Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury

Innovative insights: ITLN1 modulates renal injury in response to radiation

Radiation-induced kidney injury is a common side effect of radiotherapy, as the pelvic region is in close proximity to the kidneys, posing a risk of inducing radiation-induced kidney injury when treating any pelvic malignancies with radiotherapy. This type of injury typically manifests as chronic kidney disease a few months after radiotherapy, with the potential to progress to end-stage renal disease. Radiation-induced damage involves various components of the kidney, including glomeruli, tubules, interstitium, and extracellular matrix. Therefore, investigating its molecular mechanisms is crucial. In this study, we extensively searched literature databases, selecting recent transcriptomic studies related to acute kidney injury (AKI) published in the past decade. We downloaded the raw RNA sequencing datasets GSE30718 and GSE66494 related to AKI from the GEO database and identified that intestinal-type lectin ITLN1 plays a significant role in regulating radiation-induced kidney injury in rats. Differential gene analysis was performed using chip data from the GEO database, and further bioinformatics analysis identified 13 genes that may be involved in regulating kidney injury, with ITLN1 being the most relevant to kidney damage, thus selected as the target gene for this study. Subsequently, a rat model of radiation-induced kidney injury was established for experimental validation, assessing kidney tissue morphology and injury extent through staining observation and immunohistochemical staining. The protective effect of ITLN1 on kidney function was evaluated by measuring changes in rat body weight and blood pressure, serum kidney injury markers, and kidney structure. The experimental results indicate that overexpression of ITLN1 can improve kidney function in rats with radiation-induced kidney injury by activating the Akt/GSK-3β/Nrf2 signaling pathway, suppressing oxidative stress, cell apoptosis, inflammation, cellular senescence, and fibrosis. This study highlights the significant role of ITLN1 in regulating kidney injury, providing a novel target for future treatments of radiation-induced kidney injury.

Identification of mitochondria-related biomarkers in childhood allergic asthma

BACKGROUND

The mechanism of mitochondria-related genes (MRGs) in childhood allergic asthma (CAS) was unclear. The aim of this study was to find new biomarkers related to MRGs in CAS.

METHODS

This research utilized two CAS-related datasets (GSE40888 and GSE40732) and extracted 40 MRGs from the MitoCarta3.0 Database. Initially, differential expression analysis was performed on CAS and control samples in the GSE40888 dataset to obtain the differentially expressed genes (DEGs). Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. Protein protein interactions (PPI) network of DE-MRGs was created and the top 10 genes in the degree ranking of Maximal Clique Centrality (MCC) algorithm were defined as feature genes. Hub genes were obtained from the intersection genes from the Least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) algorithms. Additionally, the expression validation was conducted, functional enrichment analysis, immune infiltration analysis were finished, and transcription factors (TFs)-miRNA-mRNA regulatory network was constructed.

RESULTS

A total of 1505 DEGs were obtained from the GSE40888, and 44 DE-MRGs were obtained. A PPI network based on these 44 DE-MRGs was created and revealed strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of genes from the LASSO and XGBoost algorithms based on 10 signature genes which obtained from PPI. In addition, hub genes-based alignment diagram showed good diagnostic performance. The results of Gene Set Enrichment Analysis (GSEA) suggested that hub genes were closely related to mismatch repair. The B cells naive cells were significantly expressed between CAS and control groups, and MTIF3 was most strongly negatively correlated with B cells naive. In addition, the expression of MTIF3 and MRPS26 may have influenced the inflammatory response in CAS patients by affecting mitochondria-related functions. The quantitative real-time polymerase chain reaction (qRT‒PCR) results showed that four hub genes were all down-regulated in the CAS samples.

CONCLUSION

NDUFAF7, MTIF3, MRPS26, and NDUFAF1 were identified as an MRGs-related biomarkers in CAS, which provides some reference for further research on CAS.

Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury

PURPOSE

Acute kidney injury (AKI) is one of the most common functional injuries observed in trauma patients. However, certain trauma medications may exacerbate renal injury. Therefore, the early detection of trauma-related AKI holds paramount importance in improving trauma prognosis.

METHODS

Qualified datasets were selected from public databases, and common differentially expressed genes related to trauma-induced AKI and hub genes were identified through enrichment analysis and the establishment of protein-protein interaction (PPI) networks. Additionally, the specificity of these hub genes was investigated using the sepsis dataset and conducted a comprehensive literature review to assess their plausibility. The raw data from both datasets were downloaded using R software (version 4.2.1) and processed with the "affy" package19 for correction and normalization.

RESULTS

Our analysis revealed 585 upregulated and 629 downregulated differentially expressed genes in the AKI dataset, along with 586 upregulated and 948 downregulated differentially expressed genes in the trauma dataset. Concurrently, the establishment of the PPI network and subsequent topological analysis highlighted key hub genes, including CD44, CD163, TIMP metallopeptidase inhibitor 1, cytochrome b-245 beta chain, versican, membrane spanning 4-domains A4A, mitogen-activated protein kinase 14, and early growth response 1. Notably, their receiver operating characteristic curves displayed areas exceeding 75%, indicating good diagnostic performance. Moreover, our findings postulated a unique molecular mechanism underlying trauma-related AKI.

CONCLUSION

This study presents an alternative strategy for the early diagnosis and treatment of trauma-related AKI, based on the identification of potential biomarkers and therapeutic targets. Additionally, this study provides theoretical references for elucidating the mechanisms of trauma-related AKI.

Blockage of S100A8/A9 ameliorates septic nephropathy in mice

Septic acute kidney injury (AKI) is the commonest cause of complication of sepsis in intensive care units, but its pathophysiology remains unclear. Calprotectin (S100A8/A9), which is a damage-associated molecular patterns (DAMPs) molecule, exerts a critical role in modulating leukocyte recruitment and inflammatory response during various diseases. However, role of S100A8/A9 in septic AKI is largely unknown. In this research, Septic AKI was triggered by cecal ligation and puncture (CLP) operation in wild-type mice, which treated with or without an S100A9 inhibitor, Paquinimod (Paq, 10 mg/kg) that prevents S100A8/A9 to bind to Toll-like receptor 4 (TLR4). Renal function, pathological changes, cell death, and oxidative stress were evaluated. Our research indicated that the mRNA and protein expression of S100A9 are time-dependently elevated in the kidney following CLP. Moreover, the administration of Paq for 24 h significantly improved CLP-induced renal dysfunction and pathological alterations compared with vehicle treatment in mice. These beneficial effects were associated with the inhibition of CLP-triggered renal tubular epithelial cell apoptosis, inflammation, superoxide production, and mitochondrial dynamic imbalance. What's more, we further confirmed the above findings by cell co-culture experiments. Our study demonstrates that S100A9 is a prominent protein to lead to septic AKI, and the selective inhibition of S100A9 could represent a new therapeutic approach which can treat septic AKI.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Background

Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.

Methods

Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.

Results

Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56^dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.

Conclusion

Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.

Identification of potential biomarkers and therapeutic targets for posttraumatic acute respiratory distress syndrome

BACKGROUND

Despite improved supportive care, posttraumatic acute respiratory distress syndrome (ARDS) mortality has improved very little in recent years. Additionally, ARDS diagnosis is delayed or missed in many patients. We analyzed co-differentially expressed genes (co-DEGs) to explore the relationships between severe trauma and ARDS to reveal potential biomarkers and therapeutic targets for posttraumatic ARDS.

METHODS

Two gene expression datasets (GSE64711 and GSE76293) were downloaded from the Gene Expression Omnibus. The GSE64711 dataset included a subset of 244 severely injured trauma patients and 21 healthy controls. GSE76293 specimens were collected from 12 patients with ARDS who were recruited from trauma intensive care units and 11 age- and sex-matched healthy volunteers. Trauma DEGs and ARDS DEGs were identified using the two datasets. Subsequently, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network analyses were performed to elucidate the molecular functions of the DEGs. Then, hub genes of the co-DEGs were identified. Finally, to explore whether posttraumatic ARDS and septic ARDS are common targets, we included a third dataset (GSE100159) for corresponding verification.

RESULTS

90 genes were upregulated and 48 genes were downregulated in the two datasets and were therefore named co-DEGs. These co-DEGs were significantly involved in multiple inflammation-, immunity- and neutrophil activation-related biological processes. Ten co-upregulated hub genes (GAPDH, MMP8, HGF, MAPK14, LCN2, CD163, ENO1, CD44, ARG1 and GADD45A) and five co-downregulated hub genes (HERC5, IFIT2, IFIT3, RSAD2 and IFIT1) may be considered potential biomarkers and therapeutic targets for posttraumatic ARDS. Through the verification of the third dataset, posttraumatic ARDS may have its own unique targets worthy of further exploration.

CONCLUSION

This exploratory analysis supports a relationship between trauma and ARDS pathophysiology, specifically in relationship to the identified hub genes. These data may serve as potential biomarkers and therapeutic targets for posttraumatic ARDS.

ADAM10-a "multitasker" in sepsis: focus on its posttranslational target

BACKGROUND

Sepsis has a complex pathogenesis in which the uncontrolled systemic inflammatory response triggered by infection leads to vascular barrier disruption, microcirculation dysfunction and multiple organ dysfunction syndrome. Numerous recent studies reveal that a disintegrin and metalloproteinase 10 (ADAM10) acts as a "molecular scissor" playing a pivotal role in the inflammatory response during sepsis by regulating proteolysis by cleaving various membrane protein substrates, including proinflammatory cytokines, cadherins and Notch, which are involved in intercellular communication. ADAM10 can also act as the cellular receptor for Staphylococcus aureus α-toxin, leading to lethal sepsis. However, its substrate-specific modulation and precise targets in sepsis have not yet to be elucidated.

METHODS

We performed a computer-based online search using PubMed and Google Scholar for published articles concerning ADAM10 and sepsis.

CONCLUSIONS

In this review, we focus on the functions of ADAM10 in sepsis-related complex endothelium-immune cell interactions and microcirculation dysfunction through the diversity of its substrates and its enzymatic activity. In addition, we highlight the posttranslational mechanisms of ADAM10 at specific subcellular sites, or in multimolecular complexes, which will provide the insight to intervene in the pathophysiological process of sepsis caused by ADAM10 dysregulation.

Norepinephrine May Exacerbate Septic Acute Kidney Injury: A Narrative Review

Sepsis, the most serious complication of infection, occurs when a cascade of potentially life-threatening inflammatory responses is triggered. Potentially life-threatening septic shock is a complication of sepsis that occurs when hemodynamic instability occurs. Septic shock may cause organ failure, most commonly involving the kidneys. The pathophysiology and hemodynamic mechanisms of acute kidney injury in the case of sepsis or septic shock remain to be elucidated, but previous studies have suggested multiple possible mechanisms or the interplay of multiple mechanisms. Norepinephrine is used as the first-line vasopressor in the management of septic shock. Studies have reported different hemodynamic effects of norepinephrine on renal circulation, with some suggesting that it could possibly exacerbate acute kidney injury caused by septic shock. This narrative review briefly covers the updates on sepsis and septic shock regarding definitions, statistics, diagnosis, and management, with an explanation of the putative pathophysiological mechanisms and hemodynamic changes, as well as updated evidence. Sepsis-associated acute kidney injury remains a major burden on the healthcare system. This review aims to improve the real-world clinical understanding of the possible adverse outcomes of norepinephrine use in sepsis-associated acute kidney injury.

Neutrophil Gelatinase-Associated Lipocalin as a Predictor of Acute Kidney Injury in Children With Shock: A Prospective Study

BACKGROUND

The current definition of acute kidney injury (AKI) is based on serum creatinine (SrCr) and urine output, limited by delayed identification of such patients. Plasma neutrophil gelatinase-associated lipocalin (NGAL) is considered an early diagnostic and highly predictive biomarker of AKI.

OBJECTIVE

To determine the diagnostic accuracy of NGAL for AKI compared with creatinine clearance for early detection of AKI in children with shock receiving inotropic support.

METHODS

Critically ill children requiring inotropic support in the pediatric intensive care unit were enrolled prospectively. SrCr and NGAL values were obtained three times at six, 12, and 48 hours after vasopressor initiation. Patients with AKI were defined as having loss of >25% renal function based on creatinine clearance within 48 hours. NGAL level of more than 150 ng/dl was suggestive of the diagnosis of AKI. Receiver operator characteristic curves were generated for NGAL and SrCr to compare the predictive ability of both at 0, 12, and 48 hours of starting vasopressor support.  Results: A total of 94 patients were enrolled. The mean age was 43±50.95 months. Most common primary diagnoses were related to the cardiovascular system (46%). Twenty-nine patients (31%) died during the hospital stay. Thirty-four patients (36%) developed AKI within 48 hours following shock. The area under the curve (AUC) for NGAL at a cutoff of 150 ng/ml was 0.70, 0.74, and 0.73 at six-hour, 12-hour, and 48-hour follow-up, respectively. NGAL had a sensitivity of 85.3% and specificity of 50% at 0 hours of follow-up for diagnosis of AKI.

CONCLUSION

Serum NGAL has better sensitivity and AUC compared to SrCr for early diagnosis of AKI in children admitted with shock.

Co-expression and interaction network analysis reveals dysregulated neutrophil and T-cell activation as the core mechanism associated with septic shock

Septic shock as a subset of sepsis, has a much higher mortality, while the mechanism is still elusive. This study was aimed at identifying core mechanisms associated with septic shock and its high mortality by investigating transcriptome data. We screened 72 septic-shock-associated genes (SSAGs) with differential expression between septic shock and sepsis in the discovery dataset. Further gene set enrichment analysis identified upregulated neutrophil activation and impaired T-cell activation in septic shock. Co-expression analysis revealed nine co-expressed gene modules. In addition, we determined twenty-one prognostic SSAGs using cox regression analysis in an independent dataset. Moreover, protein-protein interaction (PPI) network revealed two clusters. Among these neutrophil activation was enriched in the most positively-related modules and the cluster2 PPI network, while T-cell activation was enriched in both the most negatively-related module and one of the most positively-related modules as well as the cluster1 PPI network. ELANE, LCN2 and IFI44 were identified as hub genes with CytoHubba methods and semantic similarity analysis. Notably, ELANE was the only prognostic gene and was further validated in an external dataset. Blood neutrophil count was demonstrated to increase in septic shock and be a risky factor of prognosis based on clinical data. In conclusions, septic shock is associated with upregulated neutrophil activation and dysregulated T-cell activation. Three hub genes might have potentials as sensitive markers for the further translational research and ELANE could be a robust prognostic biomarker and effective therapeutic target.

Predicting the prognosis in patients with sepsis by a pyroptosis-related gene signature

Background

Sepsis remains a life-threatening disease with a high mortality rate that causes millions of deaths worldwide every year. Many studies have suggested that pyroptosis plays an important role in the development and progression of sepsis. However, the potential prognostic and diagnostic value of pyroptosis-related genes in sepsis remains unknown.

Methods

The GSE65682 and GSE95233 datasets were obtained from Gene Expression Omnibus (GEO) database and pyroptosis-related genes were obtained from previous literature and Molecular Signature Database. Univariate cox analysis and least absolute shrinkage and selection operator (LASSO) cox regression analysis were used to select prognostic differentially expressed pyroptosis-related genes and constructed a prognostic risk score. Functional analysis and immune infiltration analysis were used to investigate the biological characteristics and immune cell enrichment in sepsis patients who were classified as low- or high-risk based on their risk score. Then the correlation between pyroptosis-related genes and immune cells was analyzed and the diagnostic value of the selected genes was assessed using the receiver operating characteristic curve.

Results

A total of 16 pyroptosis-related differentially expressed genes were identified between sepsis patients and healthy individuals. A six-gene-based (GZMB, CHMP7, NLRP1, MYD88, ELANE, and AIM2) prognostic risk score was developed. Based on the risk score, sepsis patients were divided into low- and high-risk groups, and patients in the low-risk group had a better prognosis. Functional enrichment analysis found that NOD-like receptor signaling pathway, hematopoietic cell lineage, and other immune-related pathways were enriched. Immune infiltration analysis showed that some innate and adaptive immune cells were significantly different between low- and high-risk groups, and correlation analysis revealed that all six genes were significantly correlated with neutrophils. Four out of six genes (GZMB, CHMP7, NLRP1, and AIM2) also have potential diagnostic value in sepsis diagnosis.

Conclusion

We developed and validated a novel prognostic predictive risk score for sepsis based on six pyroptosis-related genes. Four out of the six genes also have potential diagnostic value in sepsis diagnosis.

Bioinformatics Analysis Identifies TNFRSF1A as a Biomarker of Liver Injury in Sepsis TNFRSF1A is a Biomarker for Septic Liver Injury

Sepsis is a severe disease with high mortality, and liver injury is an independent risk factor for sepsis morbidity and mortality. We analyzed co-differentially expressed genes (co-DEGs) to explore potential biomarkers and therapeutic targets for sepsis-related liver injury. Three gene expression datasets (GSE60088, GSE23767, and GSE71530) were downloaded from the Gene Expression Omnibus (GEO). DEGs were screened between sepsis and control samples using GEO2R. The association of these DEGs with infection and liver disease was analyzed by using the CTD database. GO functional analysis, KEGG pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate the potential molecular mechanism of DEGs. DEGs of different tissues in GSE60088 were analyzed again to obtain specific markers of septic liver injury. Mouse model of sepsis was also established by cecal ligation and puncture (CLP), and the expression of specific markers in liver, lung, and kidney tissues was analyzed using Western blot. Here, we identified 21 DEGs in three datasets with 8 hub genes, all of which showed higher inference scores in liver diseases than bacterial infections. Among them, only TNFRSF1A had a liver-specific differential expression. TNFRSF1A was also confirmed to be specifically reduced in septic liver tissues in mice. Therefore, TNFRSF1A may serve as a potential biomarker for septic liver injury.

Identification of RPS7 as the Biomarker of Ferroptosis in Acute Kidney Injury

Objective

This paper aims to explore novel ferroptosis-related biomarkers for acute kidney injury (AKI).

Methods

Various bioinformatic methods, such as differential expression analysis, functional annotation analysis, machine learning, and chemical-gene network analysis, were used in this study. Furthermore, the expression and proferroptotic role of RPS7 were validated with further bioinformatics analysis and biochemical experiments.

Results

GSE30718 dataset and GSE139061 dataset were used, and the differentially expressed genes (DEGs) were screened. The DEGs were overlapped with ferroptosis-related genes and genes associated with AKI, which led to the identification of four candidate genes. Machine learning and ROC curve analysis were conducted, and RPS7 and TRIB3 were selected for diagnostic model analysis and functional analysis. Finally, the upregulation of RSP7 in cisplatin-induced AKI was validated in cisplatin-induced AKI, and its proferroptotic role was confirmed in cisplatin-treated proximal tubular cells.

Conclusion

Our results indicated that RPS7 might present as a novel ferroptosis-related biomarker for AKI, and it derived ferroptosis to accentuate cisplatin-induced AKI.

Identification of hub genes associated with acute kidney injury induced by renal ischemia–reperfusion injury in mice

Background: Acute kidney injury (AKI) is a severe clinical syndrome, and ischemia–reperfusion injury is an important cause of acute kidney injury. The aim of the present study was to investigate the related genes and pathways in the mouse model of acute kidney injury induced by ischemia–reperfusion injury (IRI-AKI).

Method: Two public datasets (GSE39548 and GSE131288) originating from the NCBI Gene Expression Omnibus (GEO) database were analyzed using the R software limma package, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) and gene set enrichment analysis (GSEA) were performed using the differentially expressed genes. Furthermore, a protein-protein interaction (PPI) network was constructed to investigate hub genes, and transcription factor (TF)–hub gene and miRNA–hub gene networks were constructed. Drugs and molecular compounds that could interact with hub genes were predicted using the DGIdb.

Result: A total of 323 common differentially expressed genes were identified in the renal ischemia–reperfusion injury group compared with the control group. Among these, 260 differentially expressed genes were upregulated and 66 differentially expressed genes were downregulated. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes analysis results showed that these common differentially expressed genes were enriched in positive regulation of cytokine production, muscle tissue development, and other biological processes, indicating that they were involved in mitogen-activated protein kinase (MAPK), PI3K-Akt, TNF, apoptosis, and Epstein–Barr virus infection signaling pathways. Protein-protein interaction analysis showed 10 hub genes, namely, Jun, Stat3, MYC, Cdkn1a, Hif1a, FOS, Atf3, Mdm2, Egr1, and Ddit3. Using the STRUST database, starBase database, and DGIdb database, it was predicted that 34 transcription factors, 161 mi-RNAs, and 299 drugs or molecular compounds might interact with hub genes.

Conclusion: Our findings may provide novel potential biomarkers and insights into the pathogenesis of ischemia–reperfusion injury–acute kidney injury through a comprehensive analysis of Gene Expression Omnibus data, which may provide a reliable basis for early diagnosis and treatment of ischemia–reperfusion injury–acute kidney injury.

Comprehensive analysis of fifteen hub genes to identify a promising diagnostic model, regulated networks, and immune cell infiltration in acute kidney injury

BACKGROUND

Acute kidney injury is a common clinical problem with no sensitive and specific diagnostic biomarkers and definitive treatments. The underlying molecular mechanisms of acute kidney injury are unclear. Therefore, it is pivotal to explore the underlying mechanisms and screen for novel diagnostic biomarkers, and therapeutic targets.

METHODS

The present study identified 15 hub genes by WGCNA analysis. LASSO-based logistic regression analysis was used to select key features and construct a diagnostic model of AKI. In addition, GO and KEGG analyses were performed and TF-mRNA and miRNA-mRNA network analysis and immune infiltration analysis of hub genes were performed to reveal the underlying mechanisms of AKI.

RESULTS

A diagnostic model was constructed by LASSO-based logistic regression analysis and was validated by RT-qPCR based on 15 hub genes. GO and KEGG analyses revealed DEGs were enriched in oxidation-reduction process, cell adhesion, proliferation, migration, and metabolic process. The enriched TFs were BRD2, EP300, ETS1, MYC, SPI1, and ZNF263. The enriched miRNAs were miR-181c-5p, miR-218-5p, miR-485-5p, miR-532-5p and miR-6884-5p. The immune infiltration analysis showed that Macrophages M2 was decreasing significantly revealing a protective factor for further AKI treatment.

CONCLUSIONS

The present study identified 15 hub genes based on WGCNA. Development and validation of a potentially diagnostic model based on 15 hub genes. In addition, exploring the interaction between transcriptional factors and 15 hub genes, and miRNA-mRNA relationship pairs. Furthermore, immune infiltration analysis was performed by analyzing gene expression profiles of AKI. Our study provides some basis for further experimental studies.

Evaluation Value of Serum miR-4299 and miR-16-5p in Risk Stratification of Sepsis-Induced Acute Kidney Injury

Objective

This study was designed to determine the evaluation value of serum miR-4299 and miR-16-5p in risk stratification of sepsis-induced acute kidney injury (SI-AKI).

Methods

A total of 115 sepsis patients were enrolled and assigned to the SI-AKI group (n = 64) or the sepsis-non-AKI group (n = 51) based on the occurrence of AKI, and 72 healthy individuals were enrolled. Fasting venous blood was sampled from every patient before admission, before therapy, and after therapy, followed by quantification of miR-4299 and miR-16-5p by fluorescence quantitative PCR. Receiver operating characteristic (ROC) curves were drawn to evaluate the value of serum miR-16-5p and miR-4299 expression in predicting SI-AKI, and Pearson's correlation analysis was performed to explore the associations of the two with Scr, Cys-C, and KIM-1.

Results

Cases with sepsis, especially SI-AKI, presented significantly downregulated serum miR-4299 and miR-16-5p. After therapy, the expression in them increased. The area under curve (AUC) of serum miR-4299 and miR-16-5p in the prediction value for early diagnosis of SI-AKI was 0.895 (95% CI: 0.839-0.951, cutoff value: 0.780) and 0.838 (95% CI: 0.767-0.909, cutoff value: 0.775), respectively, and the AUC of them in the prediction value for clinical efficacy on the disease were 0.733 (95% CI: 0.645-0.820, cutoff value: 1.115) and 0.776 (95% CI: 0.698-0.855, cutoff value: 1.125), respectively. Serum miR-16-5p and mIR-4299 were negatively correlated with Scr, Cys-C, and KIM-1, separately.

Conclusion

Both miR-16-5p and mIR-4299 are promising factors for early diagnosis of SI-AKI and dynamic evaluation of the efficacy on it.

Kidney single-cell transcriptome profile reveals distinct response of proximal tubule cells to SGLT2i and ARB treatment in diabetic mice

Angiotensin receptor blockers (ARBs) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been used as the standard therapy for patients with diabetic kidney disease (DKD). However, how these two drugs possess additive renoprotective effects remains unclear. Here, we conducted single-cell RNA sequencing to profile the kidney cell transcriptome of db/db mice treated with vehicle, ARBs, SGLT2i, or ARBs plus SGLT2i, using db/m mice as control. We identified 10 distinct clusters of kidney cells with predominant proximal tubular (PT) cells. We found that ARBs had more anti-inflammatory and anti-fibrotic effects, while SGLT2i affected more mitochondrial function in PT. We also identified a new PT subcluster, was increased in DKD, but reversed by the treatments. This new subcluster was also confirmed by immunostaining of mouse and human kidneys with DKD. Together, our study reveals kidney cell-specific gene signatures in response to ARBs and SGLT2i and identifies a new PT subcluster, which provides new insight into the pathogenesis of DKD.

Olfactomedin 4 Is a Biomarker for the Severity of Infectious Diseases

Biomarkers of infectious diseases are essential tools for patient monitoring, diagnostics, and prognostics. Here we review recent advances in our understanding of olfactomedin 4 (OLFM4) in neutrophil biology and of OLFM4 as a new biomarker for certain infectious diseases. OLFM4 is a neutrophil-specific granule protein that is expressed in a subset of human and mouse neutrophils. OLFM4 expression is upregulated in many viral and bacterial infections, as well as in malaria. OLFM4 appears to play an important role in regulating host innate immunity against bacterial infection. Further, higher expression of OLFM4 is associated with severity of disease for dengue virus, respiratory syncytial virus, and malaria infections. In addition, higher expression of OLFM4 or a higher percentage of OLFM4 + neutrophils is associated with poorer outcomes in septic patients. OLFM4 is a promising biomarker and potential therapeutic target in certain infectious diseases.

Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.

Identification of potential biomarkers and immune infiltration in pediatric sepsis via multiple-microarray analysis

Immune adjustment has become a sepsis occurring in the development of an important mechanism that cannot be ignored. This article from the perspective of immune infiltration of pediatric sepsis screening markers, and promote the understanding of disease mechanisms. Bioinformatics integrated six data sets of pediatric sepsis by using the surrogate variable analysis package and then analyzed differentially expressed genes (DEGs), immune infiltration and weighted gene co-expression network analysis of characteristics (WGCNA) of immune infiltration between pediatric sepsis and the control. Common genes of WGCNA and DEGs were used to functional annotation, pathway enrichment analysis and protein-protein interaction network. Support vector machine (SVM), least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to confirm the key genes for the diagnosis of pediatric sepsis. Receiver operating characteristic (ROC) curve, C index, principal component analysis (PCA) and GiViTi calibration band were used to evaluate the diagnostic performance of key genes. Decision curve analysis (DCA) was used to evaluate the clinical application value of key genes. Lastly, the correlation between key genes and immune cells was analyze. NK cells Resting and NK cell activated in pediatric sepsis during immune infiltration were significantly lower than those in the control group, while M1 Macrophages were higher than those in the control group. ROC, C-index, PCA, GiViTi calibration band and DCA indicated that MCEMP1, CD177, MMP8 and OLFM4 had high diagnostic performance for pediatric sepsis. There is a negative correlation between 4 key genes and NK cells resting, NK cells activated. Except for MCEMP1, the other 3 genes were positively correlated with M1 Macrophages. This study revealed differences in immune responses in pediatric sepsis and identified four key genes as potential biomarkers. Pediatric sepsis in pathology maybe understood better by learning about how it develops.

The horizon of pediatric cardiac critical care

Pediatric Cardiac Critical Care (PCCC) is a challenging discipline where decisions require a high degree of preparation and clinical expertise. In the modern era, outcomes of neonates and children with congenital heart defects have dramatically improved, largely by transformative technologies and an expanding collection of pharmacotherapies. Exponential advances in science and technology are occurring at a breathtaking rate, and applying these advances to the PCCC patient is essential to further advancing the science and practice of the field. In this article, we identified and elaborate on seven key elements within the PCCC that will pave the way for the future.

Placenta-specific 8 (PLAC8) mediates inflammation and mobility of the hPDLCs via MEK/ERK signaling pathway

BACKGROUND

Placenta-specific 8 (PLAC8) is reported to regulate cellular functions in the progression of various diseases. However, its role in periodontitis is still unclear.

METHODS

Human periodontal ligament cells (hPDLCs) were treated with lipopolysaccharide of Porphyromonas Gingivalis (LPS-PG) to mimic periodontitis in vitro. Real-time quantitative polymerase chain reaction (RT-qPCR) was performed to measure the mRNA expression levels and western blot was for protein levels. Wound healing and transwell migration assays were performed to assess the cell mobility of hPDLCs. Both mRNA and protein levels of inflammatory cytokines including IFN-γ, IL-17, TNF-α, IL-4, IL-10 and IL-13 were accessed to evaluated process of periodontitis in vitro. Furthermore, the protein expressions of mitogen-activated protein kinase kinase (MEK), extracellular regulated protein kinase (ERK) and their phosphorylated products quantified by western blotting assay were determined to confirm the activation of the MEK/ERK signaling pathway.

RESULTS

The microarray analysis results showed that PLAC8 was most significantly downregulated in periodontium samples of patients with periodontitis, which participates in blood coagulation and integrin-mediated signaling pathway. PLAC8 was also markedly downregulated in the LPS-PG-treated hPDLCs. Moreover, overexpression of PLAC8 ameliorated inflammation and promoted cell mobility of LPS-PG-treated hPDLCs, while inhibition of PLAC8 exhibited the opposite effects. MEK/ERK was selected based on analyses of the protein-protein interaction (PPI) network as the potential signaling pathway interacted with PLAC8, and PLAC8 showed regulatory function on activation of the MEK/ERK pathway. Additionally, U0126, the inhibitor of MEK, abrogated the effects of PLAC8 on inflammation and cell mobility of LPS-PG-treated hPDLCs.

CONCLUSION

Overexpression of PLAC8 protected hPDLCs from dysfunction of inflammation and cell mobility via activating MEK/ERK pathway, indicating a novel therapeutic target for periodontitis.

Multifaced roles of PLAC8 in cancer

The role of PLAC8 in tumorigenesis has been gradually elucidated with the development of research. Although there are common molecular mechanisms that enforce cell growth, the impact of PLAC8 is varied and can, in some instances, have opposite effects on tumorigenesis. To systematically understand the role of PLAC8 in tumors, the molecular functions of PLAC8 in cancer will be discussed by focusing on how PLAC8 impacts tumorigenesis when it arises within tumor cells and how these roles can change in different stages of cancer progression with the ultimate goal of suppressing PLAC8-relevant cancer behavior and related pathologies. In addition, we highlight the diversity of PLAC8 in different tumors and its functional output beyond cancer cell growth. The comprehension of PLAC8's molecular function might provide new target and lead to the development of novel anticancer therapies.

miR-486-5p Serves as a Diagnostic Biomarker for Sepsis and Its Predictive Value for Clinical Outcomes

Background

As a molecular detection method, miRNA can quickly diagnose and prevent diseases, intervene in disease as early as possible, and reduce mortality. This study was to investigate the potential clinical diagnostic and predictive significance of miR-486-5p in sepsis and its correlation with inflammation and disease severity.

Methods

The serum miR-486-5p in 108 sepsis, 60 pneumonia-infected, and 101 healthy controls were detected by RT-qPCR. Spearman coefficient detects the correlation between serum miRNA and disease severity indicators (APACHE II, SOFA scores), and inflammation indicators (CRP, PCT), respectively. The diagnostic significance of miR-486-5p in sepsis was analyzed by the ROC curve. Kaplan–Meier estimator and Cox regression hazards analysis of the predictive significance of serum miR-486-5p in 28-day survival from sepsis.

Results

Serum miR-486-5p was increased in sepsis patients compared with healthy control and pneumonia-infected patients (P < 0.001). And increased serum miR-486-5p was positively associated with disease severity (SOFA score and APACHE II score) and inflammation (CRP and PCT). Serum miR-486-5p can not only identify sepsis patients from healthy controls (AUC = 0.914) but also significantly distinguish sepsis patients from pneumonia-infected patients (AUC = 0.814), showing good potential as a diagnostic biomarker for sepsis. In addition, serum miR-486-5p was an independent predictor of 28-day survival (log-rank P = 0.012), and patients with high levels of miR-486-5p had a poorer overall 28-day survival (HR = 3.057, 95% CI = 1.385–17.817, P = 0.014).

Conclusion

miR-486-5p is a potential diagnostic biomarker for sepsis, and its high level is significantly correlated with the disease severity and inflammation. In addition, miR-486-5p were predictive risk factors for 28-day survival in sepsis patients.

Prolonged Transcriptional Consequences in Survivors of Sepsis

Survivors of sepsis often suffer from prolonged post-critical illness syndrome secondary to the immune system’s reprogramming. It is unclear if this process is static and pervasive due to methodological difficulties studying long-term outcomes of sepsis. The purpose of this study is to evaluate transcriptional profiles longitudinally in Drosophila melanogaster in the aftermath of sepsis to provide preliminary data for targets playing a role in post-sepsis immunostasis. Adult Drosophila melanogaster were infected with E. coli, and survivors were euthanized at 7, 14, and 21 days. Control flies were subjected to sham stress. Gene profiling was done with RNA-seq, and potential miRNA factors were computed. Profiling identified 55 unique genes at seven days, 61 unique genes at 14 days, and 78 genes at 21 days in sepsis survivors vs. sham control. Each post-sepsis timepoint had a distinctive transcriptional pattern with a signature related to oxidative stress at seven days, neuronal signal transduction at 14 days, and metabolism at 21 days. Several potential miRNA patterns were computed as potentially affecting several of the genes expressed in sepsis survivors. Our study demonstrated that post-sepsis changes in the transcriptome profile are dynamic and extend well into the Drosophila melanogaster natural life span.