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Schlapbach LJ, Ganesamoorthy D, Wilson C, Raman S, George S, Snelling PJ, Phillips N, Irwin A, Sharp N, Le Marsney R, Chavan A, Hempenstall A, Bialasiewicz S, MacDonald AD, Grimwood K, Kling JC, McPherson SJ, Blumenthal A, Kaforou M, Levin M, Herberg JA, Gibbons KS, Coin LJM. Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study. THE LANCET. CHILD & ADOLESCENT HEALTH 2024; 8:325-338. [PMID: 38513681 DOI: 10.1016/s2352-4642(24)00017-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Sepsis is defined as dysregulated host response to infection that leads to life-threatening organ dysfunction. Biomarkers characterising the dysregulated host response in sepsis are lacking. We aimed to develop host gene expression signatures to predict organ dysfunction in children with bacterial or viral infection. METHODS This cohort study was done in emergency departments and intensive care units of four hospitals in Queensland, Australia, and recruited children aged 1 month to 17 years who, upon admission, underwent a diagnostic test, including blood cultures, for suspected sepsis. Whole-blood RNA sequencing of blood was performed with Illumina NovaSeq (San Diego, CA, USA). Samples with completed phenotyping, monitoring, and RNA extraction by March 31, 2020, were included in the discovery cohort; samples collected or completed thereafter and by Oct 27, 2021, constituted the Rapid Paediatric Infection Diagnosis in Sepsis (RAPIDS) internal validation cohort. An external validation cohort was assembled from RNA sequencing gene expression count data from the observational European Childhood Life-threatening Infectious Disease Study (EUCLIDS), which recruited children with severe infection in nine European countries between 2012 and 2016. Feature selection approaches were applied to derive novel gene signatures for disease class (bacterial vs viral infection) and disease severity (presence vs absence of organ dysfunction 24 h post-sampling). The primary endpoint was the presence of organ dysfunction 24 h after blood sampling in the presence of confirmed bacterial versus viral infection. Gene signature performance is reported as area under the receiver operating characteristic curves (AUCs) and 95% CI. FINDINGS Between Sept 25, 2017, and Oct 27, 2021, 907 patients were enrolled. Blood samples from 595 patients were included in the discovery cohort, and samples from 312 children were included in the RAPIDS validation cohort. We derived a ten-gene disease class signature that achieved an AUC of 94·1% (95% CI 90·6-97·7) in distinguishing bacterial from viral infections in the RAPIDS validation cohort. A ten-gene disease severity signature achieved an AUC of 82·2% (95% CI 76·3-88·1) in predicting organ dysfunction within 24 h of sampling in the RAPIDS validation cohort. Used in tandem, the disease class and disease severity signatures predicted organ dysfunction within 24 h of sampling with an AUC of 90·5% (95% CI 83·3-97·6) for patients with predicted bacterial infection and 94·7% (87·8-100·0) for patients with predicted viral infection. In the external EUCLIDS validation dataset (n=362), the disease class and disease severity predicted organ dysfunction at time of sampling with an AUC of 70·1% (95% CI 44·1-96·2) for patients with predicted bacterial infection and 69·6% (53·1-86·0) for patients with predicted viral infection. INTERPRETATION In children evaluated for sepsis, novel host transcriptomic signatures specific for bacterial and viral infection can identify dysregulated host response leading to organ dysfunction. FUNDING Australian Government Medical Research Future Fund Genomic Health Futures Mission, Children's Hospital Foundation Queensland, Brisbane Diamantina Health Partners, Emergency Medicine Foundation, Gold Coast Hospital Foundation, Far North Queensland Foundation, Townsville Hospital and Health Services SERTA Grant, and Australian Infectious Diseases Research Centre.
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Affiliation(s)
- Luregn J Schlapbach
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Department of Intensive Care and Neonatology, and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Paediatric Intensive Care Unit, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia.
| | - Devika Ganesamoorthy
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Clare Wilson
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Sainath Raman
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Paediatric Intensive Care Unit, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Shane George
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Department of Emergency Medicine, Gold Coast University Hospital, Southport, QLD, Australia; School of Medicine and Dentistry and the Menzies Health Institute Queensland, Griffith University, Southport, QLD, Australia
| | - Peter J Snelling
- Department of Emergency Medicine, Gold Coast University Hospital, Southport, QLD, Australia; School of Medicine and Dentistry and the Menzies Health Institute Queensland, Griffith University, Southport, QLD, Australia
| | - Natalie Phillips
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Emergency Department, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Adam Irwin
- Faculty of Medicine, UQ Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia; Infection Management and Prevention Services, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Natalie Sharp
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Paediatric Intensive Care Unit, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Renate Le Marsney
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Arjun Chavan
- Paediatric Intensive Care Unit, Townsville University Hospital, Townsville, QLD, Australia
| | | | - Seweryn Bialasiewicz
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, and Queensland Paediatric Infectious Diseases Laboratory, The University of Queensland, Brisbane, QLD, Australia
| | - Anna D MacDonald
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Keith Grimwood
- School of Medicine and Dentistry and the Menzies Health Institute Queensland, Griffith University, Southport, QLD, Australia; Department of Infectious Disease and Paediatrics, Gold Coast Health, Southport, QLD, Australia
| | - Jessica C Kling
- Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | | | - Antje Blumenthal
- Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Myrsini Kaforou
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Michael Levin
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Jethro A Herberg
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Kristen S Gibbons
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Lachlan J M Coin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
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Novak T, Crawford JC, Hahn G, Hall MW, Thair SA, Newhams MM, Chou J, Mourani PM, Tarquinio KM, Markovitz B, Loftis LL, Weiss SL, Higgerson R, Schwarz AJ, Pinto NP, Thomas NJ, Gedeit RG, Sanders RC, Mahapatra S, Coates BM, Cvijanovich NZ, Ackerman KG, Tellez DW, McQuillen P, Kurachek SC, Shein SL, Lange C, Thomas PG, Randolph AG. Transcriptomic profiles of multiple organ dysfunction syndrome phenotypes in pediatric critical influenza. Front Immunol 2023; 14:1220028. [PMID: 37533854 PMCID: PMC10390830 DOI: 10.3389/fimmu.2023.1220028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 08/04/2023] Open
Abstract
Background Influenza virus is responsible for a large global burden of disease, especially in children. Multiple Organ Dysfunction Syndrome (MODS) is a life-threatening and fatal complication of severe influenza infection. Methods We measured RNA expression of 469 biologically plausible candidate genes in children admitted to North American pediatric intensive care units with severe influenza virus infection with and without MODS. Whole blood samples from 191 influenza-infected children (median age 6.4 years, IQR: 2.2, 11) were collected a median of 27 hours following admission; for 45 children a second blood sample was collected approximately seven days later. Extracted RNA was hybridized to NanoString mRNA probes, counts normalized, and analyzed using linear models controlling for age and bacterial co-infections (FDR q<0.05). Results Comparing pediatric samples collected near admission, children with Prolonged MODS for ≥7 days (n=38; 9 deaths) had significant upregulation of nine mRNA transcripts associated with neutrophil degranulation (RETN, TCN1, OLFM4, MMP8, LCN2, BPI, LTF, S100A12, GUSB) compared to those who recovered more rapidly from MODS (n=27). These neutrophil transcripts present in early samples predicted Prolonged MODS or death when compared to patients who recovered, however in paired longitudinal samples, they were not differentially expressed over time. Instead, five genes involved in protein metabolism and/or adaptive immunity signaling pathways (RPL3, MRPL3, HLA-DMB, EEF1G, CD8A) were associated with MODS recovery within a week. Conclusion Thus, early increased expression of neutrophil degranulation genes indicated worse clinical outcomes in children with influenza infection, consistent with reports in adult cohorts with influenza, sepsis, and acute respiratory distress syndrome.
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Affiliation(s)
- Tanya Novak
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, United States
- Department of Anaesthesia, Harvard Medical School, Boston, MA, United States
- National Institute of Allergy and Infectious Diseases (NIAID), Centers of Excellence for Influenza Research and Response (CEIRR), Center for Influenza Disease and Emergence Response (CIDER), Athens, GA, United States
| | - Jeremy Chase Crawford
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, United States
- National Institute of Allergy and Infectious Diseases (NIAID), Centers of Excellence for Influenza Research and Response (CEIRR), St. Jude Children's Research Hospital, Memphis, TN, United States
- Department of Immunology, St Jude Children’s Research Hospital, Memphis, TN, United States
| | - Georg Hahn
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Mark W. Hall
- Division of Critical Care Medicine, Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Simone A. Thair
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, United States
- Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Margaret M. Newhams
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, United States
- National Institute of Allergy and Infectious Diseases (NIAID), Centers of Excellence for Influenza Research and Response (CEIRR), Center for Influenza Disease and Emergence Response (CIDER), Athens, GA, United States
| | - Janet Chou
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Peter M. Mourani
- Department of Pediatrics, Section of Critical Care Medicine, University of Arkansas for Medical Sciences and Arkansas Children’s Research Institute, Little Rock, AR, United States
| | - Keiko M. Tarquinio
- Division of Critical Care Medicine, Department of Pediatrics, Emory University School of Medicine, Children’s Healthcare of Atlanta, Atlanta, GA, United States
| | - Barry Markovitz
- Department of Anesthesiology Critical Care Medicine, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | - Laura L. Loftis
- Division of Critical Care Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Scott L. Weiss
- Nemours Children’s Hospital Delaware, Critical Care Medicine, Wilmington, DE, United States
| | - Renee Higgerson
- Pediatric Critical Care Medicine, St. David’s Children’s Hospital, Austin, TX, United States
| | - Adam J. Schwarz
- Department of Pediatrics, Children’s Hospital of Orange County, Orange, CA, United States
| | - Neethi P. Pinto
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Neal J. Thomas
- Department of Pediatrics, Penn State Health Children’s Hospital, Penn State University College of Medicine, Hershey, PA, United States
| | - Rainer G. Gedeit
- Pediatric Critical Care, Milwaukee Hospital-Children’s Wisconsin, Milwaukee, WI, United States
| | - Ronald C. Sanders
- Section of Pediatric Critical Care, Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children’s Research Institute, Little Rock, AR, United States
| | - Sidharth Mahapatra
- Pediatric Critical Care Medicine, Children’s Hospital & Medical Center Omaha, University of Nebraska Medical Center, Omaha, NE, United States
| | - Bria M. Coates
- Division of Critical Care Medicine, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, United States
| | - Natalie Z. Cvijanovich
- Division of Critical Care Medicine, UCSF Benioff Children’s Hospital, Oakland, CA, United States
| | - Kate G. Ackerman
- Department of Pediatrics, University of Rochester/UR Medicine Golisano Children’s Hospital, Rochester, NY, United States
| | - David W. Tellez
- Pediatric Critical Care Medicine, Phoenix Children’s Hospital, Phoenix, AZ, United States
| | - Patrick McQuillen
- Department of Pediatrics, Benioff Children’s Hospital, University of California, San Francisco, San Francisco, CA, United States
| | - Stephen C. Kurachek
- Department of Critical Care, Children’s Specialty Center, Children’s Minnesota, Minneapolis, MN, United States
| | - Steven L. Shein
- Division of Pediatric Critical Care Medicine, University Hospitals Rainbow Babies and Children’s Hospital, Cleveland, OH, United States
| | - Christoph Lange
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Paul G. Thomas
- National Institute of Allergy and Infectious Diseases (NIAID), Centers of Excellence for Influenza Research and Response (CEIRR), St. Jude Children's Research Hospital, Memphis, TN, United States
- Department of Immunology, St Jude Children’s Research Hospital, Memphis, TN, United States
| | - Adrienne G. Randolph
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, United States
- Department of Anaesthesia, Harvard Medical School, Boston, MA, United States
- National Institute of Allergy and Infectious Diseases (NIAID), Centers of Excellence for Influenza Research and Response (CEIRR), Center for Influenza Disease and Emergence Response (CIDER), Athens, GA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
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Chen Y, Wang X, Wang J, Zong J, Wan X. Revealing novel pyroptosis-related therapeutic targets for sepsis based on machine learning. BMC Med Genomics 2023; 16:23. [PMID: 36765335 PMCID: PMC9912626 DOI: 10.1186/s12920-023-01453-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Sepsis is one of the most lethal diseases worldwide. Pyroptosis is a unique form of cell death, and the mechanism of interaction with sepsis is not yet clear. The aim of this study was to uncover pyroptosis genes associated with sepsis and to provide early therapeutic targets for the treatment of sepsis. METHODS Based on the GSE134347 dataset, sepsis-related genes were mined by differential expression analysis and weighted gene coexpression network analysis (WGCNA). Subsequently, the sepsis-related genes were analysed for enrichment, and a protein‒protein interaction (PPI) network was constructed. We performed unsupervised consensus clustering of sepsis patients based on 33 pyroptosis-related genes (PRGs) provided by prior reviews. We finally obtained the PRGs mostly associated with sepsis by machine learning prediction models combined with prior reviews. The GSE32707 dataset served as an external validation dataset to validate the model and PRGs via receiver operating characteristic (ROC) curves. The NetworkAnalyst online tool was utilized to create a ceRNA network of lncRNAs and miRNAs around PRGs mostly associated with sepsis. RESULTS A total of 170 genes associated with sepsis and 13 hub genes were acquired by WGCNA and PPI network analysis. The results of the enrichment analysis implied that these genes were mainly involved in the regulation of the inflammatory response and the positive regulation of bacterial and fungal defence responses. The prolactin signalling pathway and IL-17 signalling pathway were the primary enrichment pathways. Thirty-three PRGs can effectively classify septic patients into two subtypes, implying that there is a reciprocal relationship between sepsis and pyroptosis. Eventually, NLRC4 was considered the PRG most strongly associated with sepsis. The validation results of the prediction model and NLRC4 based on ROC curves were 0.74 and 0.67, respectively, both of which showed better predictive values. Meanwhile, the ceRNA network consisting of 6 lncRNAs and 2 miRNAs was constructed around NLRC4. CONCLUSION NLRC4, as the PRG mostly associated with sepsis, could be considered a potential target for treatment. The 6 lncRNAs and 2 miRNAs centred on NLRC4 could serve as a further research direction to uncover the deeper pathogenesis of sepsis.
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Affiliation(s)
- Ying Chen
- grid.452435.10000 0004 1798 9070Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning China ,grid.452828.10000 0004 7649 7439Department of Respiratory Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning China
| | - Xingkai Wang
- grid.452435.10000 0004 1798 9070Department of Orthopedics, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning China
| | - Jiaxin Wang
- grid.411971.b0000 0000 9558 1426Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, Liaoning China
| | - Junwei Zong
- Department of Orthopedics, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. .,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China.
| | - Xianyao Wan
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
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Li M, Huang H, Ke C, Tan L, Wu J, Xu S, Tu X. Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm. Hereditas 2022; 159:14. [PMID: 35184762 PMCID: PMC8859894 DOI: 10.1186/s41065-021-00215-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022] Open
Abstract
Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy results in a non-specific diagnosis, and to date, a standard diagnostic test to detect sepsis in patients remains lacking. Therefore, it is vital to identify sepsis-related diagnostic genes. This study aimed to conduct an integrated analysis to assess the immune scores of samples from patients diagnosed with sepsis and normal samples, followed by weighted gene co-expression network analysis (WGCNA) to identify immune infiltration-related genes and potential transcriptome markers in sepsis. Furthermore, gene regulatory networks were established to screen diagnostic markers for sepsis based on the protein-protein interaction networks involving these immune infiltration-related genes. Moreover, we integrated WGCNA with the support vector machine (SVM) algorithm to build a diagnostic model for sepsis. Results showed that the immune score was significantly lower in the samples from patients with sepsis than in normal samples. A total of 328 and 333 genes were positively and negatively correlated with the immune score, respectively. Using the MCODE plugin in Cytoscape, we identified four modules, and through functional annotation, we found that these modules were related to the immune response. Gene Ontology functional enrichment analysis showed that the identified genes were associated with functions such as neutrophil degranulation, neutrophil activation in the immune response, neutrophil activation, and neutrophil-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed the enrichment of pathways such as primary immunodeficiency, Th1- and Th2-cell differentiation, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Finally, we identified a four-gene signature, containing the hub genes LCK, CCL5, ITGAM, and MMP9, and established a model that could be used to diagnose patients with sepsis.
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Dai W, Zheng P, Luo D, Xie Q, Liu F, Shao Q, Zhao N, Qian K. LPIN1 Is a Regulatory Factor Associated With Immune Response and Inflammation in Sepsis. Front Immunol 2022; 13:820164. [PMID: 35222395 PMCID: PMC8865371 DOI: 10.3389/fimmu.2022.820164] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/17/2022] [Indexed: 12/14/2022] Open
Abstract
Objectives Sepsis is a clinical disease that is typically treated in the intensive care unit, and the complex pathophysiology under this disease has not been thoroughly understood. While ferroptosis is involved in inflammation and infection, its effect in sepsis is still unknown. The study aimed to identify ferroptosis-related genes in sepsis, providing translational potential therapeutic targets. Methods The dataset GSE65682 was used to download the sample source from the Gene Expression Omnibus (GEO) database. Consensus weighted gene co-expression network analysis (WGCNA) was performed to find suspected modules of sepsis. The differentially expressed genes (DEGs) most significantly associated with mortality were intersected with those altered by lipopolysaccharide (LPS) treatment and were further analyzed for the identification of main pathways of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The related pathway markers were further verified by qPCR. Results A total of 802 blood samples with sepsis were included for WGCNA, which identified 21 modules. Intersected with ferroptosis databases and LPS treatment groups, we identified two ferroptosis-related genes: PEBP1 and LPIN1. Only LPIN1 contributes to a poor outcome. Then, 205 DEGs were further identified according to the high or low LPIN1 expression. Among them, we constructed a gene regulatory network with several transcriptional factors using the NetworkAnalyst online tool and identified that these genes mostly correlate with inflammation and immune response. The immune infiltration analysis showed that lower expression of LPIN1 was related to macrophage infiltration and could be an independent predictor factor of the survival status in sepsis patients. Meanwhile, the multivariate Cox analysis showed that LPIN1 had a significant correlation with survival that was further verified by in vitro and in vivo experiments. Conclusion In conclusion, LPIN1 could become a reliable biomarker for patient survival in sepsis, which is associated with immune and inflammation status.
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Affiliation(s)
- Wei Dai
- Department of Intensive Care Unit, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Department of Intensive Care Unit, The Fifth Dongxin’s Hospital of Shangrao City, Shangrao, China
| | - Ping Zheng
- Department of Key Laboratory, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Deqiang Luo
- Department of Intensive Care Unit, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qian Xie
- Department of Intensive Care Unit, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Fen Liu
- Department of Intensive Care Unit, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiang Shao
- Department of Intensive Care Unit, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ning Zhao
- Department of Intensive Care Unit, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kejian Qian
- Department of Intensive Care Unit, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
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Wu K, Wang L. Stomatin-knockdown effectively attenuates sepsis-induced oxidative stress and inflammation of alveolar epithelial cells by regulating CD36. Exp Ther Med 2021; 23:69. [PMID: 34934440 PMCID: PMC8649852 DOI: 10.3892/etm.2021.10992] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 09/23/2021] [Indexed: 12/13/2022] Open
Abstract
Sepsis-induced acute lung injury is a type of lung disease with a high fatality rate that is characterized by acute inflammation. In the present study, the underlying role and potential mechanism of the stomatin (STOM) protein were investigated in lipopolysaccharide (LPS)-induced oxidative stress and inflammation in a mouse lung epithelial cell line, MLE-12. The expression levels of STOM and CD36 were measured using reverse transcription-quantitative PCR and western blotting. Subsequently, the expression levels of STOM and CD36 in LPS-treated MLE-12 cells were knocked down or overexpressed, respectively, via transfection with a small interfering RNA-STOM or a CD36-overexpression vector. An RNA immunoprecipitation (RIP) assay was used to determine the interaction between STOM and CD36, while Cell Counting Kit-8 assay and ELISA were performed to detect cell viability and oxidative stress, respectively. Moreover, western blotting and ELISA kits were used to detect the expression levels of associated inflammatory factors. The results of the present study demonstrated that STOM expression was upregulated in MLE-12 cells treated with LPS compared with the untreated control group. According to the Search Tool for the Retrieval of Interacting Genes/Proteins database, it was predicted that STOM and CD36 had the ability to interact with each other. The predicted binding between STOM and CD36 was verified using a RIP assay. The results demonstrated that STOM positively regulated the expression of CD36. Moreover, in LPS-treated MLE-12 cells, STOM-knockdown reversed the inhibitory effects of LPS on cell viability, and the promoting effects of LPS on oxidative stress and inflammation. These aforementioned changes were alleviated by the overexpression of CD36. To conclude, the results of the present study revealed that STOM may interact with CD36 to affect the levels of oxidative stress and inflammation in LPS-treated MLE-12 cells.
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Affiliation(s)
- Kangkang Wu
- Department of Infectious Disease, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu 210008, P.R. China
| | - Li Wang
- Department of Respiratory and Critical Care Medicine, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, P.R. China
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Park JM, Han YM, Oh JY, Lee DY, Choi SH, Hahm KB. Transcriptome profiling implicated in beneficiary actions of kimchi extracts against Helicobacter pylori infection. J Clin Biochem Nutr 2021; 69:171-187. [PMID: 34616109 PMCID: PMC8482382 DOI: 10.3164/jcbn.20-116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 11/15/2020] [Indexed: 12/12/2022] Open
Abstract
Dietary intervention to prevent Helicobacter pylori (H. pylori)-gastric cancer might be ideal because of no risk of bacterial resistance, safety, and rejuvenating action of atrophic gastritis. We have published data about the potential of fermented kimchi as nutritional approach for H. pylori. Hence recent advances in RNAseq analysis lead us to investigate the transcriptome analysis to explain these beneficiary actions of kimchi. gastric cells were infected with either H. pylori or H. pylori plus kimchi. 943 genes were identified as significantly increased or decreased genes according to H. pylori infection and 68 genes as significantly changed between H. pylori infection and H. pylori plus kimchi (p<0.05). Gene classification and Medline database showed DLL4, FGF18, PTPRN, SLC7A11, CHAC1, FGF21, ASAN, CTH, and CREBRF were identified as significantly increased after H. pylori, but significantly decreased with kimchi and NEO1, CLDN8, KLRG1, and IGFBP1 were identified as significantly decreased after H. pylori, but increased with kimchi. After KEGG and STRING-GO analysis, oxidative stress, ER stress, cell adhesion, and apoptosis genes were up-regulated with H. pylori infection but down-regulated with kimchi, whereas tissue regeneration, cellular anti-oxidative response, and anti-inflammation genes were reversely regulated with kimchi (p<0.01). Conclusively, transcriptomes of H. pylori plus kimchi showed significant biological actions.
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Affiliation(s)
- Jong Min Park
- Daejeon University School of Oriental Medicine, Daejeon, 34520, Korea
| | - Young Min Han
- Seoul Center, Korea Basic Science Institute, Seoul, 02456, Korea
| | - Ji Young Oh
- CJ Food Research Center, Suwon, 16471, Korea
| | | | | | - Ki Baik Hahm
- CHA Cancer Preventive Research Center, CHA Bio Complex, Pangyo, 13497, Korea
- Medpacto Research Institute, Medpacto, Seoul, 06668, Korea
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Zhang X, Cui Y, Ding X, Liu S, Han B, Duan X, Zhang H, Sun T. Analysis of mRNA‑lncRNA and mRNA‑lncRNA-pathway co‑expression networks based on WGCNA in developing pediatric sepsis. Bioengineered 2021; 12:1457-1470. [PMID: 33949285 PMCID: PMC8806204 DOI: 10.1080/21655979.2021.1908029] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs (MAPK14, ITGAM, HK3, ALOX5, CR1, HCK, NCF4, PYGL, FLOT1, CARD6, NLRC4, SH3GLB1, PGS1, RAB31, LTB4R) and 4 lncRNAs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis.Abbreviations: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.
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Affiliation(s)
- Xiaojuan Zhang
- General ICU, Zhengzhou Key Laboratory of Sepsis, Henan Engineering Research Center for Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, Zhengzhou, China
| | - Yuqing Cui
- General ICU, Zhengzhou Key Laboratory of Sepsis, Henan Engineering Research Center for Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, Zhengzhou, China
| | - Xianfei Ding
- General ICU, Zhengzhou Key Laboratory of Sepsis, Henan Engineering Research Center for Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, Zhengzhou, China
| | - Shaohua Liu
- General ICU, Zhengzhou Key Laboratory of Sepsis, Henan Engineering Research Center for Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, Zhengzhou, China
| | - Bing Han
- General ICU, Zhengzhou Key Laboratory of Sepsis, Henan Engineering Research Center for Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, Zhengzhou, China
| | - Xiaoguang Duan
- General ICU, Zhengzhou Key Laboratory of Sepsis, Henan Engineering Research Center for Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, Zhengzhou, China
| | - Haibo Zhang
- Interdepartmental Division of Critical Care Medicine, Departments of Anesthesia and Physiology, University of Toronto, Toronto, Canada
| | - Tongwen Sun
- General ICU, Zhengzhou Key Laboratory of Sepsis, Henan Engineering Research Center for Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, Zhengzhou, China
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Zhang Z, Navarese EP, Zheng B, Meng Q, Liu N, Ge H, Pan Q, Yu Y, Ma X. Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome. J Evid Based Med 2020; 13:301-312. [PMID: 33185950 DOI: 10.1111/jebm.12418] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 10/21/2020] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.
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Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Eliano Pio Navarese
- Interventional Cardiology and Cardiovascular Medicine Research, Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland
- Faculty of Medicine, University of Alberta, Edmonton, Canada
| | - Bin Zheng
- Department of Surgery, 2D, Walter C Mackenzie Health Sciences Centre, University of Alberta, Edmonton, Alberta, Canada
| | - Qinghe Meng
- Department of Surgery, State University of New York Upstate Medical University, Syracuse, New York
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Huiqing Ge
- Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qing Pan
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuetian Yu
- Department of Critical Care Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuelei Ma
- Department of biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Ying J, Wang Q, Xu T, Lu Z. Diagnostic potential of a gradient boosting-based model for detecting pediatric sepsis. Genomics 2020; 113:874-883. [PMID: 33096256 DOI: 10.1016/j.ygeno.2020.10.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/30/2020] [Accepted: 10/16/2020] [Indexed: 12/18/2022]
Abstract
Pediatric sepsis is a major cause of mortality of children worldwide. However, there is still a lack of easy-to-use predictive tools that can accurately diagnose sepsis in children. This study aimed to develop an optimal gene model for the diagnosis of pediatric sepsis using statistics and machine learning approaches. Combining gene expression profiles from a training cohort of 364 pediatric samples with a Least Absolute Shrinkage and Selection Operator analysis produced eighteen genes as diagnostic markers. With the implementation of a Gradient Boosting algorithm, a model designated PEDSEPS-GBM, that aggregated these markers was developed with optimal performance for the diagnosis of pediatric samples in the validation and two independent cohorts. Moreover, a web calculator with a user-friendly interface was established for PEDSEPS-GBM. This study presents a diagnostic model that holds great potential for the detection of pediatric sepsis, and demonstrates the biologic and clinical relevance of this model.
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Affiliation(s)
- Jianchao Ying
- Central Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Institute of Emergency Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Qian Wang
- Department of Clinical Laboratory, Wenzhou People's Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, China
| | - Teng Xu
- Institute of Translational Medicine, Baotou Central Hospital, Baotou, China
| | - Zhongqiu Lu
- Institute of Emergency Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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Zhang Z, Chen L, Xu P, Xing L, Hong Y, Chen P. Gene correlation network analysis to identify regulatory factors in sepsis. J Transl Med 2020; 18:381. [PMID: 33032623 PMCID: PMC7545567 DOI: 10.1186/s12967-020-02561-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 10/03/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. METHODS The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. RESULTS A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). CONCLUSIONS The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.
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Affiliation(s)
- Zhongheng Zhang
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
| | - Lin Chen
- grid.13402.340000 0004 1759 700XDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Ping Xu
- Emergency Department, Zigong Fourth People’s Hospital, 19 Tanmulin Road, Zigong, Sichuan China
| | - Lifeng Xing
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
| | - Yucai Hong
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
| | - Pengpeng Chen
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
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12
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Long G, Yang C. A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock. Mol Med Rep 2020; 21:1561-1571. [PMID: 32016447 PMCID: PMC7003034 DOI: 10.3892/mmr.2020.10959] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Accepted: 11/12/2019] [Indexed: 11/06/2022] Open
Abstract
Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms of PSS using four microarray datasets (GSE26378, GSE26440, GSE13904 and GSE4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease‑associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.
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Affiliation(s)
- Guoli Long
- Department of The Intensive Care Unit, Eastern Hospital, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610101, P.R. China
| | - Chen Yang
- Department of The Intensive Care Unit, Eastern Hospital, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610101, P.R. China
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Abstract
PURPOSE OF REVIEW Pediatric sepsis is a heterogeneous state associated with significant morbidity and mortality, but treatment strategies are limited. Clinical trials of immunomodulators in sepsis have shown no benefit, despite having a strong biological rationale. There is considerable interest in application of a precision medicine approach to pediatric sepsis to identify patients who are more likely to benefit from targeted therapeutic interventions. RECENT FINDINGS Precision medicine requires a clear understanding of the molecular basis of disease. 'Omics data' and bioinformatics tools have enabled identification of endotypes of pediatric septic shock, with corresponding biological pathways. Further, using a multibiomarker-based approach, patients at highest risk of poor outcomes can be identified at disease onset. Enrichment strategies, both predictive and prognostic, may be used to optimize patient selection in clinical trials and identify a subpopulation in whom therapy of interest may be trialed. A bedside-to-bench-to-bedside model may offer clinicians pragmatic tools to aid in decision-making. SUMMARY Precision medicine approaches may be used to subclassify, risk-stratify, and select pediatric patients with sepsis who may benefit from new therapies. Application of precision medicine will require robust basic and translational research, rigorous clinical trials, and infrastructure to collect and analyze big data.
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Affiliation(s)
- Mihir R Atreya
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center
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Identification of Potential Transcriptional Biomarkers Differently Expressed in Both S. aureus- and E. coli-Induced Sepsis via Integrated Analysis. BIOMED RESEARCH INTERNATIONAL 2019; 2019:2487921. [PMID: 31093495 PMCID: PMC6481126 DOI: 10.1155/2019/2487921] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 02/13/2019] [Accepted: 03/25/2019] [Indexed: 01/13/2023]
Abstract
Sepsis is a critical, complex medical condition, and the major causative pathogens of sepsis are both Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Genome-wide studies identify differentially expressed genes for sepsis. However, the results for the identification of DEGs are inconsistent or discrepant among different studies because of heterogeneity of specimen sources, various data processing methods, or different backgrounds of the samples. To identify potential transcriptional biomarkers that are differently expressed in S. aureus- and E. coli-induced sepsis, we have analyzed four microarray datasets from GEO database and integrated results with bioinformatics tools. 42 and 54 DEGs were identified in both S. aureus and E. coli samples from any three different arrays, respectively. Hierarchical clustering revealed dramatic differences between control and sepsis samples. GO functional annotations suggested that DEGs in the S. aureus group were mainly involved in the responses of both defense and immune regulation, but DEGs in the E. coli group were mainly related to the regulation of endopeptidase activity involved in the apoptotic signaling pathway. Although KEGG showed inflammatory bowel disease in the E. coli group, the KEGG pathway analysis showed that these DEGs were mainly involved in the tumor necrosis factor signaling pathway, fructose metabolism, and mannose metabolism in both S. aureus- and E. coli-induced sepsis. Eight common genes were identified between sepsis patients with either S. aureus or E. coli infection and controls in this study. All the candidate genes were further validated to be differentially expressed by an ex-vivo human blood model, and the relative expression of these genes was performed by qPCR. The qPCR results suggest that GK and PFKFB3 might contribute to the progression of S. aureus-induced sepsis, and CEACAM1, TNFAIP6, PSTPIP2, SOCS3, and IL18RAP might be closely linked with E. coli-induced sepsis. These results provide new viewpoints for the pathogenesis of both sepsis and pathogen identification.
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Zhou LT, Lv LL, Qiu S, Yin Q, Li ZL, Tang TT, Ni LH, Feng Y, Wang B, Ma KL, Liu BC. Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease. J Transl Med 2019; 17:59. [PMID: 30819181 PMCID: PMC6394064 DOI: 10.1186/s12967-019-1818-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 02/21/2019] [Indexed: 01/15/2023] Open
Abstract
Background Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the “urinary kidney-specific mRNAs” and verify their potential clinical utility in DKD. Methods To select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n = 82) was verified, and the one with best performance moved on to stage 2 study (n = 80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis. Results Our bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases. Conclusions Our study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation. Electronic supplementary material The online version of this article (10.1186/s12967-019-1818-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Le-Ting Zhou
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China.,Wuxi People's Hospital Affiliated To Nanjing Medical University, Wuxi, Jiangsu, China
| | - Lin-Li Lv
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Shen Qiu
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Qing Yin
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Zuo-Lin Li
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Tao-Tao Tang
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Li-Hua Ni
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Ye Feng
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Bin Wang
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Kun-Ling Ma
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China
| | - Bi-Cheng Liu
- Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, No. 87 Dingjiaqiao Rd, Nanjing, Jiangsu, China.
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