Gene expression profiling of sepsis‑associated acute kidney injury
- Authors:
- Published online on: September 1, 2020 https://doi.org/10.3892/etm.2020.9161
- Article Number: 34
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Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Sepsis is a clinical syndrome characterized by organ dysfunction caused by a dysregulated immune response to infection (1). Acute kidney injury (AKI) is a serious clinical problem with high levels of mortality (2). Sepsis accounts for more than 50% of all AKI cases, and the combination of sepsis and AKI increases the risk of mortality compared with sepsis alone (3,4). However, to the best of our knowledge, the specific mechanism by which sepsis causes AKI has not yet been fully elucidated, and there is no targeted therapy for sepsis-associated AKI (SA-AKI).
Although the pathophysiological mechanism has not yet been elucidated, it has been suggested that the deleterious inflammatory cascade characteristic of sepsis contributes to AKI (5). Compared with patients with sepsis without AKI, those with AKI have a higher risk of mortality (6,7). However, the renal function of some patients with SA-AKI can return to a normal level (8). A previous study reported that resuscitation of lipopolysaccharide-treated mice with warmed 0.9% saline can increase survival rates to >80%, which allows to study the prognosis of SA-AKI (9). It is therefore vital to investigate the heterogeneity and molecular mechanisms related to the prognosis of SA-AKI, which may provide new therapeutic targets for SA-AKI.
RNA sequencing (RNA-Seq) has become a routine technique for genome-wide expression analysis as it can provide high-resolution sequence information (10). The present study investigated gene expression profiles using RNA-Seq and subsequent bioinformatics analyses to assess the function of differentially expressed genes (DEGs) and molecular mechanisms relevant to the prognosis of SA-AKI.
Materials and methods
Mouse cecal ligation and puncture (CLP) model
A total of 20 8-week-old male C57BL/6J mice (weight, 20-25 g) were obtained from Shanghai SLAC Laboratory Animal Co., Ltd., and housed in a room at 22±1˚C, 40-60% humidity, with a 12-h light/dark cycle (8:00 a.m.-8:00 p.m.), with free access to food and drinking water. All operations on mice were performed by one individual who was skilled in animal modeling. As described previously (9), a 1-cm midline laparotomy was performed following anesthesia with 3% inhaled isoflurane. The base of the cecum was ligated and punctured at its distal tip using a single 21-gauge needle. Subsequently, a small amount of stool content (about 1 mm length) was extruded and put back into the abdominal cavity. The abdomen was sealed and 1 ml warm sterile 0.9% saline was injected subcutaneously to resuscitate mice. The sham group (n=8) underwent laparotomy and cecum exposure but no cecal ligation or puncture.
At 24 h or 48 h post-surgery, all mice were anesthetized by inhalation of 3% isoflurane, and then 50 µl retro-orbital venous blood was collected to measure Scr and BUN levels. Following collection of retro-orbital venous blood to assess Scr and BUN levels, 4 mice were sacrificed (24 h post-CLP, SA-AKI group) and 8 mice were given an additional 10 ml/kg warmed sterile 0.9% saline, and then sacrificed at 48 h post-CLP by cervical dislocation. AKI was defined as a serum creatinine (Scr) level >20 µmol/l and blood urea nitrogen (BUN) >28.3 mg/dl. Mice were grouped into persistent injury (PI; n=3) and recovery (RE; n=5) groups according to renal function at 48 h post-CLP. The RE group consisted of mice whose Scr and BUN levels returned to normal (<20 µmol/l and <28.3 mg/dl, respectively) at 48 h. The PI group contained mice with high levels of Scr (>20 µmol/l) and BUN (>28.3 mg/dl) at 48 h. Mice in the SA-AKI group were sacrificed to collect blood and kidneys at 24 h (n=4). Mice in the PI (n=3) and RE (n=5) group were sacrificed to collect blood and kidneys at 48 h. Mice in the sham group were sacrificed via cervical dislocation to collect blood and kidneys at 24 h (n=4) and 48 h (n=4), respectively.
All protocols of this study were approved by the Animal Ethics Committee of the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang (approval ID. 2016160; 25 February 2016), which follows the institutional guidelines.
Renal function assessment
Blood samples were centrifuged at 13,000 x g for 10 min at 4˚C to collect the supernatant. Scr and BUN levels were measured using the DRI-CHEM dry biochemical analyzer (FUJIFILM Wako Pure Chemical Corporation).
Hematoxylin-eosin staining
The kidney tissues of mice were fixed in 4% paraformaldehyde at room temperature overnight, embedded in paraffin wax, sliced into 4-mm-thick sections, and stained with hematoxylin and eosin at 37˚C for 24 h. Slides were prepared in triplicate, and sections were evaluated under a light microscope (magnification, x400). The pathological injury score of all the samples was assessed twice in a blinded fashion by independent investigators, and the mean score from the two assessments was recorded as the composite score. The grading criteria for renal injury were as follows: Edema of tubular epithelial cells, loss of normal morphology, and cytoplasmic vacuoles. The grading standard for renal tubular involvement were as follows: Observing the aforementioned characteristics of the samples under the same microscope at x400 magnification. Renal tissue pathological injury score was assessed with a 0-5 scoring system: 0, no renal tubular involvement; 1, <25% renal tubular involvement; 2, 25-<50% renal tubular involvement; 3, 50-75% renal tubular involvement; 4, >75% renal tubular involvement.
RNA-Seq and gene expression analysis
Total RNA of 3 kidneys in the PI group and 3 kidneys in the RE group were extracted using TRlzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. RNA purity was assessed using the kaiaoK5500® Spectrophotometer (Kaiao). RNA integrity and concentration were detected using the RNA Nano 6000 assay kit of the Bioanalyzer 2100 system (Agilent Technologies, Inc.). A total amount of 2 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were built using the NEBNext® Ultra™ RNA Library Prep kit for Illumina® (cat. no. E7530L; New England Biolabs, Inc.) following the manufacturer's instructions, and index codes were added to attribute sequences to each sample.
Based on the manufacturer's instructions, the index-coded samples were clustered on a cBot cluster generation system using HiSeq PE Cluster kit v4-cBot-HS (Illumina, Inc.). After the cluster was generated, the libraries were sorted on an Illumina platform to generate 150 bp paired-end reads. Genes with a false discovery rate (FDR) <0.1 and fold-change (FC) >2 were identified as DEGs.
Data analysis and pathway enrichment analysis were performed using integrated Differential Expression and Pathway analysis (iDEP, version 0.90; http://ge-lab.org/idep) (11). iDEP is an online application that integrates many Bioconductor packages and annotation databases to enable users to perform intensive bioinformatics analysis. In the present study, DESeq2 package, hierarchical clustering and κ-means clustering analysis, principal component analysis, Gene Ontology (GO) analysis, and transcription factor binding motifs and microRNA enrichment analysis were used in iDEP application.
Gene set enrichment analysis (GSEA)
GSEA was conducted using the software GSEA v2.2.2 (www.broadinstitute.org/gsea). The enrichment degree and statistical significance were quantified by normalized enrichment score (NES), nominal P-value and FDR (12). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were conducted by using GSEA software.
Statistical analysis
All data are presented as mean ± standard deviation. The results were analyzed by one-way analysis of variance and Tukey's post hoc test using SPSS 22.0 software (IBM Corp.). P<0.05 was considered to indicate a statistically significant difference.
Results
Sepsis-associated AKI mice
CLP was performed in 8-week-old male C57BL/6J mice. At 24 h after the CLP procedure, kidney function biomarkers BUN and Scr were significantly elevated. Following resuscitation, 5 mice exhibited recovery to normal kidney function by 48 h post-CLP operation, however 3 mice had persistent AKI. The levels of kidney function biomarkers are presented in Fig. 1A. It was identified that Scr and BUN levels did not significantly change in sham group at each time point. Renal tissue pathological staining and injury score were used to evaluate renal injury in the present study. In the sham group, the kidney tissue was demonstrated to be normal and no signs of damage were present. To further quantify the degree of renal damage in each group of mice, the pathological grading of renal pathological sections was evaluated. The renal tissue pathological injury score in the SA-AKI group (3.50±0.58) was significantly higher compared with that in the sham group (0.50±0.58) and RE group (1.60±0.55; P<0.05). There was no significant difference in renal tissue pathological injury score between the SA-AKI and PI groups (3.50± 0.58 vs. 3.33± 0.58; P=0.72; Fig. 1B). To investigate this heterogeneity, expression profiling was performed with the kidney-derived RNA obtained from the PI and RE groups 48 h post-CLP.
Expression profiling analyses of kidneys from mice with SA-AKI
iDEP was used to analyze the RNA-Seq dataset. The total read counts per library exhibited a small amount of variation in size (Fig. 2A). Regularized log transformation was performed using the DESeq2 package, and the distribution of the transformed data is presented in Fig. 2B and C, D presented the variation between the RE 1 and RE 2 samples to represent the variation among each replicate. Variation among each replicate was small.
Hierarchical clustering and κ-means clustering analysis
Hierarchical clustering analysis evaluated the top 2,000 significant DEGs among all DEGs, including the 2256 downregulated and 3146 upregulated DEGs, which were ranked by their standard deviation across all samples. The results of hierarchical clustering analysis results are presented in Fig. 3A, in which each column represents the expression pattern of one sample, the red lines represent the upregulated genes, and the green lines represent the downregulated genes (Fig. 3A). A scatter plot was generated to depict the gene expression distribution (Fig. 3B), and a volcano map was created to visualize significant DEGs (Fig. 3C). A principal component analysis (PCA) plot using the first and second principal components is presented in Fig. 3D. There was a significant difference between the RE and PI groups along the first principal component, which explained the 62% variance.
Subsequently, κ-means clustering was used to divide the top 2,000 DEGs into groups. Based on the within-group sum of squares plot as a reference, κ=4 was selected and indicated the number of clusters used for further analysis (Fig. 4A). The four gene clusters underwent GO analysis and the results are presented in Fig. 4B and Table I.
Identification of DEGs
Compared with the PI group, a total of 2,256 downregulated and 3,146 upregulated DEGs were identified in the RE group using a threshold of FDR <0.1 and FC >2 (Fig. 5A). The upregulated and downregulated DEGs then underwent GO analysis for Biological Process terms. In Fig. 5B, the upregulated DEGs were mainly involved in ‘metabolic processes’, the downregulated DEGs were enriched in ‘regulation of response to stress’, ‘cytokine production’, ‘apoptotic process’ and ‘cell death’. The details of the enriched GO Biological Process terms are presented in Table Ⅱ. For GO Cellular Component terms, it was identified that the upregulated DEGs in the RE group were enriched in the ‘mitochondrion’, ‘cytoskeleton’ and ‘centrosome’; the downregulated DEGs in the RE group were enriched in the ‘cell surface’, ‘cell junction’ and ‘adherens junction’ (Fig. 5C and Table III). For GO Molecular Function terms, it was identified the downregulated DEGs in the RE group were enriched in ‘enzyme binding’ and ‘receptor binding’. Furthermore, the upregulated DEGs were enriched in ‘transporter activity and active transmembrane transporter activity’ (Fig. 5D and Table IV).
According to the enriched genes in the upregulated genes of the RE group, it was identified that solute carrier family 7 member 7 (Slc7a7) was highly enriched in the top six pathways (Table SI).
Transcription factor (TF) binding motifs and microRNA (miRNA or miR) target gene sets
As presented in Table Ⅴ, using the TF target gene sets in the enrichment analysis, it was detected that target genes of v-rel reticuloendotheliosis viral oncogene homolog A (RELA; FDR <1.92x10-8) and signaling transducer and activator of transcription 3 (STAT3; FDR <4.57x10-7) were enriched for the downregulated genes. After analysis, the upregulated genes did not enrich any TF. The present study then analyzed the enriched TF binding motifs in the promoters of DEGs. DEG promoters were revealed to contain a number of G-rich motifs, and would be bound by activator protein-2 (AP-2), and other factors such as basic helix-loop-helix protein (bHLH) and Cys2-His2 zinc finger (C2H2 ZF; Table VI).
The predicted target genes for numerous miRNAs were then analyzed. As presented in Table Ⅶ, it was identified that miR-30e-5p, miR-181a-5p and miR-340-5p were significantly enriched target genes for the downregulated genes. In addition, miR-466d-5p and miR-466l-5p were significantly enriched target genes for the upregulated DEGs.
GSEA
To detect KEGG pathways that were differentially enriched in the PI and RE groups, GSEA was conducted between the PI and RE groups. The most significantly enriched signaling pathways were selected based on their NES (Fig. 6 and Table VIII). As presented in Fig. 6, ‘toll like receptor (TLR) signaling’, ‘nod like receptor (NLR) signaling’, ‘pathogenic Escherichia coli infection’, ‘cytokine-cytokine receptor interaction’ and ‘Janus kinase (JAK)/STAT signaling’ were differentially enriched in the PI group. Furthermore, ‘aminoacyl tRNA biosynthesis’, ‘valine leucine and isoleucine degradation’, ‘one carbon pool by folate’ and ‘oxidative phosphorylation’ were differentially enriched in the RE group.
 | Table VIIIDifferentially expressed pathways enriched in the PI and RE mice according to gene set enrichment analysis. |
Discussion
Sepsis is a clinically common and refractory critical illness. As a serious complication of sepsis, SA-AKI is the most common type of AKI in the clinic, with a high mortality rate (8). At present, owing to the lack of specific and effective means of SA-AKI prevention and treatment, it is of critical importance to study the pathophysiological mechanism and investigate new therapeutic targets in SA-AKI. Therefore, RNA-Seq was used in the present study to investigate gene expression profiles related to mice with SA-AKI that exhibited persistent injury or recovery. The current study identified some prognosis-related genes, transcription factors, miRNAs and pathways associated with the differential gene expression profiles, including metabolic process, the transcription factor RELA and miR-30e-5p.
κ-means clustering was used to gain insight into the molecular pathways underlying different gene expression patterns. This divided the top 2,000 DEGs into four clusters. Genes in cluster B were upregulated in the RE group, and this cluster was strongly enriched in genes related to cellular metabolic processes, such as lipid metabolism and fatty acid metabolism. It has been reported that lipoproteins play an important role in preventing infection and inflammation (13). All lipoproteins can bind and neutralize toxic bacterial substances to modulate cytokine production during the inflammatory response, which can weaken the host response (14-16). Therefore, low lipoprotein levels may damage the innate immune response to endotoxins, leading to a deterioration of the systemic inflammatory cascade (15,17-20). Svahn et al (21) reported that dietary omega-3 fatty acids can increase sepsis survival and stimulate the immune system. Omega-3 fatty acids can be metabolized into a novel substance group termed resolvins (Rv) (22). It has been suggested that treatment with RvD1 and RvD2 can increase the survival of patients with sepsis (22-24). One putative mechanism underlying this process may be the combination of an increased frequency of neutrophils and precursor cells in bone marrow and restored phagocytotic capacity (21).
Genes in cluster C were highly upregulated in the PI group, and this cluster was associated with cytokines, cell death and apoptosis. Multiple pathways related to cell death are involved in sepsis due to the pro-inflammatory, anti-inflammatory, coagulation and complement systems. Apoptosis is a natural process of cell death, which is genetically programmed and plays a crucial role in normal physiology and in the pathophysiology of sepsis (25,26). Apoptosis can be initiated by pro-inflammatory cytokines, such as tumor necrosis factor α (TNF-α), interleukin (IL)-1 and IL-6(27). It has been suggested that prevention of cell apoptosis can improve survival in animal models of sepsis and endotoxemia (27).
With FDR <0.1 and FC >2 as the cutoff, 5,402 DEGs (2,256 downregulated and 3,146 upregulated genes) were identified, which have potential to be novel regulators and may play a role in the pathophysiological mechanism underlying SA-AKI development. To further understand the molecular pathways, enrichment analysis of the DEGs was performed. For the GO Biological Process analysis, consistent with the results of k-means clustering analysis, it was identified that the upregulated genes were predominantly involved in cellular metabolic processes. Furthermore, the downregulated genes were associated with cell response to stress, cytokine production, apoptosis and cell death.
As for the GO Cellular Component analysis, it was identified that upregulated DEGs in the RE group were enriched in the mitochondrion, cytoskeleton and centrosome. Mitochondria are cytoplasmic organelles with a double phospholipid membrane that generate energy via oxidative phosphorylation (28). Mitochondria are also associated with calcium homeostasis, intracellular reactive oxygen species (ROS) generation and cell signaling functions (29,30). In response to sepsis, the inflammatory cytokines of the innate immune response, including TNF-α, IL-1 and IL-6, can promote mitochondrial permeability transition, inhibit oxidative phosphorylation and improve ROS production (31,32). It has been demonstrated that mitochondrial dysfunction plays a critical role in SA-AKI (33). However, further studies are still needed to clarify the molecular mechanism underlying mitochondrial dysfunction in SA-AKI.
The cytoskeleton is a necessary dynamic structure for cells that plays an important role in regulating cell permeability under physiological and pathophysiological conditions (34). The cytoskeleton pathway is a known modulator of endothelial barrier function and microvascular permeability (35). It is well known that an inflammatory stimulus can lead to the development of capillary leak and tissue edema due to cytoskeletal rearrangement, which is a characteristic feature of sepsis; its development is one of the causes of organ dysfunction in sepsis (36). A meta-analysis of transcriptomic data demonstrated that the cytoskeletal pathway was upregulated in sepsis patients compared with controls (37). Previous studies have found that the breakdown in blood/endothelial barrier function plays a vital role in the pathogenesis and organ function and could be a new therapeutic target for sepsis (38,39).
Cytokine production is a milestone in the immune response during inflammation and is associated with mortality during sepsis (40). Vertii et al (41) found that centrosome integrity is critical for cytokine production. Impaired cytokine production may predispose the host to infection.
For the GO Molecular Function analysis, it was confirmed that the downregulated genes in the RE group were enriched in enzyme binding and receptor binding. Furthermore, the upregulated genes in the RE group were enriched in transporter activity and active transmembrane transporter activity. This could indicate that transporter activity and active transmembrane transporter activity may be involved in sepsis recovery. The Slc7a7 gene provides instructions for the production of a protein termed y+L amino acid transporter 1 (y+LAT-1), which is involved in transporting certain protein building blocks (amino acids), namely, lysine, arginine and ornithine. The transport of amino acids from the small intestines and kidneys to the rest of the body is necessary for the body to utilize proteins. The y+LAT-1 protein forms one part (the light subunit) of a complex called the heterodimeric cationic amino acid transporter. This subunit is responsible for binding to the amino acids that are transported (42). However, whether the Slc7a7 gene is involved in the recovery of sepsis requires further study.
The present study identified that the TF target genes RELA and STAT3 were significantly associated with the downregulated genes in the RE group. RELA is a REL-associated protein involved in NF-κB heterodimer formation, nuclear translocation and activation (43). As a nuclear transcription factor of various cells, NF-κB plays a crucial role in the coordination of innate and adaptive immune responses in sepsis (44). NF-κB binds with IκB-α and forms a p65/p60 dimer in the cytoplasm in physiological conditions (45). In stress, IκB-α isolates NF-κB and is transferred to the nucleus, where it binds proinflammatory cytokines and promotes the gene transcription of TNF-α and IL-6 to further activate NF-κB to expand the inflammatory response (46). STAT3 is a member of the STAT protein family and can mediate the expression of various genes that stimulate cells. In response to cytokines and growth factors, STAT3 can be phosphorylated by receptor-related JAK and translocate to the cell nucleus where it acts as a transcriptional activator (47). The STAT3 pathway plays an important role in inflammatory signaling cascades and its activation can play a crucial role in host-bacterial interactions (48). Cytokines, such as IL-6 and IL-10, can cause the phosphorylation of the tyrosine and serine residues of STAT3 via JAK. Activated STAT3 can in turn translocate into the nucleus where it binds to specific promotor sequences and regulate the transcription of target genes (49). Zhuo et al (24) demonstrated that RvD1 may improve survival and attenuate the degree of lung inflammation of septic mice by suppressing STAT3 and NF-κB expression through a mechanism that is partly dependent on SIRT1.
TF binding motifs (TFBMs) have conserved DNA sequence elements in their promoter regions. TFBMs can act as binding sites for transcription factors and coordinate the expression of genes in the promoter regions that they appear (50). As TFs typically bind to DNA at sites matching specific sequences motifs, knowledge of the motifs for a TF will be useful to determine a potential binding site of the TF (51). The present study detected that the DEG promoters were overrepresented with numerous G-rich motifs that are bound by AP-2 and other factors, such as bHLH and C2H2 ZF. AP-2 transcription factors (TFAP-2) constitute a family of closely related and evolutionarily conserved proteins that bind to the DNA consensus sequence GCCN3GGC and stimulate target gene transcription (52). AP-2 consists of five different proteins in human and mouse: AP-2α, AP-2β, AP-2γ, AP-2δ and AP-2ε (53). A previous study found that various inflammatory cytokines and prostaglandins can induce the expression of TFAP-2, which in turn causes aberrant activation of genes associated with hyperproliferation of mesangial cells and nephrosclerosis (54). Knocking down TFAP2A significantly decreases TF binding and the gene expression of collagen IV, which may play a critical role in diabetic nephropathy (55).
Our study demonstrated that miR-30e-5p, miR-181a-5p and miR-340-5p were significantly enriched target genes for the downregulated genes, and miR-466d-5p and miR-466l-5p were significantly enriched target genes for the upregulated DEGs. miRNAs and TFs share a common link as two vital gene regulatory molecules in multicellular organisms (56). miRNAs are a family of small non-coding RNAs that modulate gene expression in a sequence-specific manner, and as a result they regulate numerous cellular processes at the post-transcriptional level (57). miR-30e-5p is a member of the miR-30 family, and plays a critical role in renal development and maintaining renal function (58). Sun et al (59) reported that the circulating miR-30e-5p level is significantly elevated in contrast-induced AKI. This may be associated with the pathophysiology of AKI. The miR-181 family has been demonstrated to control inflammation under physiological and pathological conditions by modulating various key aspects of growth, development and activation (60). A previous study demonstrated that miR-340 can inhibit cell proliferation, tumor migration and invasion by targeting JAK via the JAK1/STAT3 signaling pathway (61). It has been indicated that miR-466l may to be a negative regulator of multiple proinflammatory cytokines, such as interferons and IL-10 (62,63). These miRNAs may be potential therapeutic targets for SA-AKI.
The innate immune system is the first line of defense against microbial invasion, relying on pattern recognition receptors to recognize external pathogenic microorganisms and then remove them (64). TLRs and NLRs are important receptors that mediate immune recognition by recognizing pathogen-associated molecular patterns derived from various microbes. These processes are bridges between innate immunity and adaptive immunity (65).
In conclusion, the present study examined gene expression profiles of a SA-AKI model that exhibited persistent renal injury or renal recovery using RNA-Seq. A number of prognosis-related genes, transcription factors, miRNAs and pathways were identified to be associated with the differential expression profile, including metabolic process, the transcription factor RELA and miR-30e-5p, which provide some basis for future experimental studies. Further validation, particularly in human tissues, may provide a more comprehensive understanding of the underlying pathophysiology during SA-AKI.
Supplementary Material
Table SI. Enriched genes in the upregulated genes of the recovery group according to the GO Molecular Function terms.
Acknowledgements
The authors wish to thank Dr Wanling Yang (Department of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, SAR, P.R. China) for providing constructive instructions and comments on the design of the present study.
Funding
This work was supported by a grant from the National Nature Science Foundation of China (grant no. 81670621) and the Nature Science Foundation of Zhejiang Province (grant no. LY16H050001).
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
YY conceived the study, participated in its design and coordination, and drafted the manuscript. JY participated in the experiments and bioinformatics analysis, and contributed to drafting the manuscript. BW performed all statistical analysis. FH participated in its design and coordination, drafted the manuscript and revised the manuscript for important intellectual content. JC conceived the study and revised the manuscript important intellectual content. All authors read and approved the final manuscript.
Ethics approval and consent to participate
All protocols of this study were approved by the Animal Ethics Committee of the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang (approval ID. 2016160; 25 February 2016), which follows the institutional guidelines.
Patient consent for participation
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
|
Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, et al: The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 315:801–810. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Kellum JA and Lameire N: KDIGO AKI Guideline Work Group: Diagnosis, evaluation, and management of acute kidney injury: A KDIGO summary (Part 1). Crit Care. 17(204)2013.PubMed/NCBI View Article : Google Scholar | |
|
Hoste EA, Lameire NH, Vanholder RC, Benoit DD, Decruyenaere JM and Colardyn FA: Acute renal failure in patients with sepsis in a surgical ICU: Predictive factors, incidence, comorbidity, and outcome. J Am Soc Nephrol. 14:1022–1030. 2003.PubMed/NCBI View Article : Google Scholar | |
|
Uchino S, Kellum JA, Bellomo R, Doig GS, Morimatsu H, Morgera S, Schetz M, Tan I, Bouman C, Macedo E, et al: Acute renal failure in critically ill patients: A multinational, multicenter study. JAMA. 294:813–818. 2005.PubMed/NCBI View Article : Google Scholar | |
|
Morrell ED, Kellum JA, Pastor-Soler NM and Hallows KR: Septic acute kidney injury: Molecular mechanisms and the importance of stratification and targeting therapy. Crit Care. 18(501)2014.PubMed/NCBI View Article : Google Scholar | |
|
Bagshaw SM, Uchino S, Bellomo R, Morimatsu H, Morgera S, Schetz M, Tan I, Bouman C, Macedo E, Gibney N, et al: Septic acute kidney injury in critically ill patients: Clinical characteristics and outcomes. Clin J Am Soc Nephrol. 2:431–439. 2007.PubMed/NCBI View Article : Google Scholar | |
|
Bouchard J, Acharya A, Cerda J, Maccariello ER, Madarasu RC, Tolwani AJ, Liang X, Fu P, Liu ZH and Mehta RL: A prospective international multicenter study of AKI in the intensive care unit. Clin J Am Soc Nephrol. 10:1324–1331. 2015.PubMed/NCBI View Article : Google Scholar | |
|
Poston JT and Koyner JL: Sepsis associated acute kidney injury. BMJ. 364(k4891)2019.PubMed/NCBI View Article : Google Scholar | |
|
Tran M, Tam D, Bardia A, Bhasin M, Rowe GC, Kher A, Zsengeller ZK, Akhavan-Sharif MR, Khankin EV, Saintgeniez M, et al: PGC-1α promotes recovery after acute kidney injury during systemic inflammation in mice. J Clin Invest. 121:4003–4014. 2011.PubMed/NCBI View Article : Google Scholar | |
|
Stark R, Grzelak M and Hadfield J: RNA sequencing: The teenage years. Nat Rev Genet. 20:631–656. 2019.PubMed/NCBI View Article : Google Scholar | |
|
Ge SX, Son EW and Yao R: iDEP: An integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics. 19(534)2018.PubMed/NCBI View Article : Google Scholar | |
|
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005.PubMed/NCBI View Article : Google Scholar | |
|
Goldstein B, Giroir B and Randolph A: International Consensus Conference on Pediatric Sepsis: International pediatric sepsis consensus conference: Definitions for sepsis and organ dysfunction in pediatrics. Pediatr Crit Care Med. 6:2–8. 2005.PubMed/NCBI View Article : Google Scholar | |
|
Wu A, Hinds CJ and Thiemermann C: High-density lipoproteins in sepsis and septic shock: Metabolism, actions, and therapeutic applications. Shock. 21:210–221. 2004.PubMed/NCBI View Article : Google Scholar | |
|
Murch O, Collin M, Hinds CJ and Thiemermann C: Lipoproteins in inflammation and sepsis. I. Basic science. Intensive Care Med. 33:13–24. 2007.PubMed/NCBI View Article : Google Scholar | |
|
Vyroubal P, Chiarla C, Giovannini I, Hyspler R, Ticha A, Hrnciarikova D and Zadak Z: Hypocholesterolemia in clinically serious conditions-review. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 152:181–189. 2008.PubMed/NCBI View Article : Google Scholar | |
|
Tsai MH, Peng YS, Chen YC, Lien JM, Tian YC, Fang JT, Weng HH, Chen PC, Yang CW and Wu CS: Low serum concentration of apolipoprotein A-I is an indicator of poor prognosis in cirrhotic patients with severe sepsis. J Hepatol. 50:906–915. 2009.PubMed/NCBI View Article : Google Scholar | |
|
Chenaud C, Merlani PG, Roux-Lombard P, Burger D, Harbarth S, Luyasu S, Graf JD, Dayer JM and Ricou B: Low apolipoprotein A-I level at intensive care unit admission and systemic inflammatory response syndrome exacerbation. Crit Care Med. 32:632–637. 2004.PubMed/NCBI View Article : Google Scholar | |
|
Barlage S, Gnewuch C, Liebisch G, Wolf Z, Audebert FX, Glück T, Fröhlich D, Krämer BK, Rothe G and Schmitz G: Changes in HDL-associated apolipoproteins relate to mortality in human sepsis and correlate to monocyte and platelet activation. Intensive Care Med. 35:1877–1885. 2009.PubMed/NCBI View Article : Google Scholar | |
|
Bonville DA, Parker TS, Levine DM, Gordon BR, Hydo LJ, Eachempati SR and Barie PS: The relationships of hypocholesterolemia to cytokine concentrations and mortality in critically ill patients with systemic inflammatory response syndrome. Surg Infect (Larchmt.). 5:39–49. 2004.PubMed/NCBI View Article : Google Scholar | |
|
Svahn SL, Ulleryd MA, Grahnemo L, Ståhlman M, Borén J, Nilsson S, Jansson JO and Johansson ME: Dietary omega-3 fatty acids increase survival and decrease bacterial load in mice subjected to staphylococcus aureus-induced sepsis. Infect Immun. 84:1205–1213. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Spite M, Norling LV, Summers L, Yang R, Cooper D, Petasis NA, Flower RJ, Perretti M and Serhan CN: Resolvin D2 is a potent regulator of leukocytes and controls microbial sepsis. Nature. 461:1287–1291. 2009.PubMed/NCBI View Article : Google Scholar | |
|
Chen F, Fan XH, Wu YP, Zhu JL, Wang F, Bo LL, Li JB, Bao R and Deng XM: Resolvin D1 improves survival in experimental sepsis through reducing bacterial load and preventing excessive activation of inflammatory response. Eur J Clin Microbiol Infect Dis. 33:457–464. 2014.PubMed/NCBI View Article : Google Scholar | |
|
Zhuo Y, Zhang S, Li C, Yang L, Gao H and Wang X: Resolvin D1 promotes SIRT1 expression to counteract the activation of STAT3 and NF-κB in mice with septic-associated lung injury. Inflammation. 41:1762–1771. 2018.PubMed/NCBI View Article : Google Scholar | |
|
Kerr JF, Wyllie AH and Currie AR: Apoptosis: A basic biological phenomenon with wide-ranging implications in tissue kinetics. Br J Cancer. 26:239–257. 1972.PubMed/NCBI View Article : Google Scholar | |
|
Ayala A, Perl M, Venet F, Lomas-Neira J, Swan R and Chung CS: Apoptosis in sepsis: Mechanisms, clinical impact and potential therapeutic targets. Curr Pharm Des. 14:1853–1859. 2008.PubMed/NCBI View Article : Google Scholar | |
|
Harjai M, Bogra J, Kohli M and Pant AB: Is suppression of apoptosis a new therapeutic target in sepsis? Anaesth Intensive Care. 41:175–183. 2013.PubMed/NCBI View Article : Google Scholar | |
|
Green DR, Galluzzi L and Kroemer G: Mitochondria and the autophagy-inflammation-cell death axis in organismal aging. Science. 333:1109–1112. 2011.PubMed/NCBI View Article : Google Scholar | |
|
Galley HF: Oxidative stress and mitochondrial dysfunction in sepsis. Br J Anaesth. 107:57–64. 2011.PubMed/NCBI View Article : Google Scholar | |
|
Rocha M, Herance R, Rovira S, Hernandez-Mijares A and Victor VM: Mitochondrial dysfunction and antioxidant therapy in sepsis. Infect Disord Drug Targets. 12:161–178. 2012.PubMed/NCBI View Article : Google Scholar | |
|
Siskind LJ, Kolesnick RN and Colombini M: Ceramide channels increase the permeability of the mitochondrial outer membrane to small proteins. J Biol Chem. 277:26796–26803. 2002.PubMed/NCBI View Article : Google Scholar | |
|
Garcia-Ruiz C, Colell A, Mari M, Morales A and Fernandez-Checa JC: Direct effect of ceramide on the mitochondrial electron transport chain leads to generation of reactive oxygen species. Role of mitochondrial glutathione. J Biol Chem. 272:11369–11377. 1997.PubMed/NCBI View Article : Google Scholar | |
|
Sureshbabu A, Patino E, Ma KC, Laursen K, Finkelsztein EJ, Akchurin O, Muthukumar T, Ryter SW, Gudas L, Choi AMK and Choi ME: RIPK3 promotes sepsis-induced acute kidney injury via mitochondrial dysfunction. JCI Insight. 3(e98411)2018.PubMed/NCBI View Article : Google Scholar | |
|
Rodgers LS and Fanning AS: Regulation of epithelial permeability by the actin cytoskeleton. Cytoskeleton (Hoboken). 68:653–660. 2011.PubMed/NCBI View Article : Google Scholar | |
|
Jacobson JR and Garcia JG: Novel therapies for microvascular permeability in sepsis. Curr Drug Targets. 8:509–514. 2007.PubMed/NCBI View Article : Google Scholar | |
|
Gustot T: Multiple organ failure in sepsis: Prognosis and role of systemic inflammatory response. Curr Opin Crit Care. 17:153–159. 2011.PubMed/NCBI View Article : Google Scholar | |
|
Ma J, Chen C, Barth AS, Cheadle C, Guan X and Gao L: Lysosome and cytoskeleton pathways are robustly enriched in the blood of septic patients: A meta-analysis of transcriptomic data. Mediators Inflamm. 2015(984825)2015.PubMed/NCBI View Article : Google Scholar | |
|
de Montmollin E and Annane D: Year in review 2010: Critical care-multiple organ dysfunction and sepsis. Crit Care. 15(236)2011.PubMed/NCBI View Article : Google Scholar | |
|
Goldenberg NM, Steinberg BE, Slutsky AS and Lee WL: Broken barriers: A new take on sepsis pathogenesis. Sci Transl Med. 3(88ps25)2011.PubMed/NCBI View Article : Google Scholar | |
|
Chousterman BG, Swirski FK and Weber GF: Cytokine storm and sepsis disease pathogenesis. Semin Immunopathol. 39:517–528. 2017.PubMed/NCBI View Article : Google Scholar | |
|
Vertii A, Ivshina M, Zimmerman W, Hehnly H, Kant S and Doxsey S: The centrosome undergoes Plk1-independent interphase maturation during inflammation and mediates cytokine release. Dev Cell. 37:377–386. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Sperandeo MP, Andria G and Sebastio G: Lysinuric protein intolerance: Update and extended mutation analysis of the SLC7A7 gene. Hum Mutat. 29:14–21. 2008.PubMed/NCBI View Article : Google Scholar | |
|
Lesina M, Wörmann SM, Morton J, Diakopoulos KN, Korneeva O, Wimmer M, Einwächter H, Sperveslage J, Demir IE, Kehl T, et al: RelA regulates CXCL1/CXCR2-dependent oncogene-induced senescence in murine Kras-driven pancreatic carcinogenesis. J Clin Invest. 126:2919–2932. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Chen Y, Guo L, Lang H, Hu X, Jing S, Luo M, Xu G and Zhou Z: Effect of a stellate ganglion block on acute lung injury in septic rats. Inflammation. 41:1601–1609. 2018.PubMed/NCBI View Article : Google Scholar | |
|
Zhu T, Wang DX, Zhang W, Liao XQ, Guan X, Bo H, Sun JY, Huang NW, He J, Zhang YK, et al: Andrographolide protects against LPS-induced acute lung injury by inactivation of NF-κB. PLoS One. 8(e56407)2013.PubMed/NCBI View Article : Google Scholar | |
|
Ni S, Miao K, Zhou X, Xu N, Li C, Zhu R, Sun R and Wang Y: The involvement of follistatin-like protein 1 in osteoarthritis by elevating NF-κB-mediated inflammatory cytokines and enhancing fibroblast like synoviocyte proliferation. Arthritis Res Ther. 17(91)2015.PubMed/NCBI View Article : Google Scholar | |
|
Lim CP and Cao X: Structure, function, and regulation of STAT proteins. Mol Biosyst. 2:536–550. 2006.PubMed/NCBI View Article : Google Scholar | |
|
Lu R, Zhang YG and Sun J: STAT3 activation in infection and infection-associated cancer. Mol Cell Endocrinol. 451:80–87. 2017.PubMed/NCBI View Article : Google Scholar | |
|
Ghilardi N, Ziegler S, Wiestner A, Stoffel R, Heim MH and Skoda RC: Defective STAT signaling by the leptin receptor in diabetic mice. Proc Natl Acad Sci USA. 93:6231–6235. 1996.PubMed/NCBI View Article : Google Scholar | |
|
Devi SJ, Madhav MS, Kumar GR, Goel AK, Umakanth B, Jahnavi B and Viraktamath BC: Identification of abiotic stress miRNA transcription factor binding motifs (TFBMs) in rice. Gene. 531:15–22. 2013.PubMed/NCBI View Article : Google Scholar | |
|
Zamanighomi M, Lin Z, Wang Y, Jiang R and Wong WH: Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data. Nucleic Acids Res. 45:5666–5677. 2017.PubMed/NCBI View Article : Google Scholar | |
|
Eckert D, Buhl S, Weber S, Jäger R and Schorle H: The AP-2 family of transcription factors. Genome Biol. 6(246)2005.PubMed/NCBI View Article : Google Scholar | |
|
Xu X, Liu Z, Huang H, Zheng K, Hu X, Zhang Z and Qiu M: AP-2α and AP-2β regulate dorsal interneuron specification in the spinal cord. Neuroscience. 340:232–242. 2017.PubMed/NCBI View Article : Google Scholar | |
|
Suyama K, Kabuyama Y, Suzuki S, Kawasaki Y, Suzuki J, Suzuki H and Homma Y: Induction of transcription factor AP-2 by cytokines and prostaglandins in cultured mesangial cells. Am J Nephrol. 21:307–314. 2001.PubMed/NCBI View Article : Google Scholar | |
|
Mi X, Tang W, Chen X, Liu F and Tang X: Mitofusin 2 attenuates the histone acetylation at collagen IV promoter in diabetic nephropathy. J Mol Endocrinol. 57:233–249. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Hobert O: Gene regulation by transcription factors and microRNAs. Science. 319:1785–1786. 2008.PubMed/NCBI View Article : Google Scholar | |
|
He L and Hannon GJ: MicroRNAs: Small RNAs with a big role in gene regulation. Nat Rev Genet. 5:522–531. 2004.PubMed/NCBI View Article : Google Scholar | |
|
Dieter C, Assmann TS, Costa AR, Canani LH, de Souza BM, Bauer AC and Crispim D: MiR-30e-5p and MiR-15a-5p expressions in plasma and urine of type 1 diabetic patients with diabetic kidney disease. Front Genet. 10(563)2019.PubMed/NCBI View Article : Google Scholar | |
|
Sun SQ, Zhang T, Ding D, Zhang WF, Wang XL, Sun Z, Hu LH, Qin SY, Shen LH and He B: Circulating MicroRNA-188, -30a, and -30e as early biomarkers for contrast-induced acute kidney injury. J Am Heart Assoc. 5(e004138)2016.PubMed/NCBI View Article : Google Scholar | |
|
Galicia JC, Naqvi AR, Ko CC, Nares S and Khan AA: MiRNA-181a regulates Toll-like receptor agonist-induced inflammatory response in human fibroblasts. Genes Immun. 15:333–337. 2014.PubMed/NCBI View Article : Google Scholar | |
|
Yuan J, Ji H, Xiao F, Lin Z, Zhao X, Wang Z, Zhao J and Lu J: MicroRNA-340 inhibits the proliferation and invasion of hepatocellular carcinoma cells by targeting JAK1. Biochem Biophys Res Commun. 483:578–584. 2017.PubMed/NCBI View Article : Google Scholar | |
|
Li Y, Fan X, He X, Sun H, Zou Z, Yuan H, Xu H, Wang C and Shi X: MicroRNA-466l inhibits antiviral innate immune response by targeting interferon-alpha. Cell Mol Immunol. 9:497–502. 2012.PubMed/NCBI View Article : Google Scholar | |
|
Ma F, Liu X, Li D, Wang P, Li N, Lu L and Cao X: MicroRNA-466l upregulates IL-10 expression in TLR-triggered macrophages by antagonizing RNA-binding protein tristetraprolin-mediated IL-10 mRNA degradation. J Immunol. 184:6053–6059. 2010.PubMed/NCBI View Article : Google Scholar | |
|
Rout AK, Udgata SR, Dehury B, Pradhan SP, Swain HS, Behera BK and Das BK: Structural bioinformatics insights into the CARD-CARD interaction mediated by the mitochondrial antiviral-signaling protein of black carp. J Cell Biochem. 120:12534–12543. 2019.PubMed/NCBI View Article : Google Scholar | |
|
Creagh EM and O'Neill LA: TLRs, NLRs and RLRs: A trinity of pathogen sensors that co-operate in innate immunity. Trends Immunol. 27:352–357. 2006.PubMed/NCBI View Article : Google Scholar |
