| BackgroundSepsis-induced acute kidney injury(SI-AKI)is the most common complication in sepsis patients,with 60%AKI in septic shock patients and 3-5 times 60-day mortality than non-AKI patients,reaching 40-60%.The prognosis asked for early diagnosis based on creatinine and urine output.The abundance of SI-AKI biomarkers was in the study,but none could replace Scr with satisfy specificity and sensitivity.Recently,rapidly developed omics technologies provided various bioinformatics analysis methods,such as Phenome-wide Mendelian randomization study(MR-pheWAS)and cell-free DNA(cfDNA)methylation patterns in injury cell,for mining novel biomarkers and revealing SI-AKI mechanisms.For example,Uromodulin(UMOD),the most abundant protein in normal human urine,is reported to be a potential biomarker in AKI,but the results are inconsistent.During the pathogenesis of SI-AKI,the inflammation process could lead tubular epithelial cells(TECs),macrophages,and vascular endothelial cells to different forms of programmed cell death(PCD),such as apoptosis,pyroptosis,and necroptosis.Classicly,each of the three forms of PCD has its initiators,effectors,and executors,the new evidence indicated extensive crosstalk among them,and there is a dynamic molecular regulatory network rather than an isolated pathway for a single PCD,named as PANoptosis and still not clear in SI-AKI.Therefore,identifying the PANoptosis and PANoptosome in SI-AKI will be significant in disclosing the mechanism of potential therapeutic targets.Aim1.Utilizing publicly available Genome-wide association study(GWAS)databases and SI-AKI transcriptome data to screen potential causal molecules and biomarkers for SIAKI.Conducting meta-analysis to evaluate the potential correlation between urinary UMOD(uUMOD)and AKI.2.Compare the difference of baseline data in sepsis non AKI(SN-AKI)and SI-AKI patients by establishing patients cohort,collecting blood/urine samples and clinical data.Detect the potential biomarkers identified through database mining and neutrophil gelatinase-associated lipocalin levels,and evaluate their diagnostic efficacy.3.Evaluate the difference in cfDNA concentration between SN-AKI and SI-AKI,and use methylation modification patterns to trace the kidney epithelial cells origin of cfDNA to assess its diagnostic value for SI-AKI.4.Construct LPS-induced SI-AKI animal model to verify the biomarkers identified through bioinformatics analysis in SI-AKI mice renal tissue.To evaluate whether PANoptosis is activated in SI-AKI kidney tissue.5.To observe whether the PANoptosome sensor Z-DNA binding protein 1(ZBP1)is activated in SI-AKI kidney tissue and explore the composition of PANoptosome.To observe the regulatory relationship of PANoptosome components in SI-AKI using RIPK1 inhibitor Nec-1 treatment in mice and ZBP1 gene knockout mice.MethodPart 1:Mining biomarkers for SI-AKI based on public data1.Using the protein quantitative trait loci(pQTLs)data published by Nasa SinnottArmstrong et al.in Nature Genetics,we obtained 791 pQTLs as instrumental variables.We then conducted MR-PheWAS analysis on AKI GWAS data of 456,348 European populations published by Jiang Lan et al.in 2021.We identified molecules that have a potential causal relationship with AKI,with P<0.05 as the threshold for potential positive results.2.We searched for datasets in the GEO database containing kidney tissue related to SIAKI by querying ’sepsis’ AND ’acute kidney injury’.Differentially expressed genes(DEGs)were obtained using linear models and empirical Bayesian models,and then gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis was performed to identify downstream enriched pathways.We further conducted protein-protein interaction(PPI)network analysis to identify hub genes and hub modules,and obtained common DEGs through combining different analysis.Top DEGs,hub genes,and common DEGs were selected according to there biological function as biomarkers for downstream validation and functional analysis.3.Using a combination of keywords and Mesh terms,we searched for articles related to UMOD and AKI in both Chinese and English databases,and extracted the concentration of uUMOD of AKI and non-AKI participants.The standardized mean difference(SMD)was calculated to compare the difference of uUMOD between the two groups.4.Subgroup analysis was conducted based on surgery-related AKI,age,and detection time to further evaluate the difference of uUMOD between AKI and non-AKI patients.Part 2:Establishment the sepsis patient cohort and validation of biomarkers1.Sepsis patients were enrolled from the Emergency department of Peking Union Medical College Hospital according to the Sepsis 3.0 criteria.Baseline blood and urine samples were collected,along with clinical data such as gender,age,baseline renal function,blood routine,urine routine,electrolytes,coagulation,SOFA score and liver function.2.Randomly selected SI-AKI and the matched sepsis patients selected by propensity score to detect novel biomarkers obtained from public databases using ELISA method.The difference in biomarker levels between the two groups was compared using t-tests or non-parametric tests based on the distribution of the data.The area under the curve(AUC)was calculated to evaluate the sensitivity and specificity.3.Plasma samples were collected from SN-AKI and SI-AKI patients.GM-seq technology was used to detect plasma cfDNA methylation patterns.The UXM algorithm and non-negative least squares algorithm were used to fit the input sample and infer the relative contribution of the target cell source of cfDNA for cell tracing.We analyzed the difference of the level of kidney epithelium origin cfDNA according to AKI,septic shock,coagulation disorders,age,and hypertension subgroup and evaluated the diagnostic value of the kidney epithelium origin cfDNA.Differential methylation regions(DMR)were analyzed,and then we performed GO/KEGG pathway enrichment analysis using these DMR related genes.4.EXODUS and differential centrifugation were used to extract urinary exosomes and verify the exosomes for subsequent extracellular vesicle proteomics analysis.Part 3:Mechanisms of PANoptosis in SI-AKI1.We established the SI-AKI animal model by intraperitoneal injection of LPS(O111:B4)at a dose of 9mg/kg of 6-8 week-old male C57BL/6J mice.The mice were sacrificed 24 hours after injection.We examined the serum Creatinine(sCr),urea nitrogen(BUN),inflammatory factors,kidney injury molecules,and renal pathology to verify the SI-AKI model.2.The expression of biomarkers mined from bioinformatics analysis were detected in the kidney tissue and serum.3.We examined the expression changes of key molecules in the apoptosis,pyroptosis,and necroptosis pathways by immunoblotting,immunofluorescence,and immunohistochemistry.Immunoprecipitation was conducted to detect the possible composition of PANoptosome.4.We detect the effect of RIPKI inhibitor Nec-1 on SI-AKI mice by intraperitoneal injection.The mice were divided into four groups:pretreatment group(injected with 1.65kg/mg of Nec-1 15 minutes prior),treatment group(injected with 1.65kg/mg of Nec-1 3 hours after LPS treatment),control group(equal volume of saline),and LPS treatment group(injected with 9mg/kg of LPS intraperitoneally).We detected and analyzed the changes in sCr and BUN in different groups.5.A kind gift of ZBP1 knockout mice was genotyped for preparation of the regulation mechanism between PANoptosome components.ResultsPart 1:Mining of biomarkers for SI-AKI based on public data1.A total of 61 potential causal proteins were identified through MR-pheWAS analysis,including 34 protective and 27 risk proteins.Protective molecules included HNRNPC,FAS,DVL2,and others,while risk molecules included THBS4,CBLN4,PGLYRP1,and others.2.We selected GSE40180 and GSE125276 datasets among 11 studies according to inclusion criteria.The GSE125276 dataset obtained 316 DEGs,including 84 downregulated genes and 232 upregulated genes.Pathway enrichment analysis acquired interleukin 17(IL-17)pathway,TNF signaling pathway,and complement and coagulation cascades,while the genes in Top hub module included peptidoglycan recognition protein 1(pglyrpl).The hub module pathway enrichment analysis acquired innate immune signaling pathway.The GSE40180 dataset obtained 173 upregulated genes and 214 downregulated genes.The upregulated genes were enriched in the PPAR signaling pathway,bile acid metabolism pathway,and IL-17 signaling pathway,while the downregulated genes were enriched in the PPAR signaling pathway,bile secretion pathway,and glycolysis/gluconeogenesis pathway.The common molecules of MR-pheWAS and GEO datasets including Csf2rb,Slurp 1,and Pglyrp1.3.We screened and conducted meta-analysis of 11 literatures and a random model was selected to merge SMD(-0.71;95%confidence interval(CI):-1.00,-0.42;P<0.001),indicating that uUMOD level was lower in AKI patients than in non-AKI patients.Subgroup analysis showed that age,surgical status,and detection time did not affect this difference.Part 2:Validation of biomarkers in sepsis and SI-AKI patients.1.A total of 350 sepsis patients were included in the study,of whom 224 SI-AKI and 126 SN-AKI.Statistical analysis showed that compared to SN-AKI patients.SI-AKI patients had higher levels of age,blood leukocytes,neutrophils,blood potassium,blood lactate,creatine kinase,creatine kinase-MB,troponin,activated partial thromboplastin time,prothrombin time,D-dimer,international normalized ratio,calcitonin gene-related peptide,high-sensitivity C-reactive protein,and N-terminal pro-B-type natriuretic peptide.Among these,leukocytes(12.4[8.27;17.5]vs.8.85[6.29;14.9]× 109/L,P=0.001)and procalcitonin(8.25[2.20;33.8]vs.0.94[0.27;5.85]ng/ml,P<0.001)showed the most significant differences.Platelet and lymphocyte counts were decreased.2.As for the detection of biomarkers,SI-AKI patients had approximately 7.41-fold higher serum levels of Pglyrpl(P=0.003)and approximately 1.82-fold higher levels of NGAL(P=0.023)than SN-AKI patients.The AUC of the receiver operating characteristic curve(ROC)for pglyrpl was 0.78,wihle for NGAL was 0.72,and there was no significant difference between the two indicators(P = 0.57).The AUC of the combined use of pglyrpl and NGAL for the diagnosis of SI-AKI is 0.8.3.Compared to healthy controls,both SN-AKI and SI-AKI patients showed significantly elevated concentrations of cfDNA(P<0.01)(SN-AKI vs.healthy controls:P<0.001;sepsis vs.healthy controls:P<0.01).Compared to SN-AKI patients,SI-AKI patients had approximately 8.57-fold higher levels of cfDNA.Patients with coagulation disorders had elevated levels of cfDNA(P=0.022)compared to those with normal coagulation function.After tracing the cfDNA source,We observed that the ratio of Kidney-Ep-origin cfDNA in SI-AKI patients was higher than that in SN-AKI patients and healthy controls(compared to healthy controls:P=0.00042,compared with SNAKI:P=0.0033),while there was no significant difference between SN-AKI patients and healthy controls(P=0.95).The concentration of Kidney-Ep-origin cfDNA was significantly higher in the SI-AKI group than in healthy subjects and SN-AKI patients(compared with non-AKI sepsis:0.258 vs.0.012,P=0.0033;compared with healthy subjects:0.258 vs.0.01 1,P=9.6E-7).As for origin of the cfDNA,B cells,neurons,and "other" cells composed of various solid organ cells were increased in SI-AKI patients compared to healthy subjects and SN-AKI patients.Part 3:Mechanisms of PANoptosis in SI-AKI1.We successfully constructed the SI-AKI animal model by intraperitoneal injection of LPS.Compared to the control group,the LPS-treated mice showed significant increases in sCr level(1.19mg/dL vs.0.66mg/dL,P=0.043)and BUN level(16.36mmol/L vs.5.80mmol/L,P<0.001).Serum TNF-α(407.9pg/ml vs.322.3pg/ml,P=0.03)and IL-1b(3343pg/ml vs.1020pg/ml,P<0.001)levels also significantly increased in the LPS-treated mice.The average levels of NGAL mRNA(470.4-fold increase,P<0.001)and Kim-1 mRNA(31.82-fold increase,P=0.001)in the LPStreated mice kidney tissues were significantly higher,as shown by qPCR.Western blot analysis showed increased levels of NGAL protein expression in the kidneys of SIAKI mice,which were approximately 292.5-fold higher than those in the control group(P<0.0001).2.Compared to the control group,the SI-AKI mice had higher levels of fibrinogen gamma chain(FGG)mRNA(560.0 vs.3.3,P=0.004)and protein expression(7.23fold increase,P<0.0001)in kidney tissues.Immunohistochemistry revealed that FGG expressed in the proximal renal tubules of the SI-AKI mice was significantly increased by approximately 39.7-fold(P=0.0017).ELISA results also showed a significant increase in serum FGG levels in the LPS-treated mice(458.3 vs.38.47ng/ml,P=0.0084).3.Immunoblotting results showed that,compared to the control group,the expression levels of mixed lineage kinase domain-like protein(1.44 vs.1.00,P=0.013),receptorinteracting protein kinase 1(1.65 vs.1.00,P=0.002),FAS-associated death domain protein(1.89 vs.1.00,P<0.001),cleaved caspase-3(1.65 vs.1.00,P<0.001),cleaved caspase-7(1.60 vs.1.00,P<0.001),and IL-1b(1.30 vs.1.00,P=0.006)were upregulated in the kidney tissues of the SI-AKI mice,while the expression level of cleaved caspase-8(0.97 vs.1.00,P=0.62)showed no significant difference.Pglyrp1 mRNA expression was increased(9.62 vs.1.18,P=0.006),and its protein expression was increased by approximately 1.84-fold(P=0.007).4.ZBP1 mRNA(13.23 vs.1.072,P<0.001)and protein expression increased 2.795fold(P=0.0015)in SI-AKI mice.Immunofluorescence showed increased ZBP1 in the renal interstitium of SI-AKI mice.Immunoprecipitation demonstrated interactions between Cl-caspase8,RIPK1,Pglyrp1,and ZBP1.Compared with the control group,the LPS group showed significant increases in creatinine and urea nitrogen.Compared with the LPS treatment group,the Nec-1 pretreatment group and the Nec-1 treatment group showed a significant decrease in urea nitrogen(2.49 vs.12.45 mmol/L,P<0.001 for the pretreatment group;2.45 vs.13.25 mmol/L,P<0.001 for the treatment group).SummaryIn this study:1.Novel biomarkers potentially helpful for diagnosing SI-AKI,such as FGG and Pglyrp1,were identified through transcriptome and genome data analysis,as well as bioinformatics methods such as MR-pheWAS.The diagnostic efficacy of serum pglyrp1 was validated in patient.There is a potential correlation between low uUMOD levels and the occurrence of AKI,which is independent of age,surgical status,and detection time.2.A sepsis cohort was established to verify the diagnostic efficacy of pglyrpl in patient serum.Kidney epithelium origin cfDNA was significantly increased in SI-AKI,which can be used as a potential new marker to reflect kidney injury in real time.3.In the SI-AKI animal model,we verified the increase of omics mining markers such as FGG,Pglyrpl,and C3 in SI-AKI kidney tissue.Serum FGG in LPS-treated mice increased and we observed that FGG was mainly expressed in the proximal tubule cells.4.We observed necroptosis(RIPK1,MLKL),pyroptosis(IL-1b),and apoptosis(Cleaved-caspase3,Cleaved-caspase7)molecules were elevated in SI-AKI kidney tissue,and the PANoptosome sensor ZBP1 upregulated in the renal interstitium of SIAKI.ZBP1,Caspase8,RIPK1,and Pglyrp1 were found to interact with each other,potentially as components of PANoptosome,providing potential new insights for the diagnosis and treatment of SI-AKI. |
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