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J Am Soc Nephrol. 2008 March; 19(3): 547–558. doi: 10.1681/ASN.2007040469. | PMCID: PMC2391061 |
Copyright © 2008 by the American Society of Nephrology The Local and Systemic Inflammatory Transcriptome after Acute Kidney Injury Dmitry N. Grigoryev,* Manchang Liu,* Heitham T. Hassoun,† Chris Cheadle,* Kathleen C. Barnes,* and Hamid Rabb* *Departments of Medicine and †Surgery, Johns Hopkins University, Baltimore, Maryland Received April 17, 2007; Accepted October 17, 2007. Studies in humans and animal models have demonstrated that acute kidney injury (AKI) has a significant effect on the function of extrarenal organs. The combination of AKI and lung dysfunction is associated with 80% mortality; the lung, because of its extensive capillary network, is a prime target for AKI-induced effects. The study presented here tested the hypothesis that AKI leads to a vigorous inflammatory response and produces distinct genomic signatures in the kidney and lung. In a murine model of ischemic AKI, prominent global transcriptomic changes and histologic injury in both kidney and lung tissues were identified. These changes were evident at both early (6 h) and late (36 h) timepoints after 60-min bilateral kidney ischemia and were more prominent than similar timepoints after sham surgery or 30 min of ischemia. The inflammatory transcriptome (109 genes) of both organs changed with marked similarity, including the innate immunity genes Cd14, Socs3, Saa3, Lcn2, and Il1r2. Functional genomic analysis of these genes suggested that IL-10 and IL-6 signaling was involved in the distant effects of local inflammation, and this was supported by increased serum levels of IL-10 and IL-6 after ischemia-reperfusion. In summary, this is the first comprehensive analysis of concomitant inflammation-associated transcriptional changes in the kidney and a remote organ during AKI. Functional genomic analysis identified potential mediators that connect local and systemic inflammation, suggesting that this type of analysis may be a useful discovery tool for novel biomarkers and therapeutic drug development. Clinical studies have revealed a strong association between AKI and dysfunction of extrarenal organs, and more recently animal research has shown a significant causal effect of AKI on distant organ dysfunction. 1–7 Since the availability of dialysis, AKI-associated distant organ dysfunction constitutes the major cause of death in these patients, with the mortality rate still in the 50% range. Despite this frustrating outcome, little is known about the potential pathophysiological interactions between the kidney and extrarenal organs in critically ill patients. Numerous recent studies have demonstrated that outcomes of AKI are heavily dependent upon the severity of comorbid conditions. 8–10 Isolated AKI has a much better prognosis than AKI associated with multiple organ failure, 11,12 and the presence of renal insufficiency continues to be a sensitive marker for poor outcome in the hospitalized patient. 13 Thus, there is an urgent need to study the systemic effects of AKI, and modern discovery tools have the potential to unveil novel diagnostic and therapeutic targets. Inflammation is a major component of the initiation and exacerbation of kidney injury during AKI, 14,15 and local inflammation of kidney tissues could be a source of the development of inflammation and injury in extrarenal organs. 2,16 Given that systemic inflammation typically occurs during AKI, 17–24 we hypothesized that inflammation of postischemic kidney tissue coupled with decreased kidney clearance activates inflammatory pathways in distant organs. To test this hypothesis, we utilized an established ischemia reperfusion injury (IRI) model of AKI, which is characterized by both cellular and soluble inflammation. 20 We recently compared lung genomic and functional effects of AKI versus nephrectomy on lung tissues combined with a candidate gene approach developed by our group 25 to identify multiple biologic responses by lung tissues. 7 In the study presented here, we focused on distinct time and severity of injury-specific inflammatory transcriptomic response in the kidney during AKI. We then compared this to an inflammation-specific genomic analysis of lung. Furthermore, we evaluated soluble inflammatory products related to the implicated genes, levels of which were increased in the circulation during AKI and could serve as a link between intrarenal and distant organ dysfunction during AKI. Mouse Model of AKI-Induced Acute Lung Injury Development of AKI was demonstrated by a significant rise in serum creatinine concentration at both 6 h (1.68 ± 0.11 mg/dl) and 36 h (2.86 ± 0.12 mg/dl) after 60-min renal ischemia compared with sham (0.78 ± 0.14 mg/dl) () and confirmed by kidney histologic injury demonstrated by development of renal tubular cast formation and extensive tubular injury (). Reperfusion was confirmed after clamp removal. Serum creatinine (SCr) after 30-min ischemia was not appreciably different from sham (0.59 ± 0.08 mg/dl) at 6 h (0.81 ± 0.15 mg/dl), but demonstrated a strong trend for elevation at 36 h (2.24 ± 0.67 mg/dl). The lung histologic changes demonstrated modest septal edema and hypercellularity at 6 h, which resolved by 36 h. Meanwhile mice treated with 60-min renal ischemia demonstrated more pronounced septal edema and hypercellularity that persisted at 36 h (). The leukocyte-specific Cd11 gene expression pattern demonstrated moderate (10 to 50%) contribution of transcriptional changes of tissue-infiltrating leukocytes to total kidney () and lung () tissue transcriptomics. Inflammation-Associated Transcriptional Changes Induced by AKI in Kidney and Lung Tissues Gene ontology analysis of MOE430A GeneChip identified 1035 inflammation-related probes that represented 651 known genes that were tested for association with IRI-induced injury in kidney and lung tissues using Gene Set Enrichment Analysis. 26 The generated normalized enrichment scores, which reflect the correlation of inflammatory gene set with the transcriptional effects of AKI demonstrated the highest effect for 60-min ischemia in kidney tissues for both time points ( Table 1). The identified NES were significant [false discovery rate (FDR) < 0.25] for seven of eight tested conditions in which 30-min ischemia failed to illicit a significant transcriptional response in lung 6 h after ischemia ( Table 1). | Table 1.Gene set enrichment analysis of inflammatory genes in acute kidney injury (AKI) settings |
Genomic Response to Ischemia/Reperfusion in the Kidney Sixty-minute kidney ischemia resulted in a higher number of kidney inflammatory gene changes at both 6 and 36 h () compared with 30-min ischemia. At 6 h, Significance Analysis of Microarrays (SAM) identified 55 IRI-affected inflammatory genes after 60 min of ischemia compared with 31 genes after 30 min of ischemia, including 24 genes that were responsive to both mild and severe ischemia. Similarly, at 36 h 54 genes were affected by 60 min of ischemia compared with 28 genes affected by 30 min of ischemia, including 20 genes that were sensitive to both mild and severe ischemia. Sixty-minute ischemia induced sustained inflammation-associated expressional changes in 31 genes () of which 11 and 20 genes were previously identified as associated with kidney IRI and kidney inflammation, respectively ( Table 2). | Table 2.Renal inflammatory genes that are significantly affected during severe AKI |
The supervised hierarchical clustering analysis identified several clusters of genes with similar expression patterns, including a group of genes that were upregulated by both mild and severe injury, and the observed upregulation was sustainable throughout 36 h of reperfusion () and included the Lcn2 gene, which codes for kidney injury biomarker neutrophil gelatinase-associated lipocalin, 19,27 and Cxcl1 and Cxcl2 genes, which code for the keratinocyte-derived cytokine and macrophage inflammatory protein-2 alpha, respectively 28,29 [according to nomenclature, mouse gene symbols are italicized, first letter uppercase and all of the rest lowercase; protein symbols are same as the gene symbol, but not italicized and all uppercase ( http://www.informatics.jax.org)]. Evaluating Genomic Responses to Ischemic AKI in Lung Tissues Sixty-minute kidney ischemia activated 30 inflammatory genes in the lung at 6 h, and 22 genes at 36 h () including 10 genes that were affected at both timepoints ( Table 3). Thirty-minute kidney ischemia was insufficient in triggering significant transcriptional changes in the lung compared with sham surgery. PubMatrix analysis of AKI-induced genomic changes in the lung identified 15 genes that are known to be associated with pulmonary inflammation according to the current PubMed database ( Table 3). The supervised hierarchical clustering analysis identified two major clusters of genes with similar expression trends elicited by severe kidney injury, including Lcn2 and Cxcl2 genes (). | Table 3.Lung inflammatory genes that are significantly affected during severe AKI |
Discriminating Power of Identified Inflammatory Genes The ability of AKI-associated inflammatory gene changes to discriminate the severity and stage of kidney and lung tissues was tested using unsupervised hierarchical clustering. The unsupervised hierarchical clustering of 109 genes that were significantly affected by at least one tested condition identified three major clusters that represent sham, 30-min ischemia, and 60-min ischemia samples (). Notably, these clusters comprised samples from both tissues demonstrating similar (tissue-independent) response to AKI by inflammatory genes in kidney and lung. The transcriptional changes inflicted by 30 min of kidney injury failed to form an individual cluster in either tissue. The seven subclusters (three or more members) were represented by either sham or severe AKI gene expression profiles, demonstrating that the severely AKI-affected samples from both tissues were efficiently discriminated from their corresponding shams (). Validation of Genomics Findings The significant increase in the relative message abundance of Cd14, Socs3, Saa3, Lcn2, and Il1r2 genes was confirmed by real time PCR ( rtPCR) in both tissues () and was overall comparable with the corresponding transcriptional changes identified by the microarray technique. Although the sensitivity of these two transcript detecting approaches was interchangeable in kidney tissues, the rtPCR method was more sensitive in lung samples, which is in agreement with our previous findings. 30 This higher sensitivity of rtPCR allowed identification of significant changes in the transcript abundance for Lcn2 and Cd14 genes at 6- and 36-h timepoints, respectively (). Two downregulated genes in lung tissues, Il1rl1 and Cxcl12 ( Table 3), were also successfully validated by rtPCR (data not shown). Identification and Validation of Mediators Involved in AKI Inflammatory Signal Propagation Global functional genomics analysis of validated candidate genes ( Cd14, Socs3, Saa3, Lcn2, and Il1r2) indicated that the major AKI-dysregulated canonical pathways were IL-10 signaling ( P < 0.000001) and IL-6 signaling ( P < 0.0001) (). Given that mouse IL-10 is a cytokine synthesis inhibitory factor and inhibits the synthesis of several cytokines, including IFN-γ, interleukin-2 (IL-2), TNFα, and granulocyte colony stimulating factor (G-CSF) produced by activated macrophages and by helper T cells ( http://us.expasy.org/cgi-bin/niceprot.pl?P18893), these cytokines were added to a customized Bioplex protein array. The Ccl2 gene that codes for monocyte chemotactic protein-1 (MCP-1) and is coregulated with Il631 was tested as an additional representative of the IL-6 signaling pathway. Although the level of IL-10 protein was significantly higher in serum from mice exposed to AKI than in serum from sham-operated animals, protein levels of IL-10 targets were largely unaffected. The increase in IL-6 and MCP-1 levels in serum from AKI mice was significant and concordant (). The study presented here provides novel characterization and disease-oriented bioinformatics analysis of the inflammatory molecular signature associated with both local and distant organ effects of AKI, enabling identification of novel biomarkers and therapeutic targets. Given that there are more than 1000 known inflammatory signaling molecules, 28 the global gene expression profiling of lung and kidney tissues performed in these studies facilitated characterization of AKI-associated inflammatory processes involved in an established model of ischemic AKI. We also used a novel bioinformatics approach to correlate kidney and lung genomic changes with biological effects in the tissue samples, combining our data with the extensive information in the public domain ( i.e. PubMed, Ingenuity). The global gene expression profiling of injured mouse kidney revealed significant severity- and time-dependent association of the inflammatory pathway with AKI ( Table 1). The injury severity-dependent changes in gene expression were well correlated with the SCr and kidney morphology at early (6 h after injury) and later (36 h after injury) stages of AKI. SCr were significantly increased after 6 h of severe IRI (60-min ischemia) compared with sham and were further elevated after 36 h (), whereas 30 min ischemia did not lead to significant change in SCr at 6 h; however, creatinine was significantly up at 36 h. The extended ischemia time of 60 versus 30 min resulted in an increase in the number of inflammation-associated gene changes compared with the shorter exposure at both 6 and 36 h of AKI. A sizable group of these genes responded to both mild and severe ischemic injury and, moreover, was persistently upregulated throughout the course of AKI (). This observation suggests that this particular group of 31 genes could exert continuous inflammatory changes in the kidney as well as distant organs. Many of these genes have already associated with kidney inflammation in prior studies ( Table 2). Proinflammatory genes that were very highly upregulated included lipocalin 2 (fold change (FC) = 137.4), chemokine (C-X-C motif) ligand 2 (FC = 35.3), IL-6 (FC = 38.25), and chemokine (C-X-C motif) ligand 1 (FC = 29.58). These findings generated the hypothesis that products of these actively transcribed genes could leak into circulation and trigger and sustain systemic inflammation. It has already been described that there is an increase of serum levels of CXCL1 and IL-6 after mouse kidney ischemia, 6,29 suggesting systemic leakage of proinflammatory cytokines/chemokines released by injured kidney tissues. We used the lung as a biosensor of distant systemic effects of AKI. The histological studies of lungs from mice exposed to 30 min of renal ischemia were either similar to tissues from the sham-operated animals or demonstrated minor changes at 6 h after ischemia, which were totally resolved by 36 h. However, the mice with 60 min renal ischemia developed consistent lung changes with septal edema and hypercellularity at 6 h and these remained evident at 36 h (, D and F), similar to our previous report. 7 These lung histological findings were consistent with gene expression profiling studies that identified significant inflammatory transcriptional changes in lung tissues after 60, but not 30 min of ischemic kidney injury (). We acknowledge that kidney and lung transcriptional changes could, in part, be attributed to the transcriptomics of infiltrating leukocytes ( and ). Taken together, these findings suggest that the transcriptional inflammatory response in lungs triggered by AKI is dependent on the severity of kidney injury and could underlie lung functional and histological changes. The analysis of the expression pattern of all identified AKI-associated inflammatory genes in kidney and lung tissues demonstrated a striking similarity in expression changes ( Table 2 and Table 3) and revealed a definite inflammatory signature of AKI in both tissues (), where the similarity in the inflammatory response to AKI overpowered the tissue-specific differences. We also observed that the inflammatory signatures of severe AKI were time-specific and grouped accordingly to the timepoints (6 or 36 h) rather than to severity of the injury. Notably, the clustering of transcriptional changes inflicted by moderate kidney injury (30 min ischemia) was sporadic for both tissues, which correlated well with the functional () and histological ( and ) findings. In line with our findings that kidney IRI leads to intrarenal inflammation that promotes distant organ inflammation with similar inflammatory transcriptomics, we selected genes that were affected by AKI in both tissues. Using other selecting criteria such as novelty (were not previously associated with kidney IRI) and solubility (can exist in secretable forms) we selected serum amyloid A3 ( Saa3), IL-1 receptor type II ( Il1r2), suppressor of cytokine signaling 3 ( Socs3), and CD14 antigen ( Cd14) ( Table 2) as our primary candidates. The established kidney injury marker lipocalin 2 ( Lcn2) was chosen as a positive control. Significant upregulation of selected genes was confirmed by rtPCR at both timepoints () and gene ontology analysis identified the innate immunity processes as common to these genes. Our identification of marked changes in the innate immunity Cd14 gene, which codes for the endotoxin lipopolysaccharide (LPS) receptor was in agreement with previous reports on the induction of this gene and association of its soluble product with kidney injury. Previous data support that Cd14 gene expression is a kidney-specific response and attributed to the ability of the intrinsic renal cells to express Cd14 rather than to infiltrating myeloid cells. 32,33The upregulation of another LPS-responsive Saa3 gene that codes for the isoform of serum amyloid A 34 in lungs during AKI was recently reported by our group, 7 and we now show it is also upregulated in kidney tissues. The next two genes, Il1r2 and Socs3, belong to innate immunity regulators. The Il1r2 gene codes for the IL-1 binding protein, which functions as a decoy receptor and has no direct signaling activity. It sequesters interleukins in the serum and tissues, thus preventing their binding to corresponding functional receptors and interrupting the signaling cascade. 35 Another innate immunity regulator, Socs3, belongs to a family of negative-feedback regulators of cytokine signaling. These regulators are induced by their corresponding cytokines, which leads to subsequent shut-down of the respective signaling cascade, 36 thus inhibiting effects of cytokines involved in the development of AKI and AKI-associated distant organ injury. Finally, the Lcn2 gene that codes for neutrophil gelatinase-associated lipocalin, a well-known AKI biomarker involved in antibacterial host defense, has been shown to limit bacterial growth by sequestering iron and depriving bacteria of this important element. 37 In our studies, Lcn2 was the highest AKI-upregulated gene in kidney tissue (67.9 and 137.4 fold increase at 6 and 36 h after injury, Table 2), which corroborates reports by others. 19,27The global functional analysis that was used to select common pathways for these candidate genes led to the highest association of IL-10 and IL-6 signaling, which was dysregulated by AKI gene expression (). The increase in expression of serum IL-10 and concordant inhibition of its targets IFN-γ, IL-2, TNF-α, and G-CSF in serum from mice with severe (60 min of ischemia) AKI was consistent with these findings (). Of note, the previously reported inability of mild kidney injury (22 min of ischemia) to affect serum levels IL-10 6 explains inconsistent inflammatory signature in lungs exposed to mild AKI (30 min of ischemia) observed in our studies and highlights the severity-dependent role of IL-10 in distant injurious signaling. Although the severe AKI increased IL-10 levels in serum, it had no effect on the Il10 gene expression in kidney and lung tissues (Supplement Tables 3 to 4), suggesting that the source of the high levels of IL-10 in serum is not kidney tissues but rather circulating cells like AKI-activated macrophages and helper T cells. This hypothesis is further supported by the previous finding of high levels of IL-10, IL-6, and suppressor of cytokine signaling-3 in rat alveolar macrophages from inflamed lung where production of TNF-α was markedly reduced. 38 Given that expression of myeloid cell-specific Il10 gene was undetectable by expression profiling of kidney and lung tissues, our Cd11 expression studies ( G and H) were confirmed and support the notion that contribution of infiltrating cells to global transcriptomics of kidney and lung tissues occurs, but is limited. In contrast to Il10, the expression of Il6 was highly upregulated (>38-fold change versus sham) in kidney tissues ( Table 2), thus directly linking increased Il6 gene expression and increased IL-6 serum concentration. The kidney expression of Ccl2 (>3-fold change, Supplement Table 3) was also concordant with the increase of its product MCP-1 in serum. These findings suggest that distant effects of AKI can be conducted by both mechanisms: via AKI-activated leukocytes and by direct leakage of proinflammatory signaling molecules from injured kidney into circulation. The study presented here is, to our knowledge, the first comprehensive inflammation-based genomic map of kidney and lung tissues during AKI. By using bioinformatics tools for both more refined analysis and linking to published data, we have developed a hypothesis about kidney injury-releasing inflammatory mediators into the circulation. Our study has also identified novel AKI-associated genes representing the inflammatory component of injury in both organs. These data offer a new opportunity for understanding the mechanisms involved in local and distant effects of AKI, and identify new diagnostic and therapeutic targets. Animal Model All animal protocols were approved by the Johns Hopkins Animal Care and Use Committee. Kidney IRI was performed in male C57BL6/J mice (6 to 8 wk old, Jackson Laboratory, Bar Harbor, Maine) as described previously. 7 The ischemia duration was 30 or 60 min with a recovery time of 6 or 36 h. Evaluating Renal Function Blood samples were collected from each animal before ischemia and at sacrifice, and centrifuged for 10 min at 8000 rpm to obtain serum. SCr levels were measured as a marker of renal function, using a 557A Creatinine kit that uses a kinetic modification of the Jaffe reaction based on the alkaline picric acid method (Sigma Diagnostics, St. Louis, Missouri) and analyzed on a Cobas Mira S Plus automated analyzer (Roche Diagnostics Corp. Indianapolis, Indiana). Histological Studies For assessment of kidney morphologic injury, hematoxylin and eosin (H&E) staining was performed as described previously. 20 At 6 or 36 h, animals were exsanguinated to remove the blood from organs, kidneys and lungs were harvested (lungs were preinflated with 5% agarose gel through the trachea) and fixed with formalin, embedded with paraffin, and stained with H&E reagents for histological examination. Statistical and power prediction for minimum array requirements analyses The measurements of SCr were analyzed with one-way ANOVA. Individual group means were then compared with a Tukey multiple-comparison test. P values less than 0.05 were considered significant. The microarray sample size determination for class comparisons 39 was calculated as described previously. 7 Identified SD for control kidney (σ = 0.274) and lung (σ = 0.271) tissues were submitted to the microarray sample size identifying formula 40 with power (1 − β) = 90% and 1% FDR (significance level α = 0.01) for kidney and 2% for more heterogeneous lung tissue. 41 The power.t.test function of R2.3.1 program ( www.r-project.org) identified fold change for kidney log 2(Δ) = 1.513 (numerical 2.85) and lung log 2(Δ) = 1.249 (numerical 2.38). Transcript Profiling with Affymetrix Oligonucleotide Arrays Affymetrix GeneChip profiling was performed at the Johns Hopkins Lowe Family Genomics Core. The total kidney or lung mRNA was isolated from parenchymal tissues (capsules and pedicles were removed upon organ collection). The processed RNA (quality monitored on an Agilent 2100 Bioanalyzer) was hybridized to Affymetrix GeneChip MG-430A 2.0 (MOE430A 22,626 transcripts) and hybridization signals measured by a Agilent Gene Array Scanner as described previously. 7 The resulting digitized matrix (CEL files) was processed as described in . Identification of Inflammatory Genes and Gene Set Enrichment Analysis Gene ontology information for each probe set of MOE430A GeneChip was obtained from NetAffx ( http://www.affymetrix.com/analysis/index.affx), enriched by cross-referencing with their human orthologues, and queried for [inflamm], [cytokine], [IL], [chemokine], and [chemotaxis] terms. The resulting gene list was used as an inflammatory gene set database for Gene Set Enrichment Analysis. 26 Genes that were classified as “Present” by GeneChip Operating Software (GCOS 1.4) were used as an expression matrix and 1000 permutations were performed for each condition. FDR <0.25 was considered significant. Computational Identification of AKI-Associated Candidate Genes SAM 2.20 42 was conducted using full permutation of three control and three IRI samples (720 permutations) without application of arbitrary restrictions. 43 Genes with 2.85-fold change and 1% FDR (kidney tissues) and 2.38-fold change and 2% FDR (lung tissues) were considered significantly affected by AKI. The contribution of tissue infiltrating cells to kidney and lung transcriptomics was evaluated using leukocyte-specific gene coding for CD11 antigen (1455733_at probe). Genomic Clustering and Signature Analyses Hierarchical clustering was performed using the MeV (MultiExperiment Viewer) component ( http://www.tm4.org/mev.html) of TM4 system for microarray data management and analysis. 44 The crosstissue and inflammatory signature analyses were conducted using supervised or unsupervised clustering, respectively, with application of uncentered Pearson correlation and average linkage algorithm. In Vitro and In Silico Validation of Gene Expression Data rtPCR validation of selected candidate genes was conducted as described previously. 45 Briefly, transcript levels of selected candidate genes were identified ( n = 3 per condition, randomly selected from 5 samples) by ABI Prism 7700 Sequence Detector Systems using premanufactured probes and primers (Perkin-Elmer/Applied Biosystems). The Δ Ct value for each sample was normalized using three endogenous control genes ( Gapdh, Actb, and Pgk1). All changes in the gene expression between sham-operated ( n = 3) and injured ( n = 3) groups were evaluated with unpaired t test and were significant ( P < 0.05). AKI-affected transcripts were matched against “kidney inflammation,” “lung inflammation,” and “renal ischemia reperfusion injury” terms in PubMed using the PubMatrix automated literature search engine. 46 Global Functional Analysis The functional analysis that identifies the biological functions that were significantly associated with identified candidate genes was conducted using the Ingenuity Pathways Knowledge Base tool ( http://www.ingenuity.com). Fischer's exact test was used to calculate a P value determining the probability that each biological function assigned to our five candidate genes (tested set: 1450826_a_at, 1419532_at, 1416576_at, 1417268_at, 1427747_a_at) was due to chance alone. 47Cytokine Measurement in Mouse Serum The custom cytokine protein array was manufactured as multiplex cytokine kit (Bio-Rad, Hercules, California) based on the Luminex technology and included IL-2, IL-6, IL-10, TNF, MCP-1, and G-CSF antibodies. The cytokine array plate layout consisted of eight standards in duplicate (32,000 to 1.95 pg/ml), two blank wells (for background fluorescence subtraction), and each sample in triplicate wells. The 6- and 36-h serum samples from mice with severe (60 min of ischemia) AKI were processed for Bioplex analysis according to manufacturer's protocol and reported signal for each cytokine was converted to concentration values using Bioplex Manager 3.0 software (Bio-Rad).
[Supplemental Data Files]
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