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Rashid A, Brusletto BS, Al-Obeidat F, Toufiq M, Benakatti G, Brierley J, Malik ZA, Hussain Z, Alkhazaimi H, Sharief J, Kadwa R, Sarpal A, Chaussabel D, Malik RA, Quraishi N, Khilnani P, Zaki SA, Nadeem R, Shaikh G, Al-Dubai A, Hafez W, Hussain A. A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION? Shock 2023; 60:503-516. [PMID: 37553892 PMCID: PMC10581425 DOI: 10.1097/shk.0000000000002192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/12/2023] [Accepted: 07/19/2023] [Indexed: 08/10/2023]
Abstract
ABSTRACT This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)-VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.
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Affiliation(s)
- Asrar Rashid
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
- NMC Royal Hospital, Abu Dhabi, United Arab Emirates
| | - Berit S. Brusletto
- The Blood Cell Research Group, Department of Medical Biochemistry, Oslo University Hospital, Ullevål, Norway
| | - Feras Al-Obeidat
- College of Technological Innovation at Zayed University, Abu Dhabi, United Arab Emirates
| | - Mohammed Toufiq
- The Jackson Laboratory for Genomic Medicine Farmington, Connecticut, USA
| | - Govind Benakatti
- Medanta Gururam, Delhi, India
- Yas Clinic, Abu Dhabi, United Arab Emirates
| | - Joe Brierley
- Great Ormond Street Children's Hospital, London, United Kingdom
| | - Zainab A. Malik
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Zain Hussain
- Edinburgh Medical School, University go Edinburgh, Edinburgh, United Kingdom
| | | | | | - Raziya Kadwa
- NMC Royal Hospital, Abu Dhabi, United Arab Emirates
| | - Amrita Sarpal
- Sidra Medicine, Doha, Qatar
- Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Damien Chaussabel
- The Jackson Laboratory for Genomic Medicine Farmington, Connecticut, USA
| | - Rayaz A. Malik
- Weill Cornell Medicine-Qatar, Doha, Qatar
- Institute of Cardiovascular Science, University of Manchester, Manchester, United Kingdom
| | - Nasir Quraishi
- Centre for Spinal Studies & Surgery, Queen's Medical Centre, The University of Nottingham, Nottingham, United Kingdom
| | | | - Syed A. Zaki
- All India Institute of Medical Sciences, Hyderabad, India
| | | | - Guftar Shaikh
- Endocrinology, Royal Hospital for Children, Glasgow, United Kingdom
| | - Ahmed Al-Dubai
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Wael Hafez
- NMC Royal Hospital, Abu Dhabi, United Arab Emirates
- Medical Research Division, Department of Internal Medicine, The National Research Centre, Cairo, Egypt
| | - Amir Hussain
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
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Sweeney TE, Perumal TM, Henao R, Nichols M, Howrylak JA, Choi AM, Bermejo-Martin JF, Almansa R, Tamayo E, Davenport EE, Burnham KL, Hinds CJ, Knight JC, Woods CW, Kingsmore SF, Ginsburg GS, Wong HR, Parnell GP, Tang B, Moldawer LL, Moore FE, Omberg L, Khatri P, Tsalik EL, Mangravite LM, Langley RJ. A community approach to mortality prediction in sepsis via gene expression analysis. Nat Commun 2018; 9:694. [PMID: 29449546 PMCID: PMC5814463 DOI: 10.1038/s41467-018-03078-2] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 01/18/2018] [Indexed: 12/27/2022] Open
Abstract
Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.
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Affiliation(s)
- Timothy E Sweeney
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Inflammatix Inc., Burlingame, CA, 94010, USA
| | | | - Ricardo Henao
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27708, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA
| | - Marshall Nichols
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27708, USA
| | - Judith A Howrylak
- Division of Pulmonary and Critical Care Medicine, Penn State Milton S. Hershey Medical Center, Hershey, PA, 17033, USA
| | - Augustine M Choi
- Department of Medicine, Cornell Medical Center, New York, NY, 10065, USA
| | | | - Raquel Almansa
- Hospital Clínico Universitario de Valladolid/IECSCYL, Valladolid, 47005, Spain
| | - Eduardo Tamayo
- Hospital Clínico Universitario de Valladolid/IECSCYL, Valladolid, 47005, Spain
| | - Emma E Davenport
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Katie L Burnham
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Charles J Hinds
- William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University, London, EC1M 6BQ, UK
| | - Julian C Knight
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Christopher W Woods
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27708, USA
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, NC, 27710, USA
- Durham Veteran's Affairs Health Care System, Durham, NC, 27705, USA
| | | | - Geoffrey S Ginsburg
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27708, USA
| | - Hector R Wong
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, OH, 45223, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
| | - Grant P Parnell
- Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, NSW, 2145, Australia
| | - Benjamin Tang
- Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, NSW, 2145, Australia
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, Australia, Penrith, NSW, 2751, Australia
- Nepean Genomic Research Group, Nepean Clinical School, University of Sydney, Penrith, NSW, 2751, Australia
- Marie Bashir Institute for Infectious Diseases and Biosecurity, Westmead, NSW, 2145, Australia
| | - Lyle L Moldawer
- Department of Surgery, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Frederick E Moore
- Department of Surgery, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | | | - Purvesh Khatri
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Ephraim L Tsalik
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27708, USA
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, NC, 27710, USA
- Durham Veteran's Affairs Health Care System, Durham, NC, 27705, USA
| | | | - Raymond J Langley
- Department of Pharmacology, University of South Alabama, Mobile, AL, 36688, USA.
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Wang Y, Yin X, Yang F. Comprehensive Analysis of Gene Expression Profiles of Sepsis-Induced Multiorgan Failure Identified Its Valuable Biomarkers. DNA Cell Biol 2017; 37:90-98. [PMID: 29251990 DOI: 10.1089/dna.2017.3944] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Sepsis is an inflammatory-related disease, and severe sepsis would induce multiorgan dysfunction, which is the most common cause of death of patients in noncoronary intensive care units. Progression of novel therapeutic strategies has proven to be of little impact on the mortality of severe sepsis, and unfortunately, its mechanisms still remain poorly understood. In this study, we analyzed gene expression profiles of severe sepsis with failure of lung, kidney, and liver for the identification of potential biomarkers. We first downloaded the gene expression profiles from the Gene Expression Omnibus and performed preprocessing of raw microarray data sets and identification of differential expression genes (DEGs) through the R programming software; then, significantly enriched functions of DEGs in lung, kidney, and liver failure sepsis samples were obtained from the Database for Annotation, Visualization, and Integrated Discovery; finally, protein-protein interaction network was constructed for DEGs based on the STRING database, and network modules were also obtained through the MCODE cluster method. As a result, lung failure sepsis has the highest number of DEGs of 859, whereas the number of DEGs in kidney and liver failure sepsis samples is 178 and 175, respectively. In addition, 17 overlaps were obtained among the three lists of DEGs. Biological processes related to immune and inflammatory response were found to be significantly enriched in DEGs. Network and module analysis identified four gene clusters in which all or most of genes were upregulated. The expression changes of Icam1 and Socs3 were further validated through quantitative PCR analysis. This study should shed light on the development of sepsis and provide potential therapeutic targets for sepsis-induced multiorgan failure.
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Affiliation(s)
- Yumei Wang
- Department of Critical Care Medicine, Weihai Central Hospital , Weihai, China
| | - Xiaoling Yin
- Department of Critical Care Medicine, Weihai Central Hospital , Weihai, China
| | - Fang Yang
- Department of Critical Care Medicine, Weihai Central Hospital , Weihai, China
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Gopinathan U, Øvstebø R, Brusletto BS, Olstad OK, Kierulf P, Brandtzaeg P, Berg JP. Transcriptomic data from two primary cell models stimulating human monocytes suggest inhibition of oxidative phosphorylation and mitochondrial function by N. meningitidis which is partially up-regulated by IL-10. BMC Immunol 2017; 18:46. [PMID: 29078758 PMCID: PMC5659018 DOI: 10.1186/s12865-017-0229-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Accepted: 09/25/2017] [Indexed: 12/11/2022] Open
Abstract
Background Biological interpretation of DNA microarray data may differ depending on underlying assumptions and statistical tests of bioinformatics tools used. We used Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) to analyze previously generated DNA microarray data from human monocytes stimulated with N. meningitidis and IL-10 (“the model system”), and with meningococcal sepsis plasma before and after immunodepletion of IL-10 (“the patient plasma system”). The objectives were to compare if the two bioinformatics methods resulted in similar biological interpretation of the datasets, and to identify whether GSEA provided additional insight compared with IPA about the monocyte host response to meningococcal activation. Results In both experimental models, GSEA and IPA identified genes associated with pro-inflammatory innate immune activation, including TNF-signaling, Toll-like receptor signaling, JAK-STAT-signaling, and type I and type II interferon signaling. GSEA identified genes regulated by the presence of IL-10 with similar gene sets in both the model system and the patient plasma system. In the model system, GSEA and IPA in sum identified 170 genes associated with oxidative phosphorylation/mitochondrial function to be down-regulated in monocytes stimulated with meningococci. In the patient plasma system, GSEA and IPA in sum identified 122 genes associated with oxidative phosphorylation/mitochondrial dysfunction to be down-regulated by meningococcal sepsis plasma depleted for IL-10. Using IPA, we identified IL-10 to up-regulate 18 genes associated with oxidative phosphorylation/mitochondrial function that were down-regulated by N. meningitidis. Conclusions Biological processes associated with the gene expression changes in the model system of meningococcal sepsis were comparable with the results found in the patient plasma system. By combining GSEA with IPA, we discovered an inhibitory effect of N. meningitidis on genes associated with mitochondrial function and oxidative phosphorylation, and that IL-10 partially reverses this strong inhibitory effect, thereby identifying, to our knowledge, yet another group of genes where IL-10 regulates the effect of LPS. We suggest that relying on a single bioinformatics tool together with an arbitrarily chosen filtering criteria for data analysis may result in overlooking relevant biological processes and signaling pathways associated with genes differentially expressed between compared experimental conditions. Electronic supplementary material The online version of this article (10.1186/s12865-017-0229-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Unni Gopinathan
- Blood Cell Research Group, Section for Research, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway. .,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
| | - Reidun Øvstebø
- Blood Cell Research Group, Section for Research, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
| | - Berit Sletbakk Brusletto
- Blood Cell Research Group, Section for Research, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
| | - Ole Kristoffer Olstad
- Blood Cell Research Group, Section for Research, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
| | - Peter Kierulf
- Blood Cell Research Group, Section for Research, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Petter Brandtzaeg
- Blood Cell Research Group, Section for Research, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway.,Department of Pediatrics, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Jens Petter Berg
- Blood Cell Research Group, Section for Research, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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