite regulation after IRI compared with nonpreconditioned animals (Odc1, Cmtm6, Gm7278, Ces2c, Slc39a11). Nhp2 was the only one of these genes that showed upregulation in all groups 24 hours after IRI (Figure 7B). Univariate regression using standard linear models for each gene was applied. With an estimated β-weight of >0.95, Tspan13, Myo5a, Cndp2, and Cyp7b1 appeared to be the four most important predictors of outcome. Because inclusion of all 16 genes in a multivariable regression analysis was not feasible owing to low sample size and, as expected from the retrieval process, marked collinearity, we applied a standard linear model including each possible pair of genes at a time (i.e., all possible permutations of gene pairs, n=16×15/2 pairs) to further delineate the relative role of genes. By looking at the number of significant (P<0.05) contributions of each individual gene in the 15 analyses in which the respective gene was included, again these four genes were retrieved with Myo5a reaching significance in 11, Cndp2 in 10, and Tspan13 and Cyp7b1 in seven of 15 analyses. There were no significant contributions detected for the remaining ten genes. (see Supplemental Figure 14 for univariate analysis, multiple regression models not shown).
We identified a distinct set of genes and pathways that are linked to two preconditioning strategies, suggesting that there might indeed be a common, conserved, molecular protective mechanism. Metabolic as well as oxidation-reduction processes were among the most prominent biologic processes involved, and mitochondria, peroxisomes, plasma membrane, and the endoplasmic reticulum were revealed to be the major cellular components. Many of these findings are in line with the existing literature.2,38–43 However, the comparative approach to CR and HP in its interaction with IRI revealed several interesting candidates that have not been in the focus of AKI research so far and warrant further investigation.
Using markers for specific nephron segments, we confirmed the loss of proximal tubular cells and the accumulation of immune cells as the major cellular sequelae after IRI. Because proximal tubule cells are primary targets to damage in AKI and account for around 50% of the total kidney substance,25 this approach outlines an important tool for future studies using similar datasets in whole-kidney samples.
As expected, IRI led to profound changes in the transcriptional profile similar to findings of other investigators.44,45 We found numerous genes, which had been described to be induced early after IRI in mice30 as well as in human donor kidneys after reperfusion46 among our top genes 4 hours after IRI showing the validity of our approach. Pathway analysis using SPIA showed activation of FoxO signaling, a key regulator of apoptosis, cell cycle progression, glycolysis, stress resistance, longevity, and immune cells.47–49 The importance of immune responses after damage is further highlighted in our dataset through activation of the IL-17 signaling and C-type lectin receptor signaling pathways. The fundamental role of IL-17 signaling in the pathogenesis and protection from AKI has been shown in murine experiments50,51 and already available therapies for spondyloarthritis and psoriasis that block IL-17 signaling52,53 make it an interesting target in humans to be evaluated in clinical studies. C-type lectin receptor signaling—activated via damage-associated molecular patterns in the early phase after ischemia-reperfusion—has been shown as a target to alleviate IRI.54–56 Further, C-type lectin receptor signaling may also be the reason for the activation of NF-κB signaling observed 24 hours after IRI in our data.57 Targeting this proinflammatory pathway injury reduces tubular injury, apoptosis, necrosis, and accumulation of inflammatory cells.58 In general, pathways triggering inflammatory immune responses were prominent in our pathway analyses of postischemic changes.
A detailed comparative analysis of two modes of preconditioning and their transcriptional consequences had not been performed before. Regarding the overlap between CR and HP on a single-gene level, we found a row of interesting candidates to be studied in more detail in respect of their functional involvement in future studies. One example is the acyl-CoA synthetase Acsm3, which is involved in the first step of fatty acid metabolism and was downregulated by both preconditioning treatments. Acsm3 has been associated with fasting-induced renal organoprotection before,13 but had not been linked to HP. Peroxisomal Hao2, one of the top upregulated genes after both modes of preconditioning in our study, is involved in a reaction that ultimately leads to the production of hydrogen peroxide. Fatty acid metabolism/β-oxidation and redox activity in peroxisomes as well as mitochondria play central roles in the promotion of ischemic damage to the kidney.40,44 Kynureninase (Kynu), a commonly upregulated gene in response to preconditioning, is a hydrolase necessary for de novo synthesis of NAD+ from L-tryptophan.59,60 NAD+ is an important cofactor for genome stability, stress tolerance, and metabolism.61 Enhancing NAD+ availability, e.g., via supplementation, has been shown to improve renal function after AKI through augmented mitochondrial stress resistance.39,60,62–64 Interestingly, Olenchock et al.65 showed that α-ketoglutarate–induced secretion of kynurenic acid mediates protective effects of remote IP, emphasizing the central role of L-tryptophan metabolism to strategies that increase cellular stress resistance. Other genes from the list of the most strongly regulated overlapping candidates (Slc7a12, Rdh16f2, Prlr) had not been associated with AKI or preconditioning before. Aqp4 has been implicated to fulfill a protective function in ischemic brain damage,66,67 but a contribution to preconditioning in AKI has not been described.
A comparative analysis of preconditioning-associated alterations in transcripts involved in mitochondrial processes revealed key genes associated with ROS handling that are targets of both HP and CR (Gsta1, Gsta2, Mgst1, Mpv17l). Soluble and membrane-bound glutathione transferases like Gsta and Mgst1 detoxify harmful products of membrane-lipid peroxidation caused by ROS, and thereby protect mitochondria and cells against oxidative damage.68 Of note, we could also see the same genes to be differentially regulated after IRI, confirming their implication in AKI. Apart from altered ROS handling that was common to CR and HP, CR showed changes in terms of glycolysis, lipid metabolism, and pyruvate metabolism, all of which are known to be important processes during IRI, e.g., as part of an ischemia-induced metabolic switch, which is predominant in the oxygen-sensitive proximal tubule.36 Furthermore, the strong CR-associated downregulation of genes involved in apoptosis at baseline may protect the kidney from the upregulation of the same genes that is observed upon IRI and may be one reason for the strong effect of CR on cell death. This insight could allow for more targeted interventions aiming at a modulation of cell death in the prevention and treatment of AKI.
To obtain a more detailed view of the interplay between IRI and preconditioning, we calculated the contribution of single genes to variation using PCA loadings. Undamaged samples (0 hour) and samples at 4 hours formed tight clusters, and samples at 24 hours showed a separation between preconditioned and nonpreconditioned animals. These clusters allowed us to identify the key genes contributing to early and late damage–associated patterns as well as preconditioning-mediated protection after IRI (lower outliers). Although “early damage” (upper outliers) was associated with processes of regulation of transcription, “late damage” (right outliers) was linked to organization and repair, which is largely confirmatory of previous studies.30,45 The lower outliers, which are likely to be connected to “preconditioning-mediated protection” were associated with oxidation-reduction and metabolic processes. Here, apart from Kynu, most other genes were related to metabolic pathways and processes. Examples are Apoc3, a key regulator of triglyceride metabolism,69 and Fdps, a key enzyme in isoprenoid biosynthesis.70 Both haptoglobin (HP), a well known circulating acute phase protein with antioxidant function,71 and Transferrin (TF), necessary for iron handling in our lower outlier group, have been shown to ameliorate toxic effects of iron and confer organ protection in the kidney and other organs.72–77 Interestingly, two of the 15 “preconditioning-mediated protection” associated genes (Cyp24a1, Gc) are involved in vitamin D metabolism. CYP24A1 is a key regulator of 1,25(OH)2D3 catabolism78 and the vitamin D binding protein (GC), which plays a role in actin scavenging, for example after cell damage. Disruption and clumping of actin filaments has been observed in arteries an arterioles in the kidney after IRI.79 Of note, regulation of actin cytoskeleton was among the top regulated pathways 4 hours after damage in nonpreconditioned animals, underlining its significance in tissue damage. GC also exerts immunomodulatory functions like macrophage modulation, enhancing complement factor 5 (C5)–mediated signaling and the binding of endotoxins.80,81Ugt1a2, also belonging to the cluster of genes associated with “preconditioning-mediated protection,” encodes a glucuronosyltransferase that is highly expressed in the kidney and belongs to a family of proteins involved in elimination of steroids, heme metabolites, environmental toxins, and drugs, e.g. glucuronidation of bilirubin.82 Neuraminic acid pyruvate-lyase (NPL) is necessary to form ManNAc, the metabolites of which ultimately enter the hexosamine biosynthesis pathway.83 Increased rates of protein O-GlcNAcylation have been identified to be an early response to cellular stress and to be protective in models of IRI to the heart, brain, and kidney.84–87
In a final step, we hypothesized that the state of an organ at baseline could predefine its resistance to damage and that this is reflected by the transcriptome. Hence, we correlated gene expression with clinical outcome in individual animals irrespective of the pretreatment (i.e., nonpreconditioned, HP or CR). Although a qualitative association between expression of some genes and clinical outcome has to be expected owing to methodological reasons, we reckoned that a quantitative correlation between expression levels and a predefined combined end point could robustly reveal clinically meaningful gene candidates. A total of 34 genes were identified that showed an exquisitely strong correlation with the clinical outcome score and 16 of these genes were also identified in the overlap of genes that were common to both modes of preconditioning. Differences at baseline in urea and body weight in the CR group are a caveat regarding these analyses, which we accounted for by analyzing fractional instead of absolute changes in these parameters. Importantly, the observed correlation was not driven by differences between the groups alone because the association was also detected within the distinct groups (Supplemental Figure 13). Regression analysis suggested Cndp2, Tspan13, Myo5a, and Cyp7b1 to be among the most relevant predictors; however, these findings will require a confirmation by a focused analysis of these candidates using larger numbers of animals. Notwithstanding, these 16 genes do not pertain to the well established “usual suspects” with regard to AKI and protection from IRI, and thus certainly warrant a more detailed examination in further studies.
A comprehensive discussion of all identified genes is beyond the scope of this paper. However, these data along with the online repository providing researchers with detailed information (http://shiny.cecad.uni-koeln.de:3838/IRaP/) may serve as a source for future studies using respective genetically modified animal models. Importantly, the versatile usability of such data are also highlighted by the fact that the protective potential of preconditioning is not limited to IRI. A recent study published by our group, in which we focused on differential regulation of the proteome, could show the enormous potential in the setting of cisplatin-induced AKI.88
In conclusion, we demonstrate in this study for the first time that common organ-protective pathways, biologic processes, and gene sets are activated by CR as well as HP. Our data indicate that possible shared mechanisms between these two preconditioning strategies include strong regulation of mitochondrial energy balance and cellular radical homeostasis. Strikingly, the expression of a limited set of the genes induced by preconditioning was strongly associated with the clinical outcome of individual animals after IRI. These genes have so far not been linked to AKI and may offer the opportunity to develop novel strategies for both the identification of patients at risk and the prevention of AKI. Our online repository will empower other researchers in the field to exploit the full power of our datasets for the design of future studies.
Dr. Späth was supported by the Koeln Fortune Program/Faculty of Medicine, University of Cologne. Dr. Müller was supported by the Nachwuchsgruppen.NRW program of the Ministry of Science North Rhine Westfalia. Furthermore, this study received funding from the German Research Foundation (MU3629/2-1 to Dr. Müller) and the German Federal Ministry of Education and Research (FKZ0315893A Systems Biology of Ageing Cologne Initiative). Dr. Müller, Dr. Benzing, and Dr. Schermer received additional support from the German Research Foundation (grants BE2212 to Dr. Benzing, SCHE1562/6 to Dr. Schermer, and KFO329 to Dr. Benzing, Dr. Schermer, and Dr. Müller). Dr. Beyer reports grants from German Federal Ministry of Education and Research, during the conduct of the study.
Dr. Müller and Dr. Burst designed the study. Dr. Johnsen, Dr. Kubacki, Dr. Späth, Dr. Altmüller, and Mr. Mörsdorf performed the experiments. Dr. Yeroslaviz, Dr, Habermann, Dr. Beyer, Dr. Meharg, and Dr. Bohl analyzed the RNA-sequencing data. Dr. Göbel analyzed the histopathological images. Dr. Bohl programmed the shiny app. Dr. Johnsen and Dr. Kubacki prepared the figures. Dr. Kubacki, Dr. Johnsen, Dr. Burst, and Dr. Müller drafted the manuscript. Dr. Benzing and Dr. Schermer revised the manuscript critically for intellectual content.
We thank Martyna Brütting and Ruth Herzog for excellent technical assistance. Special thanks to the Bioinformatics Core Facility at Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) for help with the analysis of the data.
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2019050534/-/DCSupplemental.
Supplemental Appendix 1. Supplemental methods.
Supplemental Figure 1. Raw count of mapped reads for total RNA samples.
Supplemental Figure 2. Experimental setup and baseline characteristics of animals.
Supplemental Figure 3. Damage after IRI is strongly ameliorated in preconditioned animals.
Supplemental Figure 4. Pseudotime analysis.
Supplemental Figure 5. Cell-specific changes in preconditioned and nonpreconditioned animals before and after IRI.
Supplemental Figure 6. Quantitative PCR validation for RNA-seq results of selected genes that were commonly regulated in response to CR as well as HP.
Supplemental Figure 7. Fifty one genes from overlapping KEGG pathways.
Supplemental Figure 8. Genes in peroxisome pathway regulated in response to CR and HP.
Supplemental Figure 9. Genes in glutathione pathway regulated in response to CR and HP.
Supplemental Figure 10. Differential regulation of miRNAs and biological significance of regulation in response to CR and 24 hours after IRI in nonpreconditioned animals.
Supplemental Figure 11. IRI-induced gene expression patterns and pathways at 4 and 24 hours.
Supplemental Figure 12. Interactome overview of mitochondrial processes (by mitoXplorer).
Supplemental Figure 13. Heatmap for outcome correlated genes.
Supplemental Figure 14. Univariate linear regression analysis of outcome correlated genes.
Supplemental Figure 15. Spearman blots for outcome regulated genes that were enriched after CR as well as HP.
Supplemental Table 1. Differentially regulated genes.
Supplemental Table 2. GO term analysis.
Supplemental Table 3. KEGG pathway analysis.
Supplemental Table 4. Gene lists of SPIA from CR 0 hour.
Supplemental Table 5. SPIA.
Supplemental Table 6. Regulation of AUMD RNA degrading proteins after damage.
Supplemental Table 7. Analysis from PCA with loadings.
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