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Dong J, Wang S, Hu Z, Gong L. Extracellular proteins as potential biomarkers in Sepsis-related cerebral injury. Front Immunol 2023; 14:1128476. [PMID: 37901226 PMCID: PMC10611492 DOI: 10.3389/fimmu.2023.1128476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 09/13/2023] [Indexed: 10/31/2023] Open
Abstract
Background Sepsis can cause brain damage known as septic encephalopathy (SAE), which is linked to higher mortality and poorer outcomes. Objective clinical markers for SAE diagnosis and prognosis are lacking. This study aimed to identify biomarkers of SAE by investigating genes and extracellular proteins involved in sepsis-induced brain injury. Methods Extracellular protein differentially expressed genes (EP-DEGs) from sepsis patients' brain tissue (GSE135838) were obtained from Gene Expression Omnibus (GEO) and evaluated by protein annotation database. The function and pathways of EP-DEGs were examined using GO and KEGG. Protein-protein interaction (PPI) networks were built and crucial EP-DEGs were screened using STRING, Cytoscape, MCODE, and Cytohubba. The diagnostic and prognostic accuracy of key EP-DEGs was assessed in 31 sepsis patients' blood samples and a rat cecal ligation and puncture (CLP)-induced sepsis model. Cognitive and spatial memory impairment was evaluated 7-11 days post-CLP using behavioral tests. Blood and cerebrospinal fluid from 26 rats (SHAM n=14, CLP n=12) were collected 6 days after CLP to analyze key EP-DEGs. Results Thirty-one EP-DEGs from DEGs were examined. Bone marrow leukocytes, neutrophil movement, leukocyte migration, and reactions to molecules with bacterial origin were all enhanced in EP-DEGs. In comparison to the sham-operated group, sepsis rats had higher levels of MMP8 and S100A8 proteins in their venous blood (both p<0.05) and cerebrospinal fluid (p=0.0506, p<0.0001, respectively). Four important extracellular proteins, MMP8, CSF3, IL-6, and S100A8, were identified in clinical peripheral blood samples. MMP8 and S100A8 levels in the peripheral blood of sepsis patients were higher in SAE than in non-SAE. In comparison to MMP8, S100A8 had a higher area under the curve (AUC: 0.962, p<0.05) and a higher sensitivity and specificity (80% and 100%, respectively) than MMP8 (AUC: 0.790, p<0.05). High levels of S100A8 strongly correlated with 28-day mortality and the Glasgow Coma Scale (GCS) scores. Conclusion The extracellular proteins MMP8, CSF3, IL-6, and S100A8 may be crucial in the pathophysiology of SAE. S100A8 and MMP8 are possible biomarkers for SAE's onset and progression. This research may help to clarify the pathogenesis of SAE and improve the diagnosis and prognosis of the disease.
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Affiliation(s)
| | | | - Zhonghua Hu
- Department of Anesthesiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Li Gong
- Department of Anesthesiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
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2
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Long Q, Li G, Dong Q, Wang M, Li J, Wang L. Landscape of co-expressed genes between the myocardium and blood in sepsis and ceRNA network construction: a bioinformatic approach. Sci Rep 2023; 13:6221. [PMID: 37069215 PMCID: PMC10110604 DOI: 10.1038/s41598-023-33602-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/15/2023] [Indexed: 04/19/2023] Open
Abstract
Septic cardiomyopathy is a serious complication of sepsis. The mechanism of disease pathogenesis, which is caused by infection, is well researched. Despite ongoing efforts, there are no viable biological markers in the peripheral blood for early detection and diagnosis of septic cardiomyopathy. We aimed to uncover potential biomarkers of septic cardiomyopathy by comparing the covaried genes and pathways in the blood and myocardium of sepsis patients. Gene expression profiling of GSE79962, GSE65682, GSE54514, and GSE134364 was retrieved from the GEO database. Student's t-test was used for differential expression analysis. K-means clustering analysis was applied for subgroup identification. Least absolute shrinkage and selection operator (LASSO) and logistic regression were utilized for screening characteristic genes and model construction. Receiver operating characteristic (ROC) curves were generated for estimating the diagnostic efficacy. For ceRNA information prediction, miWalk and lncBase were applied. Cytoscape was used for ceRNA network construction. Inflammation-associated genes were upregulated, while genes related to mitochondria and aerobic metabolism were downregulated in both blood and the myocardium. Three groups with a significantly different mortality were identified by these covaried genes, using clustering analysis. Five characteristic genes-BCL2A1, CD44, ADGRG1, TGIF1, and ING3-were identified, which enabled the prediction of mortality of sepsis. The pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis. Thus, the genes we identified have the potential to become biomarkers for septic cardiomyopathy.
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Affiliation(s)
- Qi Long
- Department of Critical Care Medicine, Hubei Province Hospital of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China.
- Hubei Province Academy of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China.
| | - Gang Li
- Department of Critical Care Medicine, Hubei Province Hospital of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
- Hubei Province Academy of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
| | - Qiufen Dong
- Department of Critical Care Medicine, Hubei Province Hospital of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
- Hubei Province Academy of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
| | - Min Wang
- Department of Critical Care Medicine, Hubei Province Hospital of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
- Hubei Province Academy of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
| | - Jin Li
- Department of Critical Care Medicine, Hubei Province Hospital of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
- Hubei Province Academy of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
| | - Liulin Wang
- Department of Critical Care Medicine, Hubei Province Hospital of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
- Hubei Province Academy of Traditional Chinese Medicine, 856 Luoyu Street, Wuhan, 430061, Hubei, People's Republic of China
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Chen IC, Chen HH, Jiang YH, Hsiao TH, Ko TM, Chao WC. Whole transcriptome analysis to explore the impaired immunological features in critically ill elderly patients with sepsis. J Transl Med 2023; 21:141. [PMID: 36823620 PMCID: PMC9951485 DOI: 10.1186/s12967-023-04002-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/16/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Sepsis is a frequent complication in critically ill patients, is highly heterogeneous and is associated with high morbidity and mortality rates, especially in the elderly population. Utilizing RNA sequencing (RNA-Seq) to analyze biological pathways is widely used in clinical and molecular genetic studies, but studies in elderly patients with sepsis are still lacking. Hence, we investigated the mortality-relevant biological features and transcriptomic features in elderly patients who were admitted to the intensive care unit (ICU) for sepsis. METHODS We enrolled 37 elderly patients with sepsis from the ICU at Taichung Veterans General Hospital. On day-1 and day-8, clinical and laboratory data, as well as blood samples, were collected for RNA-Seq analysis. We identified the dynamic transcriptome and enriched pathways of differentially expressed genes between day-8 and day-1 through DVID enrichment analysis and Gene Set Enrichment Analysis. Then, the diversity of the T cell repertoire was analyzed with MiXCR. RESULTS Overall, 37 patients had sepsis, and responders and non-responders were grouped through principal component analysis. Significantly higher SOFA scores at day-7, longer ventilator days, ICU lengths of stay and hospital mortality were found in the non-responder group, than in the responder group. On day-8 in elderly ICU patients with sepsis, genes related to innate immunity and inflammation, such as ZDHCC19, ALOX15, FCER1A, HDC, PRSS33, and PCSK9, were upregulated. The differentially expressed genes (DEGs) were enriched in the regulation of transcription, adaptive immune response, immunoglobulin production, negative regulation of transcription, and immune response. Moreover, there was a higher diversity of T-cell receptors on day-8 in the responder group, than on day-1, indicating that they had better regulated recovery from sepsis compared with the non-response patients. CONCLUSION Sepsis mortality and incidence were both high in elderly individuals. We identified mortality-relevant biological features and transcriptomic features with functional pathway and MiXCR analyses based on RNA-Seq data; and found that the responder group had upregulated innate immunity and increased T cell diversity; compared with the non-responder group. RNA-Seq may be able to offer additional complementary information for the accurate and early prediction of treatment outcome.
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Affiliation(s)
- I-Chieh Chen
- grid.410764.00000 0004 0573 0731Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Hsin-Hua Chen
- grid.410764.00000 0004 0573 0731Division of General Internal Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan ,grid.260542.70000 0004 0532 3749Big Data Center, National Chung Hsing University, Taichung, Taiwan ,grid.265231.10000 0004 0532 1428Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan ,grid.260542.70000 0004 0532 3749Institute of Biomedical Science and Rong Hsing Research Centre for Translational Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Yu-Han Jiang
- grid.410764.00000 0004 0573 0731Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Tzu-Hung Hsiao
- grid.410764.00000 0004 0573 0731Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan ,grid.256105.50000 0004 1937 1063Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan ,grid.260542.70000 0004 0532 3749Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Tai-Ming Ko
- grid.260539.b0000 0001 2059 7017Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan ,grid.260539.b0000 0001 2059 7017Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan ,grid.28665.3f0000 0001 2287 1366Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Wen-Cheng Chao
- Big Data Center, National Chung Hsing University, Taichung, Taiwan. .,Department of Critical Care Medicine, Taichung Veterans General Hospital, No. 1650 Taiwan Boulevard, Section 4, Xitun District, Taichung City, 40705, Taiwan. .,Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan. .,Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.
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Assessment of Infection Progression per Host Gene Expression. Crit Care Med 2022; 50:1834-1837. [PMID: 36394402 DOI: 10.1097/ccm.0000000000005677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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5
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Zhou W, Zhang C, Zhuang Z, Zhang J, Zhong C. Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis. Front Immunol 2022; 13:1040286. [PMID: 36505503 PMCID: PMC9732458 DOI: 10.3389/fimmu.2022.1040286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2022] Open
Abstract
Background Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed. Methods and results A total of 1,936 patients (sepsis samples, n=1,692; normal samples, n=244) in 7 discovery datasets were included to conduct weighted gene co-expression network analysis (WGCNA) to filter out candidate genes related to sepsis. Then, two subtypes of sepsis were classified in the training sepsis set (n=1,692), the Adaptive and Inflammatory, using K-means clustering analysis on 90 sepsis-related features. We validated these subtypes using 617 samples in 5 independent datasets and the merged 5 sets. Cibersort method revealed the Adaptive subtype was related to high infiltration levels of T cells and natural killer (NK) cells and a better clinical outcome. Immune features were validated by single-cell RNA sequencing (scRNA-seq) analysis. The Inflammatory subtype was associated with high infiltration of macrophages and a disadvantageous prognosis. Based on functional analysis, upregulation of the Toll-like receptor signaling pathway was obtained in Inflammatory subtype and NK cell-mediated cytotoxicity and T cell receptor signaling pathway were upregulated in Adaptive group. To quantify the cluster findings, a scoring system, called, risk score, was established using four datasets (n=980) in the discovery cohorts based on least absolute shrinkage and selection operator (LASSO) and logistic regression and validated in external sets (n=760). Multivariate logistic regression analysis revealed the risk score was an independent predictor of outcomes of sepsis patients (OR [odds ratio], 2.752, 95% confidence interval [CI], 2.234-3.389, P<0.001), when adjusted by age and gender. In addition, the validation sets confirmed the performance (OR, 1.638, 95% CI, 1.309-2.048, P<0.001). Finally, nomograms demonstrated great discriminatory potential than that of risk score, age and gender (training set: AUC=0.682, 95% CI, 0.643-0.719; validation set: AUC=0.624, 95% CI, 0.576-0.664). Decision curve analysis (DCA) demonstrated that the nomograms were clinically useful and had better discriminative performance to recognize patients at high risk than the age, gender and risk score, respectively. Conclusions In-depth analysis of a comprehensive landscape of the transcriptome characteristics of sepsis might contribute to personalized treatments and prediction of clinical outcomes.
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Affiliation(s)
- Wei Zhou
- Department of Anesthesiology, Huzhou Central Hospital, The Affiliated Huzhou Hospital, Zhejiang University School of Medicine, Huzhou, Zhejiang, China
| | - Chunyu Zhang
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China,Department of Neurosurgery, Shanghai East Hospital, Nanjing Medical University, Nanjing, China
| | - Zhongwei Zhuang
- Department of Neurosurgery, Shanghai East Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Zhang
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China,Institute for Advanced Study, Tongji University, Shanghai, China,*Correspondence: Jing Zhang, ; Chunlong Zhong,
| | - Chunlong Zhong
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China,Department of Neurosurgery, Shanghai East Hospital, Nanjing Medical University, Nanjing, China,*Correspondence: Jing Zhang, ; Chunlong Zhong,
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6
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Yeudall S, Upchurch CM, Seegren PV, Pavelec CM, Greulich J, Lemke MC, Harris TE, Desai BN, Hoehn KL, Leitinger N. Macrophage acetyl-CoA carboxylase regulates acute inflammation through control of glucose and lipid metabolism. SCIENCE ADVANCES 2022; 8:eabq1984. [PMID: 36417534 PMCID: PMC9683712 DOI: 10.1126/sciadv.abq1984] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 10/27/2022] [Indexed: 05/27/2023]
Abstract
Acetyl-CoA carboxylase (ACC) regulates lipid synthesis; however, its role in inflammatory regulation in macrophages remains unclear. We generated mice that are deficient in both ACC isoforms in myeloid cells. ACC deficiency altered the lipidomic, transcriptomic, and bioenergetic profile of bone marrow-derived macrophages, resulting in a blunted response to proinflammatory stimulation. In response to lipopolysaccharide (LPS), ACC is required for the early metabolic switch to glycolysis and remodeling of the macrophage lipidome. ACC deficiency also resulted in impaired macrophage innate immune functions, including bacterial clearance. Myeloid-specific deletion or pharmacological inhibition of ACC in mice attenuated LPS-induced expression of proinflammatory cytokines interleukin-6 (IL-6) and IL-1β, while pharmacological inhibition of ACC increased susceptibility to bacterial peritonitis in wild-type mice. Together, we identify a critical role for ACC in metabolic regulation of the innate immune response in macrophages, and thus a clinically relevant, unexpected consequence of pharmacological ACC inhibition.
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Affiliation(s)
- Scott Yeudall
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Clint M. Upchurch
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Philip V. Seegren
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Caitlin M. Pavelec
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Jan Greulich
- Environmentally-Induced Cardiovascular Degeneration, Clinical Chemistry and Laboratory Diagnostics, Medical Faculty, University Hospital and Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Michael C. Lemke
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Thurl E. Harris
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Bimal N. Desai
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Kyle L. Hoehn
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Norbert Leitinger
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
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McLean AS, Shojaei M. Transcriptomics in the intensive care unit. THE LANCET. RESPIRATORY MEDICINE 2022; 10:824-826. [PMID: 35878620 DOI: 10.1016/s2213-2600(22)00257-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Anthony S McLean
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW 2747, Australia; Centre for Immunology and Allergy Research, Watermead Institute for Medical Research, Sydney, NSW, Australia.
| | - Maryam Shojaei
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW 2747, Australia; Centre for Immunology and Allergy Research, Watermead Institute for Medical Research, Sydney, NSW, Australia
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Exploration of the Shared Gene Signatures between Myocardium and Blood in Sepsis: Evidence from Bioinformatics Analysis. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3690893. [PMID: 35971449 PMCID: PMC9375705 DOI: 10.1155/2022/3690893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 07/11/2022] [Accepted: 07/21/2022] [Indexed: 11/23/2022]
Abstract
Background Septic cardiomyopathy is widespread during sepsis and has adverse effects on mortality. Diagnosis of septic cardiomyopathy now mainly depends on transthoracic echocardiogram. Although some laboratory tests such as troponin T and atrial brain natriuretic peptide play a role in the diagnosis, specific blood biochemistry biomarkers are still lacking. Objective and Methods. In our study, we sought to find potential biological markers from genes and pathways that are covariant in the blood and myocardium of septic patients. Bioinformatics and machine learning methods were applied to achieve our goal. Datasets of myocardium and peripheral blood of patients with sepsis were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected and received functional enrichment analysis. Unsupervised hierarchical clustering analysis was performed to identify the subtypes of sepsis. Random forest, lasso regression, and logistic regression were used for variable screening and model construction. Internal and external validation sets were applied to verify the efficiency of the model in classifying disease and predicting mortality. Results By defining significance for genes using Student's t-test, we obtained 1,049 genes commonly changed in both myocardium and blood of patients with sepsis. The upregulated genes (LogFC >0) were related to inflammation pathways, and downregulated (LogFC <0) genes were related to mitochondrial and aerobic metabolism. We divided 468 sepsis patients into two groups with different clinical result based on the mortality-related commonly changed genes (104 genes), using unsupervised hierarchical clustering analysis. In our validation datasets, a six-gene model (SMU1, CLIC3, SP100, ARHGAP25, DECR1, and TNS3) was obtained and proven to perform well in classifying groups and predicting mortality. Conclusion We have identified genes that have the potential to become biomarkers for septic cardiomyopathy. Additionally, the pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis.
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Zhang JX, Xu WH, Xing XH, Chen LL, Zhao QJ, Wang Y. ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network. Hereditas 2022; 159:27. [PMID: 35739592 PMCID: PMC9219214 DOI: 10.1186/s41065-022-00240-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/05/2022] [Indexed: 11/17/2022] Open
Abstract
Background Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication. Methods Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results. Results Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally. Conclusions We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis. Supplementary Information The online version contains supplementary material available at 10.1186/s41065-022-00240-1.
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Affiliation(s)
- Jing-Xiang Zhang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Wei-Heng Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Xin-Hao Xing
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Lin-Lin Chen
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Qing-Jie Zhao
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.
| | - Yan Wang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.
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10
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Cheng PL, Chen HH, Jiang YH, Hsiao TH, Wang CY, Wu CL, Ko TM, Chao WC. Using RNA-Seq to Investigate Immune-Metabolism Features in Immunocompromised Patients With Sepsis. Front Med (Lausanne) 2022; 8:747263. [PMID: 34977060 PMCID: PMC8718501 DOI: 10.3389/fmed.2021.747263] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/24/2021] [Indexed: 12/21/2022] Open
Abstract
Objective: Sepsis is life threatening and leads to complex inflammation in patients with immunocompromised conditions, such as cancer, and receiving immunosuppressants for autoimmune diseases and organ transplant recipients. Increasing evidence has shown that RNA-Sequencing (RNA-Seq) can be used to define subendotype in patients with sepsis; therefore, we aim to use RNA-Seq to identify transcriptomic features among immunocompromised patients with sepsis. Methods: We enrolled patients who were admitted to medical intensive care units (ICUs) for sepsis at a tertiary referral centre in central Taiwan. Whole blood on day-1 and day-8 was obtained for RNA-Seq. We used Gene Set Enrichment Analysis (GSEA) to identify the enriched pathway of day-8/day-1 differentially expressed genes and MiXCR to determine the diversity of T cell repertoire. Results: A total of 18 immunocompromised subjects with sepsis and 18 sequential organ failure assessment (SOFA) score-matched immunocompetent control subjects were enrolled. The ventilator-day, ICU-stay, and hospital-day were similar between the two groups, whereas the hospital mortality was higher in immunocompromised patients than those in immunocompetent patients (50.0 vs. 5.6%, p < 0.01). We found that the top day-8/day-1 upregulated genes in the immunocompetent group were mainly innate immunity and inflammation relevant genes, namely, PRSS33, HDC, ALOX15, FCER1A, and OLR1, whereas a blunted day-8/day-1 dynamic transcriptome was found among immunocompromised patients with septic. Functional pathway analyses of day-8/day-1 differentially expressed genes identified the upregulated functional biogenesis and T cell-associated pathways in immunocompetent patients recovered from sepsis, whereas merely downregulated metabolism-associated pathways were found in immunocompromised patients with septic. Moreover, we used MiXCR to identify a higher diversity of T cell receptor (TCR) in immunocompetent patients both on day-1 and on day-8 than those in immunocompromised patients. Conclusions: Using RNA-Seq, we found compromised T cell function, altered metabolic signalling, and decreased T cell diversity among immunocompromised patients with septic, and more mechanistic studies are warranted to elucidate the underlying mechanism.
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Affiliation(s)
- Po-Liang Cheng
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.,Precision Medicine Center, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Hsin-Hua Chen
- Division of General Internal Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,Big Data Center, Chung Hsing University, Taichung, Taiwan.,Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.,Rong Hsing Research Centre for Translational Medicine, Institute of Biomedical Science, Chung Hsing University, Taichung, Taiwan
| | - Yu-Han Jiang
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.,Precision Medicine Center, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.,Precision Medicine Center, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan.,Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Chen-Yu Wang
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Nursing, Hung Kuang University, Taichung, Taiwan
| | - Chieh-Liang Wu
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.,Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Computer Science, Tunghai University, Taichung, Taiwan.,Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
| | - Tai-Ming Ko
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.,Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Wen-Cheng Chao
- Big Data Center, Chung Hsing University, Taichung, Taiwan.,Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Computer Science, Tunghai University, Taichung, Taiwan.,Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
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11
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Application of an Exploratory Knowledge-Discovery Pipeline Based on Machine Learning to Multi-Scale OMICS Data to Characterise Myocardial Injury in a Cohort of Patients with Septic Shock: An Observational Study. J Clin Med 2021; 10:jcm10194354. [PMID: 34640372 PMCID: PMC8509561 DOI: 10.3390/jcm10194354] [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: 08/20/2021] [Revised: 09/10/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022] Open
Abstract
Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction.
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12
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Systemic viral spreading and defective host responses are associated with fatal Lassa fever in macaques. Commun Biol 2021; 4:27. [PMID: 33398113 PMCID: PMC7782745 DOI: 10.1038/s42003-020-01543-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 12/01/2020] [Indexed: 12/12/2022] Open
Abstract
Lassa virus (LASV) is endemic in West Africa and induces a viral hemorrhagic fever (VHF) with up to 30% lethality among clinical cases. The mechanisms involved in control of Lassa fever or, in contrast, the ensuing catastrophic illness and death are poorly understood. We used the cynomolgus monkey model to reproduce the human disease with asymptomatic to mild or fatal disease. After initial replication at the inoculation site, LASV reached the secondary lymphoid organs. LASV did not spread further in nonfatal disease and was rapidly controlled by balanced innate and T-cell responses. Systemic viral dissemination occurred during severe disease. Massive replication, a cytokine/chemokine storm, defective T-cell responses, and multiorgan failure were observed. Clinical, biological, immunological, and transcriptomic parameters resembled those observed during septic-shock syndrome, suggesting that similar pathogenesis is induced during Lassa fever. The outcome appears to be determined early, as differentially expressed genes in PBMCs were associated with fatal and non-fatal Lassa fever outcome very early after infection. These results provide a full characterization and important insights into Lassa fever pathogenesis and could help to develop early diagnostic tools.
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13
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Beckmann N, Salyer CE, Crisologo PA, Nomellini V, Caldwell CC. Staging and Personalized Intervention for Infection and Sepsis. Surg Infect (Larchmt) 2020; 21:732-744. [PMID: 32240042 DOI: 10.1089/sur.2019.363] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background: Sepsis is defined as a dysregulated host response to infection, resulting in life-threatening organ dysfunction. It is now understood that this dysregulation not only constitutes excessive inflammation, but also sustained immune suppression. Immune-modulatory therapies thus have great potential for novel sepsis therapies. Here, we provide a review of biomarkers and functional assays designed to immunologically stage patients with sepsis as well as therapies designed to alter the innate and adaptive immune systems of patients with sepsis beneficially. Methods: A search of PubMed/MEDLINE and clinicaltrials.gov was performed between October 1, 2019 and December 22, 2019 using search terms such as "sepsis immunotherapy," "sepsis biomarkers," "sepsis clinical trials," and variations thereof. Results: Despite more than 30 years of research, there is still no Food and Drug Administration (FDA)-cleared biomarker that has proven to be effective in either identifying patients with sepsis who are at an increased risk of adverse outcomes or responsive to specific interventions. Similarly, past clinical trials investigating new treatment strategies have rarely stratified patients with sepsis. Overall, the results of these trials have been disappointing. Novel efforts to properly gauge an individual patient's immune response and choose an appropriate immunomodulatory agent based on the results are underway. Conclusion: Our evolving understanding of the different mechanisms perturbing immune homeostasis during sepsis strongly suggests that future successes will depend on finding the right therapy for the right patient and administering it at the right time. For such a personalized medicine approach, novel biomarkers and functional assays to properly stage the patient with sepsis will be crucial. The growing repertoire of immunomodulatory agents at our disposal, as well as re-appraisal of agents that have already been tested in unstratified cohorts of patients with sepsis, may finally translate into successful treatment strategies for sepsis.
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Affiliation(s)
- Nadine Beckmann
- Division of Research, Critical Care, and Acute Care Surgery, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Christen E Salyer
- Division of Research, Critical Care, and Acute Care Surgery, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Peter A Crisologo
- Division of Podiatric Medicine and Surgery, Critical Care, and Acute Care Surgery, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Vanessa Nomellini
- Division of Trauma, Critical Care, and Acute Care Surgery, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.,Division of Research, Shriner's Hospital for Children Cincinnati, Cincinnati, Ohio, USA
| | - Charles C Caldwell
- Division of Research, Critical Care, and Acute Care Surgery, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.,Division of Research, Shriner's Hospital for Children Cincinnati, Cincinnati, Ohio, USA
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14
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Bruse N, Leijte GP, Pickkers P, Kox M. New frontiers in precision medicine for sepsis-induced immunoparalysis. Expert Rev Clin Immunol 2019; 15:251-263. [PMID: 30572728 DOI: 10.1080/1744666x.2019.1562336] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION In the last decade, the sepsis research field has shifted focus from targeting hyperinflammation to reversing sepsis-induced immunoparalysis. Sepsis-induced immunoparalysis is very heterogeneous: the magnitude and the nature of the underlying immune defects differ considerably between patients, but also within individuals over time. Therefore, a 'one-treatment-fits-all' strategy for sepsis-induced immunoparalysis is bound to fail, and an individualized 'precision medicine' approach is required. Such a strategy is nevertheless hampered by the unsuitability of the currently available markers to identify the many immune defects that can manifest in individual patients. Areas covered: We describe the currently available markers for sepsis-induced immunoparalysis and limitations pertaining to their use. Furthermore, future prospects and caveats are discussed, focusing on 'omics' approaches: genomics, transcriptomics, epigenomics, and metabolomics. Finally, we present a contemporary overview of adjuvant immunostimulatory therapies. Expert opinion: The integration of multiple omics techniques offers a systems biology approach which can yield biomarker profiles that accurately and comprehensively gauge the extent and nature of sepsis-induced immunoparalysis. We expect this development to be instrumental in facilitating precision medicine for sepsis-induced immunoparalysis, consisting of the application of targeted immunostimulatory therapies and follow-up measurements to monitor the response to treatment and to titrate or adjust medication.
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Affiliation(s)
- Niklas Bruse
- a Department of Intensive Care Medicine , Radboud University Medical Center , Nijmegen , The Netherlands.,b Radboud Center for Infectious Diseases , Radboud University Medical Center , Nijmegen , The Netherlands
| | - Guus P Leijte
- a Department of Intensive Care Medicine , Radboud University Medical Center , Nijmegen , The Netherlands.,b Radboud Center for Infectious Diseases , Radboud University Medical Center , Nijmegen , The Netherlands
| | - Peter Pickkers
- a Department of Intensive Care Medicine , Radboud University Medical Center , Nijmegen , The Netherlands.,b Radboud Center for Infectious Diseases , Radboud University Medical Center , Nijmegen , The Netherlands
| | - Matthijs Kox
- a Department of Intensive Care Medicine , Radboud University Medical Center , Nijmegen , The Netherlands.,b Radboud Center for Infectious Diseases , Radboud University Medical Center , Nijmegen , The Netherlands
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