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Kreitmann L, D'Souza G, Miglietta L, Vito O, Jackson HR, Habgood-Coote D, Levin M, Holmes A, Kaforou M, Rodriguez-Manzano J. A computational framework to improve cross-platform implementation of transcriptomics signatures. EBioMedicine 2024; 105:105204. [PMID: 38901146 PMCID: PMC11245942 DOI: 10.1016/j.ebiom.2024.105204] [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: 01/10/2024] [Revised: 05/29/2024] [Accepted: 06/02/2024] [Indexed: 06/22/2024] Open
Abstract
The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.
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Affiliation(s)
- Louis Kreitmann
- Section of Adult Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom; Centre for Antimicrobial Optimisation, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom
| | - Giselle D'Souza
- Section of Adult Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom; Centre for Antimicrobial Optimisation, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom; Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, W2 1NY, United Kingdom; Centre for Paediatrics and Child Health, Imperial College London, London, W2 1NY, United Kingdom
| | - Luca Miglietta
- Section of Adult Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom; Centre for Antimicrobial Optimisation, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom
| | - Ortensia Vito
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, W2 1NY, United Kingdom; Centre for Paediatrics and Child Health, Imperial College London, London, W2 1NY, United Kingdom
| | - Heather R Jackson
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, W2 1NY, United Kingdom; Centre for Paediatrics and Child Health, Imperial College London, London, W2 1NY, United Kingdom
| | - Dominic Habgood-Coote
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, W2 1NY, United Kingdom; Centre for Paediatrics and Child Health, Imperial College London, London, W2 1NY, United Kingdom
| | - Michael Levin
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, W2 1NY, United Kingdom; Centre for Paediatrics and Child Health, Imperial College London, London, W2 1NY, United Kingdom
| | - Alison Holmes
- Section of Adult Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom; Centre for Antimicrobial Optimisation, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom
| | - Myrsini Kaforou
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, W2 1NY, United Kingdom; Centre for Paediatrics and Child Health, Imperial College London, London, W2 1NY, United Kingdom
| | - Jesus Rodriguez-Manzano
- Section of Adult Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom; Centre for Antimicrobial Optimisation, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom.
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Wen S, Xu N, Zhao L, Yang L, Yang H, Chang C, Wang S, Qu C, Song L, Zou W, He Y, Wang G. Ruxolitinib plus standard of care in severe hospitalized adults with severe fever with thrombocytopenia syndrome (SFTS): an exploratory, single-arm trial. BMC Med 2024; 22:204. [PMID: 38764059 PMCID: PMC11103999 DOI: 10.1186/s12916-024-03421-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/09/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease, and its morbidity and mortality are increasing. At present, there is no specific therapy available. An exacerbated IFN-I response and cytokine storm are related to the mortality of patients with SFTS. Ruxolitinib is a Janus kinase (JAK) 1/2 inhibitor that can block proinflammatory cytokines and inhibit the type I IFN pathway. We aimed to explore the use of ruxolitinib plus standard of care for severe SFTS. METHODS We conducted a prospective, single-arm study of severe SFTS. We recruited participants aged 18 years or older who were admitted to the hospital with laboratory-confirmed severe SFTS and whose clinical score exceeded 8 points within 6 days of symptom onset. Participants received oral ruxolitinib (10 mg twice a day) for up to 10 days. The primary endpoint was 28-day overall survival. The secondary endpoints included the proportion of participants who needed intensive care unit (ICU) admission, total cost, changes in neurologic symptoms and clinical laboratory parameters, and adverse events (AEs) within 28 days. A historical control group (HC group, n = 26) who met the upper criteria for inclusion and hospitalized from April 1, 2021, to September 16, 2022, was selected and 1:1 matched for baseline characteristics by propensity score matching. RESULTS Between Sep 16, 2022, and Sep 16, 2023, 26 participants were recruited into the ruxolitinib treatment group (RUX group). The 28-day overall mortality was 7.7% in the RUX group and 46.2% in the HC group (P = 0.0017). There was a significantly lower proportion of ICU admissions (15.4% vs 65.4%, p < 0.001) and total hospitalization cost in the RUX group. Substantial improvements in neurologic symptoms, platelet counts, hyperferritinemia, and an absolute decrease in the serum SFTS viral load were observed in all surviving participants. Treatment-related adverse events were developed in 6 patients (23.2%) and worsened in 8 patients (30.8%), and no treatment-related serious adverse events were reported. CONCLUSIONS Our findings indicate that ruxolitinib has the potential to increase the likelihood of survival as well as reduce the proportion of ICU hospitalization and being tolerated in severe SFTS. Further trials are needed. TRAIL REGISTRATION ChiCTR2200063759, September 16, 2022.
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Affiliation(s)
- Sai Wen
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Nannan Xu
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Lianhui Zhao
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Lulu Yang
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Hui Yang
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Caiyun Chang
- Jinan Center for Disease Control and Prevention, Jinan, 250021, Shandong, China
| | - Shanshan Wang
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Chunmei Qu
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Li Song
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Wenlu Zou
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Yishan He
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China
| | - Gang Wang
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, P. R. China.
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Lei H. A two-gene marker for the two-tiered innate immune response in COVID-19 patients. PLoS One 2023; 18:e0280392. [PMID: 36649304 PMCID: PMC9844909 DOI: 10.1371/journal.pone.0280392] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.
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Affiliation(s)
- Hongxing Lei
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing, China
- Cunji Medical School, University of Chinese Academy of Sciences, Beijing, China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
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Kelly E, Whelan SO, Harriss E, Murphy S, Pollard AJ, O' Connor D. Systematic review of host genomic biomarkers of invasive bacterial disease: Distinguishing bacterial from non-bacterial causes of acute febrile illness. EBioMedicine 2022; 81:104110. [PMID: 35792524 PMCID: PMC9256842 DOI: 10.1016/j.ebiom.2022.104110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 12/03/2022] Open
Abstract
Background Infectious diseases play a significant role in the global burden of disease. The gold standard for the diagnosis of bacterial infection, bacterial culture, can lead to diagnostic delays and inappropriate antibiotic use. The advent of high- throughput technologies has led to the discovery of host-based genomic biomarkers of infection, capable of differentiating bacterial from other causes of infection, but few have achieved validation for use in a clinical setting. Methods A systematic review was performed. PubMed/Ovid Medline, Ovid Embase and Scopus databases were searched for relevant studies from inception up to 30/03/2022 with forward and backward citation searching of key references. Studies assessing the diagnostic performance of human host genomic biomarkers of bacterial infection were included. Study selection and assessment of quality were conducted by two independent reviewers. A meta-analysis was undertaken using a diagnostic random-effects model. The review was registered with PROSPERO (ID: CRD42021208462). Findings Seventy-two studies evaluating the performance of 116 biomarkers in 16,216 patients were included. Forty-six studies examined TB-specific biomarker performance and twenty-four studies assessed biomarker performance in a paediatric population. The results of pooled sensitivity, specificity, negative and positive likelihood ratio, and diagnostic odds ratio of genomic biomarkers of bacterial infection were 0.80 (95% CI 0.78 to 0.82), 0.86 (95% CI 0.84 to 0.88), 0.18 (95% CI 0.16 to 0.21), 5.5 (95% CI 4.9 to 6.3), 30.1 (95% CI 24 to 37), respectively. Significant between-study heterogeneity (I2 77%) was present. Interpretation Host derived genomic biomarkers show significant potential for clinical use as diagnostic tests of bacterial infection however, further validation and attention to test platform is warranted before clinical implementation can be achieved. Funding No funding received.
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Affiliation(s)
- Eimear Kelly
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford. UK; NIHR Oxford Biomedical Research Centre, Oxford, UK.
| | - Seán Olann Whelan
- Department of Clinical Microbiology, Galway University Hospital, Galway, Ireland
| | - Eli Harriss
- Bodleian Health Care Libraries, University of Oxford
| | - Sarah Murphy
- Department of Paediatrics, Cork University Maternity Hospital, Wilton, Cork, Ireland
| | - Andrew J Pollard
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford. UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Daniel O' Connor
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford. UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
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Bodkin N, Ross M, McClain MT, Ko ER, Woods CW, Ginsburg GS, Henao R, Tsalik EL. Systematic comparison of published host gene expression signatures for bacterial/viral discrimination. Genome Med 2022; 14:18. [PMID: 35184750 PMCID: PMC8858657 DOI: 10.1186/s13073-022-01025-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 02/09/2022] [Indexed: 12/13/2022] Open
Abstract
Background Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. Methods This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. Results Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. Conclusions In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01025-x.
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Channon-Wells S, O'Connor D. Host gene signature shows promise to distinguish bacterial and viral infections. LANCET DIGITAL HEALTH 2021; 3:e465-e466. [PMID: 34325851 PMCID: PMC8542463 DOI: 10.1016/s2589-7500(21)00136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 06/22/2021] [Indexed: 10/25/2022]
Affiliation(s)
| | - Daniel O'Connor
- National Institute for Health Research, Oxford Biomedical Research Centre and Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK.
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