1
|
Eltobgy M, Johns F, Farkas D, Leuenberger L, Cohen SP, Ho K, Karow S, Swoope G, Pannu S, Horowitz JC, Mallampalli RK, Englert JA, Bednash JS. Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways in severe COVID-19 ARDS. Front Immunol 2024; 15:1397629. [PMID: 39161760 PMCID: PMC11330807 DOI: 10.3389/fimmu.2024.1397629] [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: 03/07/2024] [Accepted: 07/17/2024] [Indexed: 08/21/2024] Open
Abstract
Introduction The acute respiratory distress syndrome (ARDS) is a common complication of severe COVID-19 and contributes to patient morbidity and mortality. ARDS is a heterogeneous syndrome caused by various insults, and results in acute hypoxemic respiratory failure. Patients with ARDS from COVID-19 may represent a subgroup of ARDS patients with distinct molecular profiles that drive disease outcomes. Here, we hypothesized that longitudinal transcriptomic analysis may identify distinct dynamic pathobiological pathways during COVID-19 ARDS. Methods We identified a patient cohort from an existing ICU biorepository and established three groups for comparison: 1) patients with COVID-19 ARDS that survived hospitalization (COVID survivors, n = 4), 2) patients with COVID-19 ARDS that did not survive hospitalization (COVID non-survivors, n = 5), and 3) patients with ARDS from other causes as a control group (ARDS controls, n = 4). RNA was isolated from peripheral blood mononuclear cells (PBMCs) at 4 time points (Days 1, 3, 7, and 10 following ICU admission) and analyzed by bulk RNA sequencing. Results We first compared transcriptomes between groups at individual timepoints and observed significant heterogeneity in differentially expressed genes (DEGs). Next, we utilized the likelihood ratio test to identify genes that exhibit different patterns of change over time between the 3 groups and identified 341 DEGs across time, including hemoglobin subunit alpha 2 (HBA1, HBA2), hemoglobin subunit beta (HBB), von Willebrand factor C and EGF domains (VWCE), and carbonic anhydrase 1 (CA1), which all demonstrated persistent upregulation in the COVID non-survivors compared to COVID survivors. Of the 341 DEGs, 314 demonstrated a similar pattern of persistent increased gene expression in COVID non-survivors compared to survivors, associated with canonical pathways of iron homeostasis signaling, erythrocyte interaction with oxygen and carbon dioxide, erythropoietin signaling, heme biosynthesis, metabolism of porphyrins, and iron uptake and transport. Discussion These findings describe significant differences in gene regulation during patient ICU course between survivors and non-survivors of COVID-19 ARDS. We identified multiple pathways that suggest heme and red blood cell metabolism contribute to disease outcomes. This approach is generalizable to larger cohorts and supports an approach of longitudinal sampling in ARDS molecular profiling studies, which may identify novel targetable pathways of injury and resolution.
Collapse
Affiliation(s)
- Moemen Eltobgy
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Finny Johns
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Daniela Farkas
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Laura Leuenberger
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Sarah P. Cohen
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Kevin Ho
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Sarah Karow
- Clinical Trials Management Office, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Gabrielle Swoope
- Clinical Trials Management Office, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Sonal Pannu
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Jeffrey C. Horowitz
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Rama K. Mallampalli
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Joshua A. Englert
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
| | - Joseph S. Bednash
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University, Columbus, OH, United States
- The Center for RNA Biology, College of Medicine, The Ohio State University, Columbus, OH, United States
| |
Collapse
|
2
|
Kailankangas V, Katayama S, Gröndahl-Yli-Hannuksela K, Vilhonen J, Tervaniemi MH, Rantakokko-Jalava K, Seiskari T, Lönnqvist E, Kere J, Oksi J, Syrjänen J, Vuopio J. Low expression of the CCL5 gene and low serum concentrations of CCL5 in severe invasive group a streptococcal disease. Infection 2024:10.1007/s15010-024-02318-6. [PMID: 38865072 DOI: 10.1007/s15010-024-02318-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 06/05/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Our objective was to elucidate host dependent factors of disease severity in invasive group A Streptococcal disease (iGAS) using transcriptome profiling of iGAS cases of varying degrees of severity at different timepoints. To our knowledge there are no previous transcriptome studies in iGAS patients. METHODS We recruited iGAS cases from June 2018 to July 2020. Whole blood samples for transcriptome analysis and serum for biomarker analysis were collected at three timepoints representing the acute (A), the convalescent (B) and the post-infection phase (C). Gene expression was compared against clinical traits and disease course. Serum chemokine ligand 5 (CCL5, an inflammatory cytokine) concentration was also measured. RESULTS Forty-five patients were enrolled. After disqualifying degraded or impure RNAs we had 34, 31 and 21 subjects at timepoints A, B, and C, respectively. Low expression of the CCL5 gene correlated strongly with severity (death or need for intensive care) at timepoint A (AUC = 0.92), supported by low concentrations of CCL5 in sera. CONCLUSIONS Low gene expression levels and low serum concentration of CCL5 in the early stages of an iGAS infection were associated with a more severe disease course. CCL5 might have potential as a predictor of disease severity. Low expression of genes of cytotoxic immunity, especially CCL5, and corresponding low serum concentrations of CCL5 associated with a severe disease course, i.e. death, or need for intensive care, in early phase of invasive group A Streptococcal disease.
Collapse
Affiliation(s)
- V Kailankangas
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland.
| | - S Katayama
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Solna, Sweden
| | | | - J Vilhonen
- Department of Infectious Diseases, Turku University Hospital, Turku, Finland
| | - M H Tervaniemi
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
| | - K Rantakokko-Jalava
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Clinical Microbiology, Laboratory Division, Turku University Hospital, Turku, Finland
| | - T Seiskari
- Department of Clinical Microbiology, Fimlab Laboratories, Tampere, Finland
| | - E Lönnqvist
- Department of Clinical Microbiology, Fimlab Laboratories, Tampere, Finland
| | - J Kere
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Solna, Sweden
| | - J Oksi
- Department of Infectious Diseases, Turku University Hospital, Turku, Finland
- Faculty of Medicine, University of Turku, Turku, Finland
| | - J Syrjänen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland
| | - J Vuopio
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Clinical Microbiology, Laboratory Division, Turku University Hospital, Turku, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| |
Collapse
|
3
|
Viasus D, Nonell L, Restrepo C, Figueroa F, Donado-Mazarrón C, Carratalà J. A Systematic Review of Gene Expression Studies in Critically Ill Patients with Sepsis and Community-Acquired Pneumonia. Biomedicines 2023; 11:2755. [PMID: 37893128 PMCID: PMC10604146 DOI: 10.3390/biomedicines11102755] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/14/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023] Open
Abstract
(1) Background: Sepsis is present in nearly 90% of critically ill patients with community-acquired pneumonia (CAP). This systematic review updates the information on studies that have assessed gene expression profiles in critically ill septic patients with CAP. (2) Methods: We searched for studies that satisfied the following criteria: (a) expression profile in critically ill patients with sepsis due to CAP, (b) presence of a control group, and (c) adult patients. Over-representation analysis was performed with clusterProfiler using the Hallmark and Reactome collections. (3) Results: A total of 4312 differentially expressed genes (DEGs) and sRNAs were included in the enrichment analysis. In the Hallmark collection, genes regulated by nuclear factor kappa B in response to tumor necrosis factor, genes upregulated by signal transducer and activator of transcription 5 in response to interleukin 2 stimulation, genes upregulated in response to interferon-gamma, genes defining the inflammatory response, a subgroup of genes regulated by MYC-version 1 (v1), and genes upregulated during transplant rejection were significantly enriched in critically ill septic patients with CAP. Moreover, 88 pathways were identified in the Reactome database. (4) Conclusions: This study summarizes the reported DEGs in critically ill septic patients with CAP and investigates their functional implications. The results highlight the complexity of immune responses during CAP.
Collapse
Affiliation(s)
- Diego Viasus
- Department of Medicine, Division of Health Sciences, Universidad del Norte and Hospital Universidad del Norte, Barranquilla 081001, Colombia
| | - Lara Nonell
- Departament de Biociències, Universitat de Vic—Universitat Central de Catalunya, 08500 Barcelona, Spain;
| | - Carlos Restrepo
- Department of Medicine, Division of Health Sciences, Universidad del Norte and Hospital Universidad del Norte, Barranquilla 081001, Colombia
| | - Fabian Figueroa
- Department of Medicine, Division of Health Sciences, Universidad del Norte and Hospital Universidad del Norte, Barranquilla 081001, Colombia
| | - Carla Donado-Mazarrón
- Department of Infectious Diseases, Bellvitge University Hospital, Bellvitge Biomedical Research Institute (IDIBELL), University of Barcelona, 08907 Barcelona, Spain;
| | - Jordi Carratalà
- Department of Infectious Diseases, Bellvitge University Hospital, Bellvitge Biomedical Research Institute (IDIBELL), University of Barcelona, 08907 Barcelona, Spain;
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, 28029 Madrid, Spain
| |
Collapse
|
4
|
Abstract
BACKGROUND Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, with extremely high mortality. Notably, sepsis is a heterogeneous syndrome characterized by a vast, multidimensional array of clinical and biologic features, which has hindered advances in the therapeutic field beyond the current standards. DATA SOURCES We used PubMed to search the subject-related medical literature by searching for the following single and/or combination keywords: sepsis, heterogeneity, personalized treatment, host response, infection, epidemiology, mortality, incidence, age, children, sex, comorbidities, gene susceptibility, infection sites, bacteria, fungi, virus, host response, organ dysfunction and management. RESULTS We found that host factors (age, biological sex, comorbidities, and genetics), infection etiology, host response dysregulation and multiple organ dysfunctions can all result in different disease manifestations, progression, and response to treatment, which make it difficult to effectively treat and manage sepsis patients. CONCLUSIONS Herein, we have summarized contributing factors to sepsis heterogeneity, including host factors, infection etiology, host response dysregulation, and multiple organ dysfunctions, from the key elements of pathogenesis of sepsis. An in-depth understanding of the factors that contribute to the heterogeneity of sepsis will help clinicians understand the complexity of sepsis and enable researchers to conduct more personalized clinical studies for homogenous patients.
Collapse
Affiliation(s)
- Wei Wang
- Department of Pediatrics, ShengJing Hospital of China Medical University, No. 36, SanHao Street, Shenyang City, Liaoning Province, 110004, China
| | - Chun-Feng Liu
- Department of Pediatrics, ShengJing Hospital of China Medical University, No. 36, SanHao Street, Shenyang City, Liaoning Province, 110004, China.
| |
Collapse
|
5
|
Gadsby NJ, Musher DM. The Microbial Etiology of Community-Acquired Pneumonia in Adults: from Classical Bacteriology to Host Transcriptional Signatures. Clin Microbiol Rev 2022; 35:e0001522. [PMID: 36165783 PMCID: PMC9769922 DOI: 10.1128/cmr.00015-22] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
All modern advances notwithstanding, pneumonia remains a common infection with substantial morbidity and mortality. Understanding of the etiology of pneumonia continues to evolve as new techniques enable identification of already known organisms and as new organisms emerge. We now review the etiology of pneumonia (at present often called "community-acquired pneumonia") beginning with classic bacteriologic techniques, which identified Streptococcus pneumoniae as the overwhelmingly common cause, to more modern bacteriologic studies, which emphasize Haemophilus influenzae, Staphylococcus aureus, Moraxella catarrhalis, Enterobacteriaceae, Pseudomonas, and normal respiratory flora. Urine antigen detection is useful in identifying Legionella and pneumococcus. The low yield of bacteria in recent studies is due to the failure to obtain valid sputum samples before antibiotics are administered. The use of high-quality sputum specimens enables identification of recognized ("typical") bacterial pathogens as well as a role for commensal bacteria ("normal respiratory flora"). Nucleic acid amplification technology for viruses has revolutionized diagnosis, showing the importance of viral pneumonia leading to hospitalization with or without coinfecting bacterial organisms. Quantitative PCR study of sputum is in its early stages of application, but regular detection of high counts of bacterial DNA from organisms that are not seen on Gram stain or grown in quantitative culture presents a therapeutic dilemma. This finding may reflect the host microbiome of the respiratory tract, in which case treatment may not need to be given for them. Finally, host transcriptional signatures might enable clinicians to distinguish between viral and bacterial pneumonia, an important practical consideration.
Collapse
Affiliation(s)
- Naomi J. Gadsby
- Department of Laboratory Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Daniel M. Musher
- Michael E. DeBakey Veterans Administration Medical Center, Houston, Texas, USA
- Baylor College of Medicine, Houston, Texas, USA
| |
Collapse
|
6
|
Chawla DG, Cappuccio A, Tamminga A, Sealfon SC, Zaslavsky E, Kleinstein SH. Benchmarking transcriptional host response signatures for infection diagnosis. Cell Syst 2022; 13:974-988.e7. [PMID: 36549274 PMCID: PMC9768893 DOI: 10.1016/j.cels.2022.11.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/04/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- Daniel G Chawla
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrea Tamminga
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Steven H Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA; Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT 06511, USA.
| |
Collapse
|
7
|
Ma Q, Zhang M, Zhang C, Teng X, Yang L, Tian Y, Wang J, Han D, Tan W. An automated DNA computing platform for rapid etiological diagnostics. SCIENCE ADVANCES 2022; 8:eade0453. [PMID: 36427311 PMCID: PMC9699674 DOI: 10.1126/sciadv.ade0453] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Rapid and accurate classification of the etiology for acute respiratory illness not only helps establish timely therapeutic plans but also prevents inappropriate use of antibiotics. Host gene expression patterns in peripheral blood can discriminate bacterial from viral causes of acute respiratory infection (ARI) but suffer from long turnaround time, as well as high cost resulting from the measurement methods of microarrays and next-generation sequencing. Here, we developed an automated DNA computing-based platform that can implement an in silico trained classification model at the molecular level with seven different mRNA expression patterns for accurate diagnosis of ARI etiology in 4 hours. By integrating sample loading, marker amplification, classifier implementation, and results reporting into one platform, we obtained a diagnostic accuracy of 87% in 80 clinical samples without the aid of computer and laboratory technicians. This platform creates opportunities toward an accurate, rapid, low-cost, and automated diagnosis of disease etiology in emergency departments or point-of-care clinics.
Collapse
Affiliation(s)
- Qian Ma
- Zhejiang Cancer Hospital, The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
- Intellinosis Biotechnologies Co. Ltd., Shanghai, China
| | - Mingzhi Zhang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Chao Zhang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
- Intellinosis Biotechnologies Co. Ltd., Shanghai, China
- Corresponding author. (D.H.); (W.T.); (C.Z.)
| | - Xiaoyan Teng
- Department of Laboratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 201306, China
| | - Linlin Yang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Yuan Tian
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Junyan Wang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Da Han
- Zhejiang Cancer Hospital, The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
- Corresponding author. (D.H.); (W.T.); (C.Z.)
| | - Weihong Tan
- Zhejiang Cancer Hospital, The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
- Corresponding author. (D.H.); (W.T.); (C.Z.)
| |
Collapse
|
8
|
Gant V, Singer M. Combining pathogen and host metagenomics for a better sepsis diagnostic. Nat Microbiol 2022; 7:1713-1714. [DOI: 10.1038/s41564-022-01255-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
9
|
Stojanovic Z, Gonçalves-Carvalho F, Marín A, Abad Capa J, Domínguez J, Latorre I, Lacoma A, Prat-Aymerich C. Advances in diagnostic tools for respiratory tract infections: from tuberculosis to COVID-19 - changing paradigms? ERJ Open Res 2022; 8:00113-2022. [PMID: 36101788 PMCID: PMC9235056 DOI: 10.1183/23120541.00113-2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/31/2022] [Indexed: 11/05/2022] Open
Abstract
Respiratory tract infections (RTIs) are one of the most common reasons for seeking healthcare, but are amongst the most challenging diseases in terms of clinical decision-making. Proper and timely diagnosis is critical in order to optimise management and prevent further emergence of antimicrobial resistance by misuse or overuse of antibiotics. Diagnostic tools for RTIs include those involving syndromic and aetiological diagnosis: from clinical and radiological features to laboratory methods targeting both pathogen detection and host biomarkers, as well as their combinations in terms of clinical algorithms. They also include tools for predicting severity and monitoring treatment response. Unprecedented milestones have been achieved in the context of the COVID-19 pandemic, involving the most recent applications of diagnostic technologies both at genotypic and phenotypic level, which have changed paradigms in infectious respiratory diseases in terms of why, how and where diagnostics are performed. The aim of this review is to discuss advances in diagnostic tools that impact clinical decision-making, surveillance and follow-up of RTIs and tuberculosis. If properly harnessed, recent advances in diagnostic technologies, including omics and digital transformation, emerge as an unprecedented opportunity to tackle ongoing and future epidemics while handling antimicrobial resistance from a One Health perspective.
Collapse
Affiliation(s)
- Zoran Stojanovic
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Co-first authors
| | - Filipe Gonçalves-Carvalho
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Co-first authors
| | - Alicia Marín
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Jorge Abad Capa
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Jose Domínguez
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
| | - Irene Latorre
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
| | - Alicia Lacoma
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
- Co-senior authors
| | - Cristina Prat-Aymerich
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Co-senior authors
| |
Collapse
|
10
|
Siljan WW, Sivakumaran D, Ritz C, Jenum S, Ottenhoff THM, Ulvestad E, Holter JC, Heggelund L, Grewal HMS. Host Transcriptional Signatures Predict Etiology in Community-Acquired Pneumonia: Potential Antibiotic Stewardship Tools. Biomark Insights 2022; 17:11772719221099130. [PMID: 35693251 PMCID: PMC9174553 DOI: 10.1177/11772719221099130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/20/2022] [Indexed: 11/01/2022] Open
Abstract
Background: Current approaches for pathogen identification in community-acquired pneumonia (CAP) remain suboptimal, leaving most patients without a microbiological diagnosis. If better diagnostic tools were available for differentiating between viral and bacterial CAP, unnecessary antibacterial therapy could be avoided in viral CAP patients. Methods: In 156 adults hospitalized with CAP classified to have bacterial, viral, or mixed viral-bacterial infection based on microbiological testing or both microbiological testing and procalcitonin (PCT) levels, we aimed to identify discriminatory host transcriptional signatures in peripheral blood samples acquired at hospital admission, by applying Dual-color-Reverse-Transcriptase-Multiplex-Ligation-dependent-Probe-Amplification (dc-RT MLPA). Results: In patients classified by microbiological testing, a 9-transcript signature showed high accuracy for discriminating bacterial from viral CAP (AUC 0.91, 95% CI 0.85-0.96), while a 10-transcript signature similarly discriminated mixed viral-bacterial from viral CAP (AUC 0.91, 95% CI 0.86-0.96). In patients classified by both microbiological testing and PCT levels, a 13-transcript signature showed excellent accuracy for discriminating bacterial from viral CAP (AUC 1.00, 95% CI 1.00-1.00), while a 7-transcript signature similarly discriminated mixed viral-bacterial from viral CAP (AUC 0.93, 95% CI 0.87-0.98). Conclusion: Our findings support host transcriptional signatures in peripheral blood samples as a potential tool for guiding clinical decision-making and antibiotic stewardship in CAP.
Collapse
Affiliation(s)
- William W Siljan
- Department of Pulmonary Medicine, Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Dhanasekaran Sivakumaran
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| | - Christian Ritz
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Synne Jenum
- Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | - Tom HM Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands
| | - Elling Ulvestad
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| | - Jan C Holter
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Lars Heggelund
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- Department of Internal Medicine, Vestre Viken Hospital Trust, Drammen, Norway
| | - Harleen MS Grewal
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| |
Collapse
|
11
|
Lemańska-Perek A, Krzyżanowska-Gołąb D, Dragan B, Tyszko M, Adamik B. Fibronectin as a Marker of Disease Severity in Critically Ill COVID-19 Patients. Cells 2022; 11:cells11091566. [PMID: 35563870 PMCID: PMC9100231 DOI: 10.3390/cells11091566] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 01/27/2023] Open
Abstract
The SARS-CoV-2 virus alters the expression of genes for extracellular matrix proteins, including fibronectin. The aim of the study was to establish the relationship between different forms of fibronectin, such as plasma (pFN), cellular (EDA-FN), and proteolytic FN-fragments, and disease severity and mortality of critically ill patients treated in the intensive care unit. The levels of pFN, EDA-FN, and FN-fragments were measured in patients with a viral (N = 43, COVID-19) or bacterial (N = 41, sepsis) infection, using immunoblotting and ELISA. The level of EDA-FN, but not pFN, was related to the treatment outcome and was significantly higher in COVID-19 Non-survivors than in Survivors. Furthermore, EDA-FN levels correlated with APACHE II and SOFA scores. FN-fragments were detected in 95% of COVID-19 samples and the amount was significantly higher in Non-survivors than in Survivors. Interestingly, FN-fragments were present in only 56% of samples from patients with bacterial sepsis, with no significant differences between Non-survivors and Survivors. The new knowledge gained from our research will help to understand the differences in immune response depending on the etiology of the infection. Fibronectin is a potential biomarker that can be used in clinical settings to monitor the condition of COVID-19 patients and predict treatment outcomes.
Collapse
Affiliation(s)
- Anna Lemańska-Perek
- Department of Chemistry and Immunochemistry, Wroclaw Medical University, M. Sklodowskiej-Curie 48/50, 50-369 Wroclaw, Poland;
- Correspondence:
| | - Dorota Krzyżanowska-Gołąb
- Department of Chemistry and Immunochemistry, Wroclaw Medical University, M. Sklodowskiej-Curie 48/50, 50-369 Wroclaw, Poland;
| | - Barbara Dragan
- Clinical Department of Anesthesiology and Intensive Therapy, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland; (B.D.); (M.T.); (B.A.)
| | - Maciej Tyszko
- Clinical Department of Anesthesiology and Intensive Therapy, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland; (B.D.); (M.T.); (B.A.)
| | - Barbara Adamik
- Clinical Department of Anesthesiology and Intensive Therapy, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland; (B.D.); (M.T.); (B.A.)
| |
Collapse
|
12
|
Kircher M, Chludzinski E, Krepel J, Saremi B, Beineke A, Jung K. Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin. Int J Mol Sci 2022; 23:ijms23052481. [PMID: 35269624 PMCID: PMC8910329 DOI: 10.3390/ijms23052481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.
Collapse
Affiliation(s)
- Magdalena Kircher
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
| | - Elisa Chludzinski
- Department of Pathology, University of Veterinary Medicine Hannover, Buenteweg 17, 30559 Hannover, Germany; (E.C.); (A.B.)
| | - Jessica Krepel
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
| | - Babak Saremi
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
| | - Andreas Beineke
- Department of Pathology, University of Veterinary Medicine Hannover, Buenteweg 17, 30559 Hannover, Germany; (E.C.); (A.B.)
| | - Klaus Jung
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
- Correspondence: ; Tel.: +49-511-953-8878
| |
Collapse
|
13
|
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.
Collapse
|
14
|
Atallah J, Mansour MK. Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review. Front Med (Lausanne) 2022; 9:805107. [PMID: 35186993 PMCID: PMC8850635 DOI: 10.3389/fmed.2022.805107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 12/15/2022] Open
Abstract
Host-based diagnostics are a rapidly evolving field that may serve as an alternative to traditional pathogen-based diagnostics for infectious diseases. Understanding the exact mechanisms underlying a host-immune response and deriving specific host-response signatures, biomarkers and gene transcripts will potentially achieve improved diagnostics that will ultimately translate to better patient outcomes. Several studies have focused on novel techniques and assays focused on immunodiagnostics. In this review, we will highlight recent publications on the current use of host-based diagnostics alone or in combination with traditional microbiological assays and their potential future implications on the diagnosis and prognostic accuracy for the patient with infectious complications. Finally, we will address the cost-effectiveness implications from a healthcare and public health perspective.
Collapse
Affiliation(s)
- Johnny Atallah
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Michael K. Mansour
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
15
|
McCall MN, Chu CY, Wang L, Benoodt L, Thakar J, Corbett A, Holden-Wiltse J, Slaunwhite C, Grier A, Gill SR, Falsey AR, Topham DJ, Caserta MT, Walsh EE, Qiu X, Mariani TJ. A systems genomics approach uncovers molecular associates of RSV severity. PLoS Comput Biol 2021; 17:e1009617. [PMID: 34962914 PMCID: PMC8746750 DOI: 10.1371/journal.pcbi.1009617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 01/10/2022] [Accepted: 11/05/2021] [Indexed: 01/06/2023] Open
Abstract
Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity. This paper presents a novel approach to understanding the localized molecular responses to respiratory syncytial virus (RSV) and the system-level correlates of clinical outcomes. To do this, we developed a novel statistical method able to integrate high dimensional molecular data characterizing the host airway microbota and immune and nasal gene expression. We show that this integrative approach facilitates superior performance in estimating clinical outcome as opposed to any single data type. Using this approach, we identified both cell type-specific and shared biomarkers and regulatory pathways associated with RSV severity. Specifically, we identified an association between RSV severity, activation of pathways controlling Th17, and inhibition of B cell receptor signaling, which were present in both the site of infection airway and in peripheral immune cells. These results can guide future efforts to identify biomarkers for identifying or predicting illness severity following infant RSV infection. They may also be useful as biomarkers to inform the efficacy of future interventions (e.g., therapies) or preventative measures to suppress the rate of severe disease (e.g., vaccines).
Collapse
Affiliation(s)
- Matthew N McCall
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester New York, United States of America.,Department of Biomedical Genetics, University of Rochester Medical Center, Rochester New York, United States of America
| | - Chin-Yi Chu
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, University of Rochester Medical Center, Rochester New York, United States of America.,Department of Pediatrics, University of Rochester Medical Center, Rochester New York, United States of America
| | - Lu Wang
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester New York, United States of America
| | - Lauren Benoodt
- Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester New York, United States of America
| | - Juilee Thakar
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester New York, United States of America.,Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester New York, United States of America
| | - Anthony Corbett
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester New York, United States of America.,Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester New York, United States of America
| | - Jeanne Holden-Wiltse
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester New York, United States of America.,Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester New York, United States of America
| | - Christopher Slaunwhite
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, University of Rochester Medical Center, Rochester New York, United States of America.,Department of Pediatrics, University of Rochester Medical Center, Rochester New York, United States of America
| | - Alex Grier
- Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester New York, United States of America
| | - Steven R Gill
- Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester New York, United States of America
| | - Ann R Falsey
- Department of Medicine, University of Rochester Medical Center, Rochester New York, United States of America.,Department of Medicine, Rochester General Hospital, Rochester New York, United States of America
| | - David J Topham
- Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester New York, United States of America.,David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester New York, United States of America
| | - Mary T Caserta
- Department of Pediatrics, University of Rochester Medical Center, Rochester New York, United States of America
| | - Edward E Walsh
- Department of Medicine, University of Rochester Medical Center, Rochester New York, United States of America.,Department of Medicine, Rochester General Hospital, Rochester New York, United States of America
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester New York, United States of America
| | - Thomas J Mariani
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, University of Rochester Medical Center, Rochester New York, United States of America.,Department of Pediatrics, University of Rochester Medical Center, Rochester New York, United States of America
| |
Collapse
|
16
|
Ross M, Henao R, Burke TW, Ko ER, McClain MT, Ginsburg GS, Woods CW, Tsalik EL. A comparison of host response strategies to distinguish bacterial and viral infection. PLoS One 2021; 16:e0261385. [PMID: 34905580 PMCID: PMC8670660 DOI: 10.1371/journal.pone.0261385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/29/2021] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI). METHODS In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests. RESULTS The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively. CONCLUSIONS A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.
Collapse
Affiliation(s)
- Melissa Ross
- Duke University School of Medicine, Durham, NC, United States of America
| | - Ricardo Henao
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
| | - Thomas W. Burke
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC, United States of America
| | - Emily R. Ko
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC, United States of America
- Duke Regional Hospital, Durham, NC, United States of America
| | - Micah T. McClain
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC, United States of America
- Medical Service, Durham Veterans Affairs Health Care System, Durham, NC, United States of America
| | - Geoffrey S. Ginsburg
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC, United States of America
| | - Christopher W. Woods
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC, United States of America
- Medical Service, Durham Veterans Affairs Health Care System, Durham, NC, United States of America
| | - Ephraim L. Tsalik
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC, United States of America
- Emergency Medicine Service, Durham Veterans Affairs Health Care System, Durham, NC, United States of America
| |
Collapse
|
17
|
Peterson DR, Baran AM, Bhattacharya S, Branche AR, Croft DP, Corbett AM, Walsh EE, Falsey AR, Mariani TJ. Gene Expression Risk Scores for COVID-19 Illness Severity. J Infect Dis 2021; 227:322-331. [PMID: 34850892 PMCID: PMC8767880 DOI: 10.1093/infdis/jiab568] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/29/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood. METHODS We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19. RESULTS Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. CONCLUSIONS These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
Collapse
Affiliation(s)
- Derick R Peterson
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA
| | - Andrea M Baran
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA
| | - Soumyaroop Bhattacharya
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester, Rochester, New York, USA
| | - Angela R Branche
- Division of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, New York, USA
| | - Daniel P Croft
- Division of Pulmonary and Critical Care, Department of Medicine, University of Rochester, Rochester, New York, USA
| | - Anthony M Corbett
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA
| | - Edward E Walsh
- Division of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, New York, USA,Department of Medicine, Rochester General Hospital, Rochester, New York, USA
| | - Ann R Falsey
- Division of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, New York, USA,Department of Medicine, Rochester General Hospital, Rochester, New York, USA
| | - Thomas J Mariani
- Correspondence: Thomas J. Mariani, PhD, Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, University of Rochester Medical Center, 601 Elmwood Ave, Box 850, Rochester, NY 14642 ()
| |
Collapse
|
18
|
Póvoa P, Coelho L. Which Biomarkers Can Be Used as Diagnostic Tools for Infection in Suspected Sepsis? Semin Respir Crit Care Med 2021; 42:662-671. [PMID: 34544183 DOI: 10.1055/s-0041-1735148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The diagnosis of infection in patients with suspected sepsis is frequently difficult to achieve with a reasonable degree of certainty. Currently, the diagnosis of infection still relies on a combination of systemic manifestations, manifestations of organ dysfunction, and microbiological documentation. In addition, the microbiologic confirmation of infection is obtained only after 2 to 3 days of empiric antibiotic therapy. These criteria are far from perfect being at least in part responsible for the overuse and misuse of antibiotics, in the community and in hospital, and probably the main drive for antibiotic resistance. Biomarkers have been studied and used in several clinical settings as surrogate markers of infection to improve their diagnostic accuracy as well as in the assessment of response to antibiotics and in antibiotic stewardship programs. The aim of this review is to provide a clear overview of the current evidence of usefulness of biomarkers in several clinical scenarios, namely, to diagnose infection to prescribe antibiotics, to exclude infection to withhold antibiotics, and to identify the causative pathogen to target antimicrobial treatment. In recent years, new evidence with "old" biomarkers, like C-reactive protein and procalcitonin, as well as new biomarkers and molecular tests, as breathomics or bacterial DNA identification by polymerase chain reaction, increased markedly in different areas adding useful information for clinical decision making at the bedside when adequately used. The recent evidence shows that the information given by biomarkers can support the suspicion of infection and pathogen identification but also, and not less important, can exclude its diagnosis. Although the ideal biomarker has not yet been found, there are various promising biomarkers that represent true evolutions in the diagnosis of infection in patients with suspected sepsis.
Collapse
Affiliation(s)
- Pedro Póvoa
- Polyvalent Intensive Care Unit, Sao Francisco Xavier Hospital, CHLO, Lisbon, Portugal.,Nova Medical School, Clinical Medicine, CHRC, New University of Lisbon, Lisbon, Portugal.,Center for Clinical Epidemiology and Research Unit of Clinical Epidemiology, OUH Odense University Hospital, Odense, Denmark
| | - Luis Coelho
- Polyvalent Intensive Care Unit, Sao Francisco Xavier Hospital, CHLO, Lisbon, Portugal.,Nova Medical School, Clinical Medicine, CHRC, New University of Lisbon, Lisbon, Portugal
| |
Collapse
|
19
|
Xu N, Hao F, Dong X, Yao Y, Guan Y, Yang L, Chen F, Zheng F, Li Q, Liu W, Zhao C, Li W, Palavecino E, Wang W, Wang G. A two-transcript biomarker of host classifier genes for discrimination of bacterial from viral infection in acute febrile illness: a multicentre discovery and validation study. LANCET DIGITAL HEALTH 2021; 3:e507-e516. [PMID: 34325854 DOI: 10.1016/s2589-7500(21)00102-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 04/07/2021] [Accepted: 05/12/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Acute febrile illness is one of the main reasons for outpatient hospital visits worldwide. However, differential diagnosis between bacterial and viral causes is challenging and misdiagnosis can result in antimicrobial overuse and hinder prompt treatment. We aimed to build and validate a diagnostic model to discriminate bacterial from viral infection in acute febrile illness by evaluating the expression of potential classifier host genes. METHODS In this multicentre discovery and validation study, we included patients aged 14-85 years with acute febrile illness (fever for ≤14 days, axillary temperature of ≥38°C, and confirmed bacterial infection, viral infection, or non-infectious inflammatory disease), and healthy control participants (no significant medical history and no fever within the past 90 days) from four hospitals in Shandong province, China. Patients from the first hospital were divided into the screening, discovery, and internal validation groups, and patients from the three other hospitals comprised the external validation group. We measured expression of candidate genes in peripheral blood by RT-PCR, and patients for whom a successful RT-PCT result was recorded were included in the next-step analysis. For patients from the first hospital, those enrolled during the early phase of the study were assigned to the screening group, which was used to identify the optimal transcripts (IFI44L and PI3) for discrimination between bacterial and viral infections by screening four candidate genes (FAM89A, IFI44L, PI3, and ITGB2) by RT-PCR. The remaining patients were then randomly assigned (1:1) to discovery and internal validation groups by time of admission and blood drawing via the equidistant random sampling method. A logistic regression model integrating the mRNA levels of IFI44L and PI3 was built by use of the discovery group, and the diagnostic performance of the model was evaluated in the internal and external validation groups using area under the receiver operating curve (AUC), sensitivity, and specificity. FINDINGS Between March 1, 2018, and Aug 31, 2019, we assessed 1658 individuals for inclusion in the study. After exclusion of ineligible participants, 458 participants were enrolled (178 patients with acute febrile illness caused by bacterial infection, 212 with acute febrile illness caused by viral infection, 38 with non-infectious inflammatory diseases, and 30 healthy controls). The 390 patients with bacterial or viral infections were assigned to one of four groups: screening (n=64, 33 with bacterial infections and 31 with viral infections), discovery (n=124, 55 with bacterial infections and 69 with viral infections), internal validation (n=124, 55 with bacterial infections and 69 with viral infections), and external validation (n=78, 35 with bacterial infections and 43 with viral infections). Of the four candidate host genes (FAM89A, IFI44L, PI3, and ITGB2), IFI44L and PI3 showed the most discriminative expression pattern and were used to build the logistic regression model. We established the optimal cutoff of the bacterial infection likelihood score to be 0·547598. With the diagnostic result from the gold standard tests (culture and PCR) as the reference, the two-transcript classifier model had an AUC of 0·969 (95% CI 0·937-1·000), sensitivity of 0·891 (0·782-0·949), and specificity of 0·971 (0·900-0·992) to discriminate bacterial and viral infections in the internal validation group. The model showed similar results in the external validation group (AUC 0·986, 95% CI 0·968-1·000; sensitivity 0·857, 0·706-0·937; and specificity 0·954, 0·845-0·987). INTERPRETATION IFI44L and PI3 transcripts, measured by RT-PCR, are robust classifiers to discriminate bacterial from viral infection in acute febrile illness. This two-transcript biomarker has the potential to be transformed into a commercial panel and applied universally. FUNDING None.
Collapse
Affiliation(s)
- Nannan Xu
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fanchang Hao
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Xiaomeng Dong
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yongyuan Yao
- Department of Intensive Care Medicine, Rizhao People's Hospital, Rizhao, China
| | - Yanyan Guan
- Department of Infectious Disease, Rizhao People's Hospital, Rizhao, China
| | - Lulu Yang
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fengzhe Chen
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Feng Zheng
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qingyan Li
- Department of Infectious Disease, Liaocheng People's Hospital, Liaocheng, China
| | - Wenguo Liu
- Department of Infectious Disease, Gaotang People's Hospital, Liaocheng, China
| | - Cui Zhao
- Department of Infectious Disease, Gaotang People's Hospital, Liaocheng, China
| | - Wen Li
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | | | - Wei Wang
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gang Wang
- Department of Infectious Disease, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
| |
Collapse
|
20
|
Peterson DR, Baran AM, Bhattacharya S, Branche AR, Croft DP, Corbett AM, Walsh EE, Falsey AR, Mariani TJ. Gene Expression Risk Scores for COVID-19 Illness Severity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.08.24.457521. [PMID: 34462743 PMCID: PMC8404885 DOI: 10.1101/2021.08.24.457521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood. METHODS We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID. RESULTS Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. CONCLUSION These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
Collapse
Affiliation(s)
- Derick R Peterson
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
| | - Andrea M Baran
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
| | - Soumyaroop Bhattacharya
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester, Rochester, NY, USA
| | - Angela R Branche
- Division of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, NY, USA
| | - Daniel P Croft
- Division of Pulmonary and Critical Care, Department of Medicine, University of Rochester, Rochester, NY, USA
| | - Anthony M Corbett
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
| | - Edward E Walsh
- Division of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, NY, USA
- Department of Medicine, Rochester General Hospital, Rochester, NY, USA
| | - Ann R Falsey
- Division of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, NY, USA
- Department of Medicine, Rochester General Hospital, Rochester, NY, USA
| | - Thomas J Mariani
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester, Rochester, NY, USA
| |
Collapse
|
21
|
Li HK, Kaforou M, Rodriguez-Manzano J, Channon-Wells S, Moniri A, Habgood-Coote D, Gupta RK, Mills EA, Arancon D, Lin J, Chiu YH, Pennisi I, Miglietta L, Mehta R, Obaray N, Herberg JA, Wright VJ, Georgiou P, Shallcross LJ, Mentzer AJ, Levin M, Cooke GS, Noursadeghi M, Sriskandan S. Discovery and validation of a three-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations: a case-control and observational cohort study. LANCET MICROBE 2021; 2:e594-e603. [PMID: 34423323 PMCID: PMC8367196 DOI: 10.1016/s2666-5247(21)00145-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Background Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We aimed to derive and validate a blood transcriptional signature to detect viral infections, including COVID-19, among adults with suspected infection who presented to the emergency department. Methods Individuals (aged ≥18 years) presenting with suspected infection to an emergency department at a major teaching hospital in the UK were prospectively recruited as part of the Bioresource in Adult Infectious Diseases (BioAID) discovery cohort. Whole-blood RNA sequencing was done on samples from participants with subsequently confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes that met additional filtering criteria were subjected to feature selection to derive the most parsimonious discriminating signature. We validated the signature via RT-qPCR in a prospective validation cohort of participants who presented to an emergency department with undifferentiated fever, and a second case-control validation cohort of emergency department participants with PCR-positive COVID-19 or bacterial infection. We assessed signature performance by calculating the area under receiver operating characteristic curves (AUROCs), sensitivities, and specificities. Findings A three-gene transcript signature, comprising HERC6, IGF1R, and NAGK, was derived from the discovery cohort of 56 participants with bacterial infections and 27 with viral infections. In the validation cohort of 200 participants, the signature differentiated bacterial from viral infections with an AUROC of 0·976 (95% CI 0·919−1·000), sensitivity of 97·3% (85·8−99·9), and specificity of 100% (63·1−100). The AUROC for C-reactive protein (CRP) was 0·833 (0·694−0·944) and for leukocyte count was 0·938 (0·840−0·986). The signature achieved higher net benefit in decision curve analysis than either CRP or leukocyte count for discriminating viral infections from all other infections. In the second validation analysis, which included SARS-CoV-2-positive participants, the signature discriminated 35 bacterial infections from 34 SARS-CoV-2-positive COVID-19 infections with AUROC of 0·953 (0·893−0·992), sensitivity 88·6%, and specificity of 94·1%. Interpretation This novel three-gene signature discriminates viral infections, including COVID-19, from other emergency infection presentations in adults, outperforming both leukocyte count and CRP, thus potentially providing substantial clinical utility in managing acute presentations with infection. Funding National Institute for Health Research, Medical Research Council, Wellcome Trust, and EU-FP7.
Collapse
Affiliation(s)
- Ho Kwong Li
- Department of Infectious Disease, Imperial College London, London, UK
- Medical Research Council Centre for Molecular Bacteriology & Infection, Imperial College London, London, UK
| | - Myrsini Kaforou
- Department of Infectious Disease, Imperial College London, London, UK
| | - Jesus Rodriguez-Manzano
- Department of Infectious Disease, Imperial College London, London, UK
- National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infection & Antimicrobial Resistance, Imperial College London, London, UK
| | | | - Ahmad Moniri
- Department of Electrical & Electronic Engineering, Imperial College London, London, UK
| | | | - Rishi K Gupta
- Institute of Global Health, University College London, London, UK
| | - Ewurabena A Mills
- Department of Infectious Disease, Imperial College London, London, UK
| | | | - Jessica Lin
- Department of Infectious Disease, Imperial College London, London, UK
| | - Yueh-Ho Chiu
- Department of Infectious Disease, Imperial College London, London, UK
| | - Ivana Pennisi
- Department of Infectious Disease, Imperial College London, London, UK
| | - Luca Miglietta
- Department of Infectious Disease, Imperial College London, London, UK
- Department of Electrical & Electronic Engineering, Imperial College London, London, UK
| | - Ravi Mehta
- Department of Infectious Disease, Imperial College London, London, UK
| | - Nelofar Obaray
- Department of Infectious Disease, Imperial College London, London, UK
| | - Jethro A Herberg
- Department of Infectious Disease, Imperial College London, London, UK
| | - Victoria J Wright
- Department of Infectious Disease, Imperial College London, London, UK
| | - Pantelis Georgiou
- Department of Electrical & Electronic Engineering, Imperial College London, London, UK
- Centre for Bio-Inspired Technology, Imperial College London, London, UK
| | | | | | - Michael Levin
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham S Cooke
- Department of Infectious Disease, Imperial College London, London, UK
| | - Mahdad Noursadeghi
- Division of Infection and Immunity, University College London, London, UK
| | - Shiranee Sriskandan
- Department of Infectious Disease, Imperial College London, London, UK
- Medical Research Council Centre for Molecular Bacteriology & Infection, Imperial College London, London, UK
- National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infection & Antimicrobial Resistance, Imperial College London, London, UK
| |
Collapse
|
22
|
Di Minno A, Gelzo M, Stornaiuolo M, Ruoppolo M, Castaldo G. The evolving landscape of untargeted metabolomics. Nutr Metab Cardiovasc Dis 2021; 31:1645-1652. [PMID: 33895079 DOI: 10.1016/j.numecd.2021.01.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/08/2021] [Accepted: 01/13/2021] [Indexed: 02/07/2023]
Abstract
AIMS Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics. DATA SYNTHESIS Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available. CONCLUSIONS Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.
Collapse
Affiliation(s)
- Alessandro Di Minno
- Dipartimento di Farmacia, Università Degli Studi di Napoli "Federico II", Napoli, 80131, Italy; CEINGE-Biotecnologie Avanzate, Naples, Italy
| | - Monica Gelzo
- CEINGE-Biotecnologie Avanzate, Naples, Italy; Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Naples, Italy
| | - Mariano Stornaiuolo
- Dipartimento di Farmacia, Università Degli Studi di Napoli "Federico II", Napoli, 80131, Italy
| | - Margherita Ruoppolo
- CEINGE-Biotecnologie Avanzate, Naples, Italy; Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Naples, Italy
| | - Giuseppe Castaldo
- CEINGE-Biotecnologie Avanzate, Naples, Italy; Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Naples, Italy.
| |
Collapse
|
23
|
Gómez-Carballa A, Barral-Arca R, Cebey-López M, Bello X, Pardo-Seco J, Martinón-Torres F, Salas A. Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis. Int J Mol Sci 2021; 22:ijms22063148. [PMID: 33808774 PMCID: PMC8003556 DOI: 10.3390/ijms22063148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/11/2021] [Accepted: 03/15/2021] [Indexed: 12/24/2022] Open
Abstract
The fight against the spread of antibiotic resistance is one of the most important challenges facing health systems worldwide. Given the limitations of current diagnostic methods, the development of fast and accurate tests for the diagnosis of viral and bacterial infections would improve patient management and treatment, as well as contribute to reducing antibiotic misuse in clinical settings. In this scenario, analysis of host transcriptomics constitutes a promising target to develop new diagnostic tests based on the host-specific response to infections. We carried out a multi-cohort meta-analysis of blood transcriptomic data available in public databases, including 11 different studies and 1209 samples from virus- (n = 695) and bacteria- (n = 514) infected patients. We applied a Parallel Regularized Regression Model Search (PReMS) on a set of previously reported genes that distinguished viral from bacterial infection to find a minimum gene expression bio-signature. This strategy allowed us to detect three genes, namely BAFT, ISG15 and DNMT1, that clearly differentiate groups of infection with high accuracy (training set: area under the curve (AUC) 0.86 (sensitivity: 0.81; specificity: 0.87); testing set: AUC 0.87 (sensitivity: 0.82; specificity: 0.86)). BAFT and ISG15 are involved in processes related to immune response, while DNMT1 is related to the preservation of methylation patterns, and its expression is modulated by pathogen infections. We successfully tested this three-transcript signature in the 11 independent studies, demonstrating its high performance under different scenarios. The main advantage of this three-gene signature is the low number of genes needed to differentiate both groups of patient categories.
Collapse
Affiliation(s)
- Alberto Gómez-Carballa
- GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain; (A.G.-C.); (R.B.-A.); (M.C.-L.); (X.B.); (J.P.-S.)
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago de Compostela, 15706 Galicia, Spain;
| | - Ruth Barral-Arca
- GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain; (A.G.-C.); (R.B.-A.); (M.C.-L.); (X.B.); (J.P.-S.)
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago de Compostela, 15706 Galicia, Spain;
| | - Miriam Cebey-López
- GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain; (A.G.-C.); (R.B.-A.); (M.C.-L.); (X.B.); (J.P.-S.)
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago de Compostela, 15706 Galicia, Spain;
| | - Xabier Bello
- GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain; (A.G.-C.); (R.B.-A.); (M.C.-L.); (X.B.); (J.P.-S.)
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago de Compostela, 15706 Galicia, Spain;
| | - Jacobo Pardo-Seco
- GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain; (A.G.-C.); (R.B.-A.); (M.C.-L.); (X.B.); (J.P.-S.)
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago de Compostela, 15706 Galicia, Spain;
| | - Federico Martinón-Torres
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago de Compostela, 15706 Galicia, Spain;
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, 15706 Galicia, Spain
| | - Antonio Salas
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, 15706 Galicia, Spain
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, 15706 Galicia, Spain
- Correspondence:
| |
Collapse
|
24
|
Lei H, Xu X, Wang C, Xue D, Wang C, Chen J. A host-based two-gene model for the identification of bacterial infection in general clinical settings. Int J Infect Dis 2021; 105:662-667. [PMID: 33667695 DOI: 10.1016/j.ijid.2021.02.112] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVES In this study, we aimed to develop a simple gene model to identify bacterial infection, which can be implemented in general clinical settings. METHODS We used a clinically availablereal-time quantitative polymerase chain reaction platform to conduct focused gene expression assays on clinical blood samples. Samples were collected from 2 tertiary hospitals. RESULTS We found that the 8 candidate genes for bacterial infection were significantly dysregulated in bacterial infection and displayed good performance in group classification, whereas the 2 genes for viral infection displayed poor performance. A two-gene model (S100A12 and CD177) displayed 93.0% sensitivity and 93.7% specificity in the modeling stage. In the independent validation stage, 87.8% sensitivity and 96.6% specificity were achieved in one set of case-control groups, and 93.6% sensitivity and 97.1% specificity in another set. CONCLUSIONS We have validated the signature genes for bacterial infection and developed a two-gene model to identify bacterial infection in general clinical settings.
Collapse
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.
| | - Xiaoyue Xu
- Department of Clinical Laboratory, 307th Hospital of Chinese People's Liberation Army, Beijing, China
| | - Chi Wang
- Department of Clinical Laboratory of Medicine, Chinese PLA general hospital & Medical School of Chinese PLA, Beijing, China
| | - Dandan Xue
- Department of Clinical Laboratory of Medicine, Chinese PLA general hospital & Medical School of Chinese PLA, Beijing, China
| | - Chengbin Wang
- Department of Clinical Laboratory of Medicine, Chinese PLA general hospital & Medical School of Chinese PLA, Beijing, China.
| | - Jiankui Chen
- Department of Clinical Laboratory, 307th Hospital of Chinese People's Liberation Army, Beijing, China.
| |
Collapse
|
25
|
Schaenman JM, Rossetti M, Liang EC, Lum E, Abdalla B, Bunnapradist S, Pham PT, Danovitch G, Reed EF, Cole SW. Leukocyte transcriptome indicators of development of infection in kidney transplant recipients. Clin Transplant 2021; 35:e14252. [PMID: 33570750 DOI: 10.1111/ctr.14252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 01/18/2021] [Accepted: 01/31/2021] [Indexed: 12/01/2022]
Abstract
After kidney transplantation, infection and death are important clinical complications, especially for the growing numbers of older patients with limited resilience to withstand adverse events. Evaluation of changes in gene expression in immune cells can reveal the underlying mechanisms behind vulnerability to infection. A cohort of 60 kidney transplant recipients was evaluated. Gene expression in peripheral blood mononuclear cells 3 months after kidney transplantation was analyzed to compare differences between patients with infection and those who were infection-free in the first-year post-transplant. Pro-inflammatory genes such as IL1B, CCL4, and TNF were found to be downregulated in post-transplant PBMC from patients who developed infection. In contrast, genes involved in metabolism, HLA genes, and transcripts involved in type I interferon innate antiviral responses were found to be upregulated. Promoter-based bioinformatic analyses implicated increased activity of interferon regulatory factors, erythroid nuclear factor (E2), and CCAAT-enhancer-binding protein (C/EBP) in patients who developed infections. Differential patterns of gene expression were observed in patients who developed infection after kidney transplantation, with patterns distinct from changes associated with patient age, suggesting possible mechanisms behind vulnerability to infection. Assessment of gene expression in blood may offer an approach for patient risk stratification and monitoring after transplantation.
Collapse
Affiliation(s)
- Joanna M Schaenman
- Division of Infectious Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Maura Rossetti
- Department of Pathology and Laboratory Medicine, UCLA Immunogenetics Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Emily C Liang
- Division of Infectious Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Erik Lum
- Division of Nephrology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Basmah Abdalla
- Division of Nephrology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Suphamai Bunnapradist
- Division of Nephrology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Phuong Thu Pham
- Division of Nephrology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Gabriel Danovitch
- Division of Nephrology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Elaine F Reed
- Department of Pathology and Laboratory Medicine, UCLA Immunogenetics Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Steve W Cole
- Department of Medicine, Division of Hematology-Oncology, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| |
Collapse
|
26
|
Distinguishing bacterial versus non-bacterial causes of febrile illness - A systematic review of host biomarkers. J Infect 2021; 82:1-10. [PMID: 33610683 DOI: 10.1016/j.jinf.2021.01.028] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Acute febrile illnesses (AFIs) represent a major disease burden globally; however, the paucity of reliable, rapid point-of-care testing makes their diagnosis difficult. A simple tool for distinguishing bacterial versus non-bacterial infections would radically improve patient management and reduce indiscriminate antibiotic use. Diagnostic tests based on host biomarkers can play an important role here, and a target product profile (TPP) was developed to guide development. OBJECTIVES To qualitatively evaluate host biomarkers that can distinguish bacterial from non-bacterial causes of AFI. DATA SOURCES The PubMed database was systematically searched for relevant studies published between 2015 and 2019. STUDY ELIGIBILITY CRITERIA Studies comparing diagnostic performances of host biomarkers in patients with bacterial versus non-bacterial infections were included. PARTICIPANTS Studies involving human participants and/or human samples were included. METHODS We collected information following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A risk of bias assessment was performed, based on a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Score 2). RESULTS We identified 1107 publications. Following screening, 55 publications were included, with 265 biomarker entries. Entries mostly comprised protein biomarkers (58.9%), followed by haematological, RNA, and metabolite biomarkers (15.5%, 8.7%, 12.5%). Sensitivity/specificity was reported for 45.7% of biomarker entries. We assessed a high overall risk of bias for most entries (75.8%). In studies with low/medium risk of bias, four biomarker entries tested in blood samples had sensitivity/specificity of more than 0.90/0.80. Only 12 additional biomarker entries were identified with sensitivity/specificity of more than 0.65/0.65. CONCLUSIONS Most recently assessed biomarkers represent well-known biomarkers, e.g. C-reactive protein and procalcitonin. Some protein biomarkers with the highest reported performances include a combined biomarker signature (CRP, IP-10, and TRAIL) and human neutrophil lipocalin (HNL). Few new biomarkers are in the pipeline; however, some RNA signatures show promise. Further high-quality studies are needed to confirm these findings.
Collapse
|
27
|
Gillette MA, Mani DR, Uschnig C, Pellé KG, Madrid L, Acácio S, Lanaspa M, Alonso P, Valim C, Carr SA, Schaffner SF, MacInnis B, Milner DA, Bassat Q, Wirth DF. Biomarkers to distinguish bacterial from viral pediatric clinical pneumonia in a malaria endemic setting. Clin Infect Dis 2021; 73:e3939-e3948. [PMID: 33534888 PMCID: PMC8653634 DOI: 10.1093/cid/ciaa1843] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Indexed: 12/05/2022] Open
Abstract
Background Differential etiologies of pediatric acute febrile respiratory illness pose challenges for all populations globally, but especially in malaria-endemic settings because the pathogens responsible overlap in clinical presentation and frequently occur together. Rapid identification of bacterial pneumonia with high-quality diagnostic tools would enable appropriate, point-of-care antibiotic treatment. Current diagnostics are insufficient, and the discovery and development of new tools is needed. We report a unique biomarker signature identified in blood samples to accomplish this. Methods Blood samples from 195 pediatric Mozambican patients with clinical pneumonia were analyzed with an aptamer-based, high-dynamic-range, quantitative assay (~1200 proteins). We identified new biomarkers using a training set of samples from patients with established bacterial, viral, or malarial pneumonia. Proteins with significantly variable abundance across etiologies (false discovery rate <0.01) formed the basis for predictive diagnostic models derived from machine learning techniques (Random Forest, Elastic Net). Validation on a dedicated test set of samples was performed. Results Significantly different abundances between bacterial and viral infections (219 proteins) and bacterial infections and mixed (viral and malaria) infections (151 proteins) were found. Predictive models achieved >90% sensitivity and >80% specificity, regardless of number of pathogen classes. Bacterial pneumonia was strongly associated with neutrophil markers—in particular, degranulation including HP, LCN2, LTF, MPO, MMP8, PGLYRP1, RETN, SERPINA1, S100A9, and SLPI. Conclusions Blood protein signatures highly associated with neutrophil biology reliably differentiated bacterial pneumonia from other causes. With appropriate technology, these markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia.
Collapse
Affiliation(s)
- Michael A Gillette
- Broad Institute of MIT and Harvard, Cambridge, MA.,Massachusetts General Hospital, Hospital Division of Pulmonary and Critical Care Medicine, Boston, MA.,Harvard Medical School, Boston, MA
| | - D R Mani
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Christopher Uschnig
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA
| | - Karell G Pellé
- Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA
| | - Lola Madrid
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.,Centro de Investigação em Saúde de Manhiça (CISM), CP Maputo, Mozambique
| | - Sozinho Acácio
- Centro de Investigação em Saúde de Manhiça (CISM), CP Maputo, Mozambique
| | - Miguel Lanaspa
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.,Centro de Investigação em Saúde de Manhiça (CISM), CP Maputo, Mozambique
| | - Pedro Alonso
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.,Centro de Investigação em Saúde de Manhiça (CISM), CP Maputo, Mozambique
| | - Clarissa Valim
- Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA.,Boston University School of Public Health, Department of Global Health, Boston, MA
| | | | - Stephen F Schaffner
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA
| | - Bronwyn MacInnis
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA
| | - Danny A Milner
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard Medical School, Boston, MA.,Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA.,ASCP - The American Society for Clinical Pathology, Chicago, IL
| | - Quique Bassat
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.,Centro de Investigação em Saúde de Manhiça (CISM), CP Maputo, Mozambique.,ICREA, Pg. Lluís Companys, Barcelona, Spain.,Pediatric Infectious Diseases Unit, Pediatrics Department, Hospital Sant Joan de Déu (University of Barcelona), Barcelona, Spain.,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Dyann F Wirth
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T. H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA
| |
Collapse
|
28
|
Bhattacharya S, Mereness JA, Baran AM, Misra RS, Peterson DR, Ryan RM, Reynolds AM, Pryhuber GS, Mariani TJ. Lymphocyte-Specific Biomarkers Associated With Preterm Birth and Bronchopulmonary Dysplasia. Front Immunol 2021; 11:563473. [PMID: 33552042 PMCID: PMC7859626 DOI: 10.3389/fimmu.2020.563473] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 12/07/2020] [Indexed: 01/11/2023] Open
Abstract
Many premature babies who are born with neonatal respiratory distress syndrome (RDS) go on to develop Bronchopulmonary Dysplasia (BPD) and later Post-Prematurity Respiratory Disease (PRD) at one year corrected age, characterized by persistent or recurrent lower respiratory tract symptoms frequently related to inflammation and viral infection. Transcriptomic profiles were generated from sorted peripheral blood CD8+ T cells of preterm and full-term infants enrolled with consent in the NHLBI Prematurity and Respiratory Outcomes Program (PROP) at the University of Rochester and the University at Buffalo. We identified outcome-related gene expression patterns following standard methods to identify markers for oxygen utilization and BPD as outcomes in extremely premature infants. We further identified predictor gene sets for BPD based on transcriptomic data adjusted for gestational age at birth (GAB). RNA-Seq analysis was completed for CD8+ T cells from 145 subjects. Among the subjects with highest risk for BPD (born at <29 weeks gestational age (GA); n=72), 501 genes were associated with oxygen utilization. In the same set of subjects, 571 genes were differentially expressed in subjects with a diagnosis of BPD and 105 genes were different in BPD subjects as defined by physiologic challenge. A set of 92 genes could predict BPD with a moderately high degree of accuracy. We consistently observed dysregulation of TGFB, NRF2, HIPPO, and CD40-associated pathways in BPD. Using gene expression data from both premature and full-term subjects (n=116), we identified a 28 gene set that predicted the PRD status with a moderately high level of accuracy, which also were involved in TGFB signaling. Transcriptomic data from sort-purified peripheral blood CD8+ T cells from 145 preterm and full-term infants identified sets of molecular markers of inflammation associated with independent development of BPD in extremely premature infants at high risk for the disease and of PRD among the preterm and full-term subjects.
Collapse
Affiliation(s)
- Soumyaroop Bhattacharya
- Division of Neonatology, Department of Pediatrics, University of Rochester, Rochester, NY, United States
| | - Jared A Mereness
- Division of Neonatology, Department of Pediatrics, University of Rochester, Rochester, NY, United States
| | - Andrea M Baran
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States
| | - Ravi S Misra
- Division of Neonatology, Department of Pediatrics, University of Rochester, Rochester, NY, United States
| | - Derick R Peterson
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States
| | - Rita M Ryan
- Department of Pediatrics, University at Buffalo, Buffalo, NY, United States.,Department of Pediatrics, Case Western Reserve University, Cleveland, OH, United States
| | | | - Gloria S Pryhuber
- Division of Neonatology, Department of Pediatrics, University of Rochester, Rochester, NY, United States
| | - Thomas J Mariani
- Division of Neonatology, Department of Pediatrics, University of Rochester, Rochester, NY, United States
| |
Collapse
|
29
|
Mahle RE, Suchindran S, Henao R, Steinbrink JM, Burke TW, McClain MT, Ginsburg GS, Woods CW, Tsalik EL. Validation of a host gene expression test for bacterial/viral discrimination in immunocompromised hosts. Clin Infect Dis 2021; 73:605-613. [PMID: 33462581 DOI: 10.1093/cid/ciab043] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host response tests remains unknown. We evaluated a host response test discriminating bacterial, viral, and non-infectious conditions in immunocompromised subjects. METHODS An 81-gene signature was measured using RT-PCR in subjects with immunocompromise (chemotherapy, solid organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects. RESULTS Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial vs. non-bacterial discrimination and 80.8% for viral vs. non-viral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (p=0.04 relative to immunocompetent subjects) and 75.4% for viral infection (p=0.30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions. CONCLUSION A host gene expression test discriminated bacterial, viral, and non-infectious etiologies at a lower overall accuracy in immunocompromised patients compared to immunocompetent patients, though this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host response strategy may offer clinically useful diagnostic information for patients with immunocompromise.
Collapse
Affiliation(s)
- Rachael E Mahle
- Duke University School of Medicine, Durham, North Carolina, USA
| | - Sunil Suchindran
- Duke Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Ricardo Henao
- Duke Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Julie M Steinbrink
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Thomas W Burke
- Duke Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Micah T McClain
- Duke Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Medical Service, Durham VA Health Care System, Durham, North Carolina, USA
| | - Geoffrey S Ginsburg
- Duke Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christopher W Woods
- Duke Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Medical Service, Durham VA Health Care System, Durham, North Carolina, USA
| | - Ephraim L Tsalik
- Duke Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Emergency Medicine Service, Durham VA Health Care System, Durham, North Carolina, USA
| |
Collapse
|
30
|
Abstract
Viral infections and their emergence continue to pose a threat to human lives. Up to the present, there are limited numbers of vaccines that effectively work and few antivirals licensed for use in clinical practice. Added to this is the increase in antiviral resistance, meaning that drugs that do work are at risk of reduced efficacy. The recent global pandemic of coronavirus 2019 has provided evidence for the risk of a preventative vaccination and effective treatment of viruses' subsequent consequences. The aim of this article is to review traditional and herbal treatments for infections, specifically addressing gastrointestinal and respiratory viral infections.
Collapse
|
31
|
Differential Markers of Bacterial and Viral Infections in Children for Point-of-Care Testing. Trends Mol Med 2020; 26:1118-1132. [PMID: 33008730 PMCID: PMC7522093 DOI: 10.1016/j.molmed.2020.09.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/22/2020] [Accepted: 09/02/2020] [Indexed: 02/08/2023]
Abstract
Children suffering from infectious diseases, both bacterial and viral, are often treated with empirical antibiotics. Keeping in mind both the menace of microorganisms and antibiotic toxicity, it is imperative to develop point-of-care testing (POCT) to discriminate bacterial from viral infections, and to define indications for antibiotic treatment. This article reviews potential protein biomarkers and host-derived gene expression signatures for differentiating between bacterial and viral infections in children, and focuses on emerging multiplex POCT devices for the simultaneous detection of sets of protein biomarkers or streamlined gene expression signatures that may provide rapid and cost-effective pathogen-discriminating tools. Bacteria and viruses activate or inhibit different signaling pathways in the cells they infect, and further give rise to different host transcriptional signatures as well as to unique protein biomarkers. Many of the newly evaluated protein biomarkers, especially in combination, have better discriminative value for distinguishing between bacterial and viral infections than the biomarkers that are currently used for examining infections in children. The transcriptomes of children undergo remarkable changes when they are infected by different types of bacteria and viruses. Approaches based on host-derived DNA/RNA signatures can accurately discriminate bacterial from viral infections. Emerging multiplex POCT techniques allow simultaneous testing of protein- or gene-based biomarkers in an outpatient setting.
Collapse
|
32
|
Corbi SCT, de Vasconcellos JF, Bastos AS, Bussaneli DG, da Silva BR, Santos RA, Takahashi CS, de S Rocha C, Carvalho BDS, Maurer-Morelli CV, Orrico SRP, Barros SP, Scarel-Caminaga RM. Circulating lymphocytes and monocytes transcriptomic analysis of patients with type 2 diabetes mellitus, dyslipidemia and periodontitis. Sci Rep 2020; 10:8145. [PMID: 32424199 PMCID: PMC7235087 DOI: 10.1038/s41598-020-65042-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 04/21/2020] [Indexed: 02/06/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM), dyslipidemia and periodontitis are frequently associated pathologies; however, there are no studies showing the peripheral blood transcript profile of these combined diseases. Here we identified the differentially expressed genes (DEGs) of circulating lymphocytes and monocytes to reveal potential biomarkers that may be used as molecular targets for future diagnosis of each combination of these pathologies (compared to healthy patients) and give insights into the underlying molecular mechanisms of these diseases. Study participants (n = 150) were divided into groups: (H) systemically and periodontal healthy (control group); (P) with periodontitis, but systemically healthy; (DL-P) with dyslipidemia and periodontitis; (T2DMwell-DL-P) well-controlled type 2 diabetes mellitus with dyslipidemia and periodontitis; and (T2DMpoorly-DL-P) poorly-controlled type 2 diabetes mellitus with dyslipidemia and periodontitis. We preprocessed the microarray data using the Robust Multichip Average (RMA) strategy, followed by the RankProd method to identify candidates for DEGs. Furthermore, we performed functional enrichment analysis using Ingenuity Pathway Analysis and Gene Set Enrichment Analysis. DEGs were submitted to pairwise comparisons, and selected DEGs were validated by quantitative polymerase chain reaction. Validated DEGs verified from T2DMpoorly-DL-P versus H were: TGFB1I1, VNN1, HLADRB4 and CXCL8; T2DMwell-DL-P versus H: FN1, BPTF and PDE3B; DL-P versus H: DAB2, CD47 and HLADRB4; P versus H: IGHDL-P, ITGB2 and HLADRB4. In conclusion, we identified that circulating lymphocytes and monocytes of individuals simultaneously affected by T2DM, dyslipidemia and periodontitis, showed an altered molecular profile mainly associated to inflammatory response, immune cell trafficking, and infectious disease pathways. Altogether, these results shed light on novel potential targets for future diagnosis, monitoring or development of targeted therapies for patients sharing these conditions.
Collapse
Affiliation(s)
- Sâmia C T Corbi
- Department of Diagnosis and Surgery, School of Dentistry at Araraquara, UNESP- São Paulo State University, Araraquara, 14801385, SP, Brazil
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry at Araraquara, UNESP- São Paulo State University, Araraquara, 14801385, SP, Brazil
| | - Jaira F de Vasconcellos
- Molecular Genomics and Therapeutics Section, Genetics of Development and Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 10 Center Drive, Building 10, Room 9D11, Bethesda, MD, 20892, USA
- Department of Surgery, Uniformed Services University of the Health Sciences and Henry Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Alliny S Bastos
- Department of Diagnosis and Surgery, School of Dentistry at Araraquara, UNESP- São Paulo State University, Araraquara, 14801385, SP, Brazil
| | - Diego Girotto Bussaneli
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry at Araraquara, UNESP- São Paulo State University, Araraquara, 14801385, SP, Brazil
| | - Bárbara Roque da Silva
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry at Araraquara, UNESP- São Paulo State University, Araraquara, 14801385, SP, Brazil
| | - Raquel Alves Santos
- Postgraduate Program in Sciences of the University of Franca, Franca, 14404600, SP, Brazil
| | - Catarina S Takahashi
- Department of Genetics, Faculty of Medicine of Ribeirão Preto, USP - University of São Paulo, Ribeirão Preto, 14049900, SP, Brazil
- Department of Biology, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, USP -University of São Paulo, Ribeirão Preto, 14049900, SP, Brazil
| | - Cristiane de S Rocha
- Department of Medical Genetics and Medicine Genomics, University of Campinas - UNICAMP, Campinas, 13083-887, SP, Brazil
| | - Benilton de Sá Carvalho
- Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, 13083-859, São Paulo, Brazil
| | - Cláudia V Maurer-Morelli
- Department of Medical Genetics and Medicine Genomics, University of Campinas - UNICAMP, Campinas, 13083-887, SP, Brazil
| | - Silvana R P Orrico
- Department of Diagnosis and Surgery, School of Dentistry at Araraquara, UNESP- São Paulo State University, Araraquara, 14801385, SP, Brazil
- Advanced Research Center in Medicine, Union of the Colleges of the Great Lakes (UNILAGO), São José do Rio Preto, SP, 15030-070, Brazil
| | - Silvana P Barros
- Department of Periodontology, University of North Carolina at Chapel Hill - UNC, School of Dentistry, Chapel Hill, NC, USA
| | - Raquel M Scarel-Caminaga
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry at Araraquara, UNESP- São Paulo State University, Araraquara, 14801385, SP, Brazil.
| |
Collapse
|
33
|
|
34
|
Barral-Arca R, Gómez-Carballa A, Cebey-López M, Currás-Tuala MJ, Pischedda S, Viz-Lasheras S, Bello X, Martinón-Torres F, Salas A. RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans. Int J Mol Sci 2020; 21:ijms21082748. [PMID: 32326627 PMCID: PMC7215422 DOI: 10.3390/ijms21082748] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/03/2020] [Accepted: 04/11/2020] [Indexed: 12/15/2022] Open
Abstract
There is a growing interest in unraveling gene expression mechanisms leading to viral host invasion and infection progression. Current findings reveal that long non-coding RNAs (lncRNAs) are implicated in the regulation of the immune system by influencing gene expression through a wide range of mechanisms. By mining whole-transcriptome shotgun sequencing (RNA-seq) data using machine learning approaches, we detected two lncRNAs (ENSG00000254680 and ENSG00000273149) that are downregulated in a wide range of viral infections and different cell types, including blood monocluclear cells, umbilical vein endothelial cells, and dermal fibroblasts. The efficiency of these two lncRNAs was positively validated in different viral phenotypic scenarios. These two lncRNAs showed a strong downregulation in virus-infected patients when compared to healthy control transcriptomes, indicating that these biomarkers are promising targets for infection diagnosis. To the best of our knowledge, this is the very first study using host lncRNAs biomarkers for the diagnosis of human viral infections.
Collapse
Affiliation(s)
- Ruth Barral-Arca
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
| | - Alberto Gómez-Carballa
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
| | - Miriam Cebey-López
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
| | - María José Currás-Tuala
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
| | - Sara Pischedda
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
| | - Sandra Viz-Lasheras
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
| | - Xabier Bello
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
| | - Federico Martinón-Torres
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela (SERGAS), 15706 Galicia, Spain
| | - Antonio Salas
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; (R.B.-A.); (A.G.-C.); (M.C.-L.); (M.J.C.-T.); (S.P.); (S.V.-L.); (X.B.)
- GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, Spain
- Genetics, Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago (IDIS) and Universidad de Santiago de Compostela (USC), 15706 Galicia, Spain;
- Correspondence:
| |
Collapse
|
35
|
Carugati M, Morlacchi LC, Peri AM, Alagna L, Rossetti V, Bandera A, Gori A, Blasi F. Challenges in the Diagnosis and Management of Bacterial Lung Infections in Solid Organ Recipients: A Narrative Review. Int J Mol Sci 2020; 21:E1221. [PMID: 32059371 PMCID: PMC7072844 DOI: 10.3390/ijms21041221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/31/2020] [Accepted: 02/07/2020] [Indexed: 12/11/2022] Open
Abstract
Respiratory infections pose a significant threat to the success of solid organ transplantation, and the diagnosis and management of these infections are challenging. The current narrative review addressed some of these challenges, based on evidence from the literature published in the last 20 years. Specifically, we focused our attention on (i) the obstacles to an etiologic diagnosis of respiratory infections among solid organ transplant recipients, (ii) the management of bacterial respiratory infections in an era characterized by increased antimicrobial resistance, and (iii) the development of antimicrobial stewardship programs dedicated to solid organ transplant recipients.
Collapse
Affiliation(s)
- Manuela Carugati
- Internal Medicine Department, Division of Infectious Diseases, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (A.M.P.); (L.A.); (A.B.); (A.G.)
- Division of Infectious Diseases and International Health, Duke University, Durham, NC 27710, USA
| | - Letizia Corinna Morlacchi
- Internal Medicine Department, Respiratory Unit and Adult Cystic Fibrosis Center, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (L.C.M.); (V.R.); (F.B.)
| | - Anna Maria Peri
- Internal Medicine Department, Division of Infectious Diseases, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (A.M.P.); (L.A.); (A.B.); (A.G.)
| | - Laura Alagna
- Internal Medicine Department, Division of Infectious Diseases, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (A.M.P.); (L.A.); (A.B.); (A.G.)
| | - Valeria Rossetti
- Internal Medicine Department, Respiratory Unit and Adult Cystic Fibrosis Center, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (L.C.M.); (V.R.); (F.B.)
| | - Alessandra Bandera
- Internal Medicine Department, Division of Infectious Diseases, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (A.M.P.); (L.A.); (A.B.); (A.G.)
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milano, Italy
| | - Andrea Gori
- Internal Medicine Department, Division of Infectious Diseases, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (A.M.P.); (L.A.); (A.B.); (A.G.)
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milano, Italy
- Centre for Multidisciplinary Research in Health Science, 20122 Milano, Italy
| | - Francesco Blasi
- Internal Medicine Department, Respiratory Unit and Adult Cystic Fibrosis Center, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, 20122 Milano, Italy; (L.C.M.); (V.R.); (F.B.)
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milano, Italy
| | | |
Collapse
|
36
|
Torres A, Loeches IM, Sligl W, Lee N. Severe flu management: a point of view. Intensive Care Med 2020; 46:153-162. [PMID: 31912206 PMCID: PMC7095473 DOI: 10.1007/s00134-019-05868-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 11/13/2019] [Indexed: 12/12/2022]
Abstract
Annual flu seasons are typically characterized by changes in types and subtypes of influenza, with variations in terms of severity. Despite remarkable improvements in the prevention and management of patients with suspected or laboratory-confirmed diagnosis of influenza, annual seasonal influenza continues to be associated with a high morbidity and mortality. Admission to the intensive care unit is required for patients with severe forms of seasonal influenza infection, with primary pneumonia being present in most of the cases. This review summarizes the most recent knowledge on the diagnosis and treatment strategies in critically ill patients with influenza, focused on diagnostic testing methods, antiviral therapy, use of corticosteroids, antibacterial and antifungal therapy, and supportive measures. The review focuses on diagnostic testing methods, antiviral therapy, use of corticosteroids, antibacterial and antifungal therapy, supportive measures and relevant existing evidence, in order to provide the non-expert clinician a useful overview. An enhanced understanding of current diagnostic and treatment aspects of influenza infection can contribute to improve outcomes and reduce mortality among ICU patients with influenza.
Collapse
Affiliation(s)
- Antoni Torres
- Service of Pneumology, Hospital Clinic of Barcelona, University of Barcelona, Institut d'Investigació August Pi i Sunyer (IDIBAPS) and Centro de Investigación Biomédica en Red, Enfermedades Respiratorias (CIBERES), C/Villarroel 170, 08036, Barcelona, Spain.
| | - Ignacio-Martin- Loeches
- Service of Pneumology, Hospital Clinic of Barcelona, University of Barcelona, Institut d'Investigació August Pi i Sunyer (IDIBAPS) and Centro de Investigación Biomédica en Red, Enfermedades Respiratorias (CIBERES), C/Villarroel 170, 08036, Barcelona, Spain
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St. James's Hospital, Dublin, Ireland
| | - Wendy Sligl
- Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, Canada
- Department of Critical Care Medicine, University of Alberta, Edmonton, Canada
| | - Nelson Lee
- Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, Canada
| |
Collapse
|
37
|
Yu J, Peterson DR, Baran AM, Bhattacharya S, Wylie TN, Falsey AR, Mariani TJ, Storch GA. Host Gene Expression in Nose and Blood for the Diagnosis of Viral Respiratory Infection. J Infect Dis 2020; 219:1151-1161. [PMID: 30339221 DOI: 10.1093/infdis/jiy608] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 10/15/2018] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Recently there has been a growing interest in the potential for host transcriptomic analysis to augment the diagnosis of infectious diseases. METHODS We compared nasal and blood samples for evaluation of the host transcriptomic response in children with acute respiratory syncytial virus (RSV) infection, symptomatic non-RSV respiratory virus infection, asymptomatic rhinovirus infection, and virus-negative asymptomatic controls. We used nested leave-one-pair-out cross-validation and supervised principal components analysis to define small sets of genes whose expression patterns accurately classified subjects. We validated gene classification scores using an external data set. RESULTS Despite lower quality of nasal RNA, the number of genes detected by microarray in each sample type was equivalent. Nasal gene expression signal derived mainly from epithelial cells but also included a variable leukocyte contribution. The number of genes with increased expression in virus-infected children was comparable in nasal and blood samples, while nasal samples also had decreased expression of many genes associated with ciliary function and assembly. Nasal gene expression signatures were as good or better for discriminating between symptomatic, asymptomatic, and uninfected children. CONCLSUSIONS Our results support the use of nasal samples to augment pathogen-based tests to diagnose viral respiratory infection.
Collapse
Affiliation(s)
- Jinsheng Yu
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
| | - Derick R Peterson
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine, New York
| | - Andrea M Baran
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine, New York
| | - Soumyaroop Bhattacharya
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester School of Medicine, New York
| | - Todd N Wylie
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
| | - Ann R Falsey
- Department of Medicine, University of Rochester School of Medicine, New York
| | - Thomas J Mariani
- Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester School of Medicine, New York
| | - Gregory A Storch
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri
| |
Collapse
|
38
|
Lydon EC, Henao R, Burke TW, Aydin M, Nicholson BP, Glickman SW, Fowler VG, Quackenbush EB, Cairns CB, Kingsmore SF, Jaehne AK, Rivers EP, Langley RJ, Petzold E, Ko ER, McClain MT, Ginsburg GS, Woods CW, Tsalik EL. Validation of a host response test to distinguish bacterial and viral respiratory infection. EBioMedicine 2019; 48:453-461. [PMID: 31631046 PMCID: PMC6838360 DOI: 10.1016/j.ebiom.2019.09.040] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Distinguishing bacterial and viral respiratory infections is challenging. Novel diagnostics based on differential host gene expression patterns are promising but have not been translated to a clinical platform nor extensively tested. Here, we validate a microarray-derived host response signature and explore performance in microbiology-negative and coinfection cases. METHODS Subjects with acute respiratory illness were enrolled in participating emergency departments. Reference standard was an adjudicated diagnosis of bacterial infection, viral infection, both, or neither. An 87-transcript signature for distinguishing bacterial, viral, and noninfectious illness was measured from peripheral blood using RT-PCR. Performance characteristics were evaluated in subjects with confirmed bacterial, viral, or noninfectious illness. Subjects with bacterial-viral coinfection and microbiologically-negative suspected bacterial infection were also evaluated. Performance was compared to procalcitonin. FINDINGS 151 subjects with microbiologically confirmed, single-etiology illness were tested, yielding AUROCs 0•85-0•89 for bacterial, viral, and noninfectious illness. Accuracy was similar to procalcitonin (88% vs 83%, p = 0•23) for bacterial vs. non-bacterial infection. Whereas procalcitonin cannot distinguish viral from non-infectious illness, the RT-PCR test had 81% accuracy in making this determination. Bacterial-viral coinfection was subdivided. Among 19 subjects with bacterial superinfection, the RT-PCR test identified 95% as bacterial, compared to 68% with procalcitonin (p = 0•13). Among 12 subjects with bacterial infection superimposed on chronic viral infection, the RT-PCR test identified 83% as bacterial, identical to procalcitonin. 39 subjects had suspected bacterial infection; the RT-PCR test identified bacterial infection more frequently than procalcitonin (82% vs 64%, p = 0•02). INTERPRETATION The RT-PCR test offered similar diagnostic performance to procalcitonin in some subgroups but offered better discrimination in others such as viral vs. non-infectious illness and bacterial/viral coinfection. Gene expression-based tests could impact decision-making for acute respiratory illness as well as a growing number of other infectious and non-infectious diseases.
Collapse
Affiliation(s)
- Emily C Lydon
- Duke University School of Medicine, Durham, NC, USA; Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA
| | - Ricardo Henao
- Duke University Department of Biostatistics and Informatics, Durham, NC, USA
| | - Thomas W Burke
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA
| | - Mert Aydin
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA
| | - Bradly P Nicholson
- Institute of Medical Research, Durham Veterans Affairs Medical Center, Durham, NC, USA
| | - Seth W Glickman
- University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Vance G Fowler
- Duke University Department of Medicine, Durham, NC, USA; Duke Clinical Research Institute, Durham, NC, USA
| | | | - Charles B Cairns
- University of North Carolina Medical Center, Chapel Hill, NC, USA; United Arab Emirates University, Al Ain, UAE
| | | | | | | | - Raymond J Langley
- University of South Alabama Health University Hospital, Mobile, AL, USA
| | - Elizabeth Petzold
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA
| | - Emily R Ko
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Department of Hospital Medicine, Duke Regional Hospital, Durham, NC 27705, USA
| | - Micah T McClain
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Geoffrey S Ginsburg
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA
| | - Christopher W Woods
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USA.
| | - Ephraim L Tsalik
- Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USA.
| |
Collapse
|
39
|
Gómez-Carballa A, Cebey-López M, Pardo-Seco J, Barral-Arca R, Rivero-Calle I, Pischedda S, Currás-Tuala MJ, Gómez-Rial J, Barros F, Martinón-Torres F, Salas A. A qPCR expression assay of IFI44L gene differentiates viral from bacterial infections in febrile children. Sci Rep 2019; 9:11780. [PMID: 31409879 PMCID: PMC6692396 DOI: 10.1038/s41598-019-48162-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 07/16/2019] [Indexed: 11/29/2022] Open
Abstract
The diagnosis of bacterial infections in hospital settings is currently performed using bacterial culture from sterile site, but they are lengthy and limited. Transcriptomic biomarkers are becoming promising tools for diagnosis with potential applicability in clinical settings. We evaluated a RT-qPCR assay for a 2-transcript host expression signature (FAM89A and IFI44L genes) inferred from microarray data that allow to differentiate between viral and bacterial infection in febrile children. This assay was able to discriminate viral from bacterial infections (P-value = 1.04 × 10-4; AUC = 92.2%; sensitivity = 90.9%; specificity = 85.7%) and showed very high reproducibility regardless of the reference gene(s) used to normalize the data. Unexpectedly, the monogenic IFI44L expression signature yielded better results than those obtained from the 2-transcript test (P-value = 3.59 × 10-5; AUC = 94.1%; sensitivity = 90.9%; specificity = 92.8%). We validated this IFI44L signature in previously published microarray and whole-transcriptome data from patients affected by different types of viral and bacterial infections, confirming that this gene alone differentiates between both groups, thus saving time, effort, and costs. Herein, we demonstrate that host expression microarray data can be successfully translated into a fast, highly accurate and relatively inexpensive in vitro assay that could be implemented in the clinical routine.
Collapse
Affiliation(s)
- Alberto Gómez-Carballa
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain.
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain.
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain.
| | - Miriam Cebey-López
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - Jacobo Pardo-Seco
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain
| | - Ruth Barral-Arca
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - Irene Rivero-Calle
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - Sara Pischedda
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - María José Currás-Tuala
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - José Gómez-Rial
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - Francisco Barros
- Unidad de Medicina Molecular, Fundación Pública Galega de Medicina Xenómica, CIBERER, Santiago de Compostela, Spain
| | - Federico Martinón-Torres
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - Antonio Salas
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain
| |
Collapse
|
40
|
Host-Based Diagnostics for Acute Respiratory Infections. Clin Ther 2019; 41:1923-1938. [PMID: 31353133 DOI: 10.1016/j.clinthera.2019.06.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/24/2019] [Accepted: 06/24/2019] [Indexed: 02/07/2023]
Abstract
PURPOSE The inappropriate use of antimicrobials, especially in acute respiratory infections (ARIs), is largely driven by difficulty distinguishing bacterial, viral, and noninfectious etiologies of illness. A new frontier in infectious disease diagnostics looks to the host response for disease classification. This article examines how host response-based diagnostics for ARIs are being used in clinical practice, as well as new developments in the research pipeline. METHODS A limited search was conducted of the relevant literature, with emphasis placed on literature published in the last 5 years (2014-2019). FINDINGS Advances are being made in all areas of host response-based diagnostics for ARIs. Specifically, there has been significant progress made in single protein biomarkers, as well as in various "omics" fields (including proteomics, metabolomics, and transcriptomics) and wearable technologies. There are many potential applications of a host response-based approach; a few key examples include the ability to discriminate bacterial and viral disease, presymptomatic diagnosis of infection, and pathogen-specific host response diagnostics, including modeling disease progression. IMPLICATIONS As biomarker measurement technologies continue to improve, host response-based diagnostics will increasingly be translated to clinically available platforms that can generate a holistic characterization of an individual's health. This knowledge, in the hands of both patient and provider, can improve care for the individual patient and help fight rising rates of antibiotic resistance.
Collapse
|
41
|
Pinu FR, Goldansaz SA, Jaine J. Translational Metabolomics: Current Challenges and Future Opportunities. Metabolites 2019; 9:E108. [PMID: 31174372 PMCID: PMC6631405 DOI: 10.3390/metabo9060108] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/04/2019] [Accepted: 06/04/2019] [Indexed: 02/06/2023] Open
Abstract
Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.
Collapse
Affiliation(s)
- Farhana R Pinu
- The New Zealand Institute for Plant and Food Research, Private Bag 92169, Auckland 1142, New Zealand.
| | - Seyed Ali Goldansaz
- Department of Agriculture, Food and Nutritional Sciences, University of Alberta, Edmonton, AB T6G 2P5, Canada.
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
| | - Jacob Jaine
- Analytica Laboratories Ltd., Ruakura Research Centre, Hamilton 3216, New Zealand.
| |
Collapse
|
42
|
Walter JM, Ren Z, Yacoub T, Reyfman PA, Shah RD, Abdala-Valencia H, Nam K, Morgan VK, Anekalla KR, Joshi N, McQuattie-Pimentel AC, Chen CI, Chi M, Han S, Gonzalez-Gonzalez FJ, Soberanes S, Aillon RP, Watanabe S, Williams KJN, Lu Z, Paonessa J, Hountras P, Breganio M, Borkowski N, Donnelly HK, Allen JP, Amaral LA, Bharat A, Misharin AV, Bagheri N, Hauser AR, Budinger GRS, Wunderink RG. Multidimensional Assessment of the Host Response in Mechanically Ventilated Patients with Suspected Pneumonia. Am J Respir Crit Care Med 2019; 199:1225-1237. [PMID: 30398927 PMCID: PMC6519857 DOI: 10.1164/rccm.201804-0650oc] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 11/02/2018] [Indexed: 12/14/2022] Open
Abstract
Rationale: The identification of informative elements of the host response to infection may improve the diagnosis and management of bacterial pneumonia. Objectives: To determine whether the absence of alveolar neutrophilia can exclude bacterial pneumonia in critically ill patients with suspected infection and to test whether signatures of bacterial pneumonia can be identified in the alveolar macrophage transcriptome. Methods: We determined the test characteristics of alveolar neutrophilia for the diagnosis of bacterial pneumonia in three cohorts of mechanically ventilated patients. In one cohort, we also isolated macrophages from alveolar lavage fluid and used the transcriptome to identify signatures of bacterial pneumonia. Finally, we developed a humanized mouse model of Pseudomonas aeruginosa pneumonia to determine if pathogen-specific signatures can be identified in human alveolar macrophages. Measurements and Main Results: An alveolar neutrophil percentage less than 50% had a negative predictive value of greater than 90% for bacterial pneumonia in both the retrospective (n = 851) and validation cohorts (n = 76 and n = 79). A transcriptional signature of bacterial pneumonia was present in both resident and recruited macrophages. Gene signatures from both cell types identified patients with bacterial pneumonia with test characteristics similar to alveolar neutrophilia. Conclusions: The absence of alveolar neutrophilia has a high negative predictive value for bacterial pneumonia in critically ill patients with suspected infection. Macrophages can be isolated from alveolar lavage fluid obtained during routine care and used for RNA-Seq analysis. This novel approach may facilitate a longitudinal and multidimensional assessment of the host response to bacterial pneumonia.
Collapse
Affiliation(s)
- James M. Walter
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Ziyou Ren
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Tyrone Yacoub
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois
| | - Paul A. Reyfman
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Raj D. Shah
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | | | - Kiwon Nam
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Vince K. Morgan
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Kishore R. Anekalla
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Nikita Joshi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | | | - Ching-I Chen
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Monica Chi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - SeungHye Han
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | | | - Saul Soberanes
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Raul P. Aillon
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Satoshi Watanabe
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | | | - Ziyan Lu
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Joseph Paonessa
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Peter Hountras
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Madonna Breganio
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Nicole Borkowski
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Helen K. Donnelly
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, and
| | - Jonathan P. Allen
- Department of Microbiology and Immunology, Northwestern University, Chicago, Illinois; and
| | - Luis A. Amaral
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois
| | - Ankit Bharat
- Division of Thoracic Surgery, Department of Surgery, Feinberg School of Medicine, and
| | | | - Neda Bagheri
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois
| | - Alan R. Hauser
- Department of Microbiology and Immunology, Northwestern University, Chicago, Illinois; and
| | | | | |
Collapse
|
43
|
Lin GL, McGinley JP, Drysdale SB, Pollard AJ. Epidemiology and Immune Pathogenesis of Viral Sepsis. Front Immunol 2018; 9:2147. [PMID: 30319615 PMCID: PMC6170629 DOI: 10.3389/fimmu.2018.02147] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 08/30/2018] [Indexed: 12/11/2022] Open
Abstract
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can be caused by a broad range of pathogens; however, bacterial infections represent the majority of sepsis cases. Up to 42% of sepsis presentations are culture negative, suggesting a non-bacterial cause. Despite this, diagnosis of viral sepsis remains very rare. Almost any virus can cause sepsis in vulnerable patients (e.g., neonates, infants, and other immunosuppressed groups). The prevalence of viral sepsis is not known, nor is there enough information to make an accurate estimate. The initial standard of care for all cases of sepsis, even those that are subsequently proven to be culture negative, is the immediate use of broad-spectrum antibiotics. In the absence of definite diagnostic criteria for viral sepsis, or at least to exclude bacterial sepsis, this inevitably leads to unnecessary antimicrobial use, with associated consequences for antimicrobial resistance, effects on the host microbiome and excess healthcare costs. It is important to understand non-bacterial causes of sepsis so that inappropriate treatment can be minimised, and appropriate treatments can be developed to improve outcomes. In this review, we summarise what is known about viral sepsis, its most common causes, and how the immune responses to severe viral infections can contribute to sepsis. We also discuss strategies to improve our understanding of viral sepsis, and ways we can integrate this new information into effective treatment.
Collapse
Affiliation(s)
- Gu-Lung Lin
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,National Institute for Health Research, Oxford Biomedical Research Centre, Oxford, United Kingdom
| | - Joseph P McGinley
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,National Institute for Health Research, Oxford Biomedical Research Centre, Oxford, United Kingdom
| | - Simon B Drysdale
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,National Institute for Health Research, Oxford Biomedical Research Centre, Oxford, United Kingdom.,Department of Paediatrics, St George's University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Andrew J Pollard
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,National Institute for Health Research, Oxford Biomedical Research Centre, Oxford, United Kingdom
| |
Collapse
|
44
|
Schlaberg R, Barrett A, Edes K, Graves M, Paul L, Rychert J, Lopansri BK, Leung DT. Fecal Host Transcriptomics for Non-Invasive Human Mucosal Immune Profiling: Proof of Concept in Clostridium Difficile Infection. Pathog Immun 2018; 3:164-180. [PMID: 30283823 PMCID: PMC6166656 DOI: 10.20411/pai.v3i2.250] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background: Host factors play an important role in pathogenesis and disease outcome in Clostridium difficile infection (CDI), and characterization of these responses could uncover potential host biomarkers to complement existing microbe-based diagnostics. Methods: We extracted RNA from fecal samples of patients with CDI and profiled human mRNA using amplicon-based next-generation sequencing (NGS). We compared the fecal host mRNA transcript expression profiles of patients with CDI to controls with non-CDI diarrhea. Results: We found that the ratio of human actin gamma 1 (ACTG1) to 16S ribosomal RNA (rRNA) was highly correlated with NGS quality as measured by percentage of reads on target. Patients with CDI could be differentiated from those with non-CDI diarrhea based on their fecal mRNA expression profiles using principal component analysis. Among the most differentially expressed genes were ones related to immune response (IL23A, IL34) and actin-cytoskeleton function (TNNT1, MYL4, SMTN, MYBPC3, all adjusted P-values < 1 x 10-3). Conclusions: In this proof-of-concept study, we used host fecal transcriptomics for non-invasive profiling of the mucosal immune response in CDI. We identified differentially expressed genes with biological plausibility based on animal and cell culture models. This demonstrates the potential of fecal transcriptomics to uncover host-based biomarkers for enteric infections.
Collapse
Affiliation(s)
- Robert Schlaberg
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah.,ARUP Laboratories, Salt Lake City, Utah
| | - Amanda Barrett
- Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, Utah
| | - Kornelia Edes
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah
| | - Michael Graves
- Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, Utah
| | - Litty Paul
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah
| | | | - Bert K Lopansri
- Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, Utah.,Division of Infectious Diseases and Clinical Epidemiology, Intermountain Medical Center, Murray, Utah
| | - Daniel T Leung
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah.,Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, Utah
| |
Collapse
|
45
|
Lydon EC, Ko ER, Tsalik EL. The host response as a tool for infectious disease diagnosis and management. Expert Rev Mol Diagn 2018; 18:723-738. [PMID: 29939801 DOI: 10.1080/14737159.2018.1493378] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks, and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, and over the past decade, there has been a particular focus on the host response. Since this article was originally published in 2015, there have been significant developments in the field of host response diagnostics, warranting this updated review. Areas Covered: This review begins by discussing developments in single biomarkers and pauci-analyte biomarker panels. It then delves into 'omics, an area where there has been truly exciting progress. Specifically, progress has been made in sepsis diagnosis and prognosis; differentiating viral, bacterial, and fungal pathogen classes; pre-symptomatic diagnosis; and understanding disease-specific diagnostic challenges in tuberculosis, Lyme disease, and Ebola. Expert Commentary: As 'omics have become faster, more precise, and less expensive, the door has been opened for academic, industry, and government efforts to develop host-based infectious disease classifiers. While there are still obstacles to overcome, the chasm separating these scientific advances from the patient's bedside is shrinking.
Collapse
Affiliation(s)
- Emily C Lydon
- a Duke University School of Medicine , Duke University , Durham , NC , USA
| | - Emily R Ko
- b Duke Center for Applied Genomics & Precision Medicine, Department of Medicine , Duke University , Durham , NC , USA.,c Duke Regional Hospital, Department of Medicine , Duke University , Durham , NC , USA
| | - Ephraim L Tsalik
- b Duke Center for Applied Genomics & Precision Medicine, Department of Medicine , Duke University , Durham , NC , USA.,d Division of Infectious Diseases & International Health, Department of Medicine , Duke University , Durham , NC , USA.,e Emergency Medicine Service , Durham Veterans Affairs Health Care System , Durham , NC , USA
| |
Collapse
|
46
|
Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM, Fraley SI. Emerging Technologies for Molecular Diagnosis of Sepsis. Clin Microbiol Rev 2018; 31:e00089-17. [PMID: 29490932 PMCID: PMC5967692 DOI: 10.1128/cmr.00089-17] [Citation(s) in RCA: 187] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.
Collapse
Affiliation(s)
- Mridu Sinha
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
| | - Julietta Jupe
- Donald Danforth Plant Science Center, Saint Louis, Missouri, USA
| | - Hannah Mack
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
| | - Todd P Coleman
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
- Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA
| | - Shelley M Lawrence
- Department of Pediatrics, Division of Neonatal-Perinatal Medicine, University of California, San Diego, San Diego, California, USA
- Rady Children's Hospital of San Diego, San Diego, California, USA
- Clinical Translational Research Institute, University of California, San Diego, San Diego, California, USA
- Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA
| | - Stephanie I Fraley
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
- Clinical Translational Research Institute, University of California, San Diego, San Diego, California, USA
- Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA
| |
Collapse
|
47
|
Krammer F, Smith GJD, Fouchier RAM, Peiris M, Kedzierska K, Doherty PC, Palese P, Shaw ML, Treanor J, Webster RG, García-Sastre A. Influenza. Nat Rev Dis Primers 2018; 4:3. [PMID: 29955068 PMCID: PMC7097467 DOI: 10.1038/s41572-018-0002-y] [Citation(s) in RCA: 841] [Impact Index Per Article: 140.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Influenza is an infectious respiratory disease that, in humans, is caused by influenza A and influenza B viruses. Typically characterized by annual seasonal epidemics, sporadic pandemic outbreaks involve influenza A virus strains of zoonotic origin. The WHO estimates that annual epidemics of influenza result in ~1 billion infections, 3–5 million cases of severe illness and 300,000–500,000 deaths. The severity of pandemic influenza depends on multiple factors, including the virulence of the pandemic virus strain and the level of pre-existing immunity. The most severe influenza pandemic, in 1918, resulted in >40 million deaths worldwide. Influenza vaccines are formulated every year to match the circulating strains, as they evolve antigenically owing to antigenic drift. Nevertheless, vaccine efficacy is not optimal and is dramatically low in the case of an antigenic mismatch between the vaccine and the circulating virus strain. Antiviral agents that target the influenza virus enzyme neuraminidase have been developed for prophylaxis and therapy. However, the use of these antivirals is still limited. Emerging approaches to combat influenza include the development of universal influenza virus vaccines that provide protection against antigenically distant influenza viruses, but these vaccines need to be tested in clinical trials to ascertain their effectiveness.
Collapse
Affiliation(s)
- Florian Krammer
- 0000 0001 0670 2351grid.59734.3cDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Gavin J. D. Smith
- 0000 0001 2180 6431grid.4280.eDuke–NUS Medical School, Singapore, Singapore ,0000 0004 1936 7961grid.26009.3dDuke Global Health Institute, Duke University, Durham, NC USA
| | - Ron A. M. Fouchier
- 000000040459992Xgrid.5645.2Department of Viroscience, Erasmus MC, Rotterdam, Netherlands
| | - Malik Peiris
- 0000000121742757grid.194645.bWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China ,0000000121742757grid.194645.bCenter of Influenza Research, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
| | - Katherine Kedzierska
- 0000 0001 2179 088Xgrid.1008.9Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria Australia
| | - Peter C. Doherty
- 0000 0001 2179 088Xgrid.1008.9Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria Australia ,0000 0001 0224 711Xgrid.240871.8Department of Immunology, St Jude Children’s Research Hospital, Memphis, TN USA
| | - Peter Palese
- 0000 0001 0670 2351grid.59734.3cDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY USA ,0000 0001 0670 2351grid.59734.3cDivision of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Megan L. Shaw
- 0000 0001 0670 2351grid.59734.3cDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - John Treanor
- 0000 0004 1936 9166grid.412750.5Division of Infectious Diseases, Department of Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY USA
| | - Robert G. Webster
- 0000 0001 0224 711Xgrid.240871.8Department of Infectious Diseases, St Jude Children’s Research Hospital, Memphis, TN USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| |
Collapse
|
48
|
Kodukula K, Faller DV, Harpp DN, Kanara I, Pernokas J, Pernokas M, Powers WR, Soukos NS, Steliou K, Moos WH. Gut Microbiota and Salivary Diagnostics: The Mouth Is Salivating to Tell Us Something. Biores Open Access 2017; 6:123-132. [PMID: 29098118 PMCID: PMC5665491 DOI: 10.1089/biores.2017.0020] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The microbiome of the human body represents a symbiosis of microbial networks spanning multiple organ systems. Bacteria predominantly represent the diversity of human microbiota, but not to be forgotten are fungi, viruses, and protists. Mounting evidence points to the fact that the "microbial signature" is host-specific and relatively stable over time. As our understanding of the human microbiome and its relationship to the health of the host increases, it is becoming clear that many and perhaps most chronic conditions have a microbial involvement. The oral and gastrointestinal tract microbiome constitutes the bulk of the overall human microbial load, and thus presents unique opportunities for advancing human health prognosis, diagnosis, and therapy development. This review is an attempt to catalog a broad diversity of recent evidence and focus it toward opportunities for prevention and treatment of debilitating illnesses.
Collapse
Affiliation(s)
- Krishna Kodukula
- Bridgewater College, Bridgewater, Virginia
- ShangPharma Innovation, Inc., South San Francisco, California
- PhenoMatriX, Inc., Natick, Massachusetts
| | - Douglas V. Faller
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Cancer Research Center, Boston University School of Medicine, Boston, Massachusetts
| | - David N. Harpp
- Department of Chemistry, McGill University, Montreal, Canada
| | | | - Julie Pernokas
- Advanced Dental Associates of New England, Woburn, Massachusetts
| | - Mark Pernokas
- Advanced Dental Associates of New England, Woburn, Massachusetts
| | - Whitney R. Powers
- Department of Health Sciences, Boston University, Boston, Massachusetts
- Department of Anatomy, Boston University School of Medicine, Boston, Massachusetts
| | - Nikolaos S. Soukos
- Dana Research Center, Department of Physics, Northeastern University, Boston, Massachusetts
| | - Kosta Steliou
- PhenoMatriX, Inc., Natick, Massachusetts
- Cancer Research Center, Boston University School of Medicine, Boston, Massachusetts
| | - Walter H. Moos
- ShangPharma Innovation, Inc., South San Francisco, California
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, San Francisco, California
| |
Collapse
|