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Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature. Cell Syst 2022; 13:989-1001.e8. [PMID: 36549275 DOI: 10.1016/j.cels.2022.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/05/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.
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Tiwari A, Hobbs BD, Li J, Kho AT, Amr S, Celedón JC, Weiss ST, Hersh CP, Tantisira KG, McGeachie MJ. Blood miRNAs Are Linked to Frequent Asthma Exacerbations in Childhood Asthma and Adult COPD. Noncoding RNA 2022; 8:ncrna8020027. [PMID: 35447890 PMCID: PMC9030787 DOI: 10.3390/ncrna8020027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/23/2022] [Accepted: 03/30/2022] [Indexed: 12/23/2022] Open
Abstract
MicroRNAs have been independently associated with asthma and COPD; however, it is unclear if microRNA associations will overlap when evaluating retrospective acute exacerbations. Objective: We hypothesized that peripheral blood microRNAs would be associated with retrospective acute asthma exacerbations in a pediatric asthma cohort and that such associations may also be relevant to acute COPD exacerbations. Methods: We conducted small-RNA sequencing on 374 whole-blood samples from children with asthma ages 6-14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS) and 450 current and former adult smokers with and without COPD who participated in the COPDGene study. Measurements and Main Results: After QC, we had 351 samples and 649 microRNAs for Differential Expression (DE) analysis between the frequent (n = 183) and no or infrequent exacerbation (n = 168) groups in GACRS. Fifteen upregulated miRs had odds ratios (OR) between 1.22 and 1.59 for a doubling of miR counts, while five downregulated miRs had ORs between 0.57 and 0.8. These were assessed for generalization in COPDGene, where three of the upregulated miRs (miR-532-3p, miR-296-5p, and miR-766-3p) and two of the downregulated miRs (miR-7-5p and miR-451b) replicated. Pathway enrichment analysis showed MAPK and PI3K-Akt signaling pathways were strongly enriched for target genes of DE miRNAs and miRNAs generalizing to COPD exacerbations, as well as infection response pathways to various pathogens. Conclusion: miRs (451b; 7-5p; 532-3p; 296-5p and 766-3p) associated with both childhood asthma and adult COPD exacerbations may play a vital role in airflow obstruction and exacerbations and point to shared genomic regulatory machinery underlying exacerbations in both diseases.
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Affiliation(s)
- Anshul Tiwari
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (A.T.); (B.D.H.); (J.L.); (A.T.K.); (S.T.W.); (C.P.H.)
| | - Brian D. Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (A.T.); (B.D.H.); (J.L.); (A.T.K.); (S.T.W.); (C.P.H.)
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Jiang Li
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (A.T.); (B.D.H.); (J.L.); (A.T.K.); (S.T.W.); (C.P.H.)
| | - Alvin T. Kho
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (A.T.); (B.D.H.); (J.L.); (A.T.K.); (S.T.W.); (C.P.H.)
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Samir Amr
- Translational Genomics Core, Mass General Brigham Personalized Medicine, Cambridge, MA 02139, USA;
| | - Juan C. Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15224, USA;
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (A.T.); (B.D.H.); (J.L.); (A.T.K.); (S.T.W.); (C.P.H.)
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (A.T.); (B.D.H.); (J.L.); (A.T.K.); (S.T.W.); (C.P.H.)
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Kelan G. Tantisira
- Division of Pediatric Respiratory Medicine, Rady Children’s Hospital, University of California, San Diego, CA 92123, USA;
| | - Michael J. McGeachie
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (A.T.); (B.D.H.); (J.L.); (A.T.K.); (S.T.W.); (C.P.H.)
- Correspondence: ; Tel.: +617-525-2272; Fax: 617-731-1541
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Ko ER, Henao R, Frankey K, Petzold EA, Isner PD, Jaehne AK, Allen N, Gardner-Gray J, Hurst G, Pflaum-Carlson J, Jayaprakash N, Rivers EP, Wang H, Ugalde I, Amanullah S, Mercurio L, Chun TH, May L, Hickey RW, Lazarus JE, Gunaratne SH, Pallin DJ, Jambaulikar G, Huckins DS, Ampofo K, Jhaveri R, Jiang Y, Komarow L, Evans SR, Ginsburg GS, Tillekeratne LG, McClain MT, Burke TW, Woods CW, Tsalik EL. Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection. JAMA Netw Open 2022; 5:e227299. [PMID: 35420659 PMCID: PMC9011121 DOI: 10.1001/jamanetworkopen.2022.7299] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 02/24/2022] [Indexed: 12/24/2022] Open
Abstract
Importance Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship. Objective To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI. Design, Setting, and Participants This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc. Interventions The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection. Main Outcomes and Measures Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement. Results Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection. Conclusions and Relevance In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.
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Affiliation(s)
- Emily R. Ko
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
- Hospital Medicine, Division of General Internal Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Ricardo Henao
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
- Department of Biostatistics and Informatics, Duke University, Durham, North Carolina
- Duke Clinical Research Institute, Durham, North Carolina
| | - Katherine Frankey
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Elizabeth A. Petzold
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Pamela D. Isner
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Anja K. Jaehne
- Department of Emergency Medicine, Henry Ford Hospital System, Detroit, Michigan
| | - Nakia Allen
- Department of Pediatrics, Henry Ford Hospital System, Detroit, Michigan
| | - Jayna Gardner-Gray
- Department of Emergency Medicine, Henry Ford Hospital System, Detroit, Michigan
- Department of Medicine, Henry Ford Hospital System, Detroit, Michigan
- Division of Pulmonary and Critical Care Medicine, Henry Ford Hospital System, Detroit, Michigan
| | - Gina Hurst
- Department of Emergency Medicine, Henry Ford Hospital System, Detroit, Michigan
- Department of Medicine, Henry Ford Hospital System, Detroit, Michigan
- Division of Pulmonary and Critical Care Medicine, Henry Ford Hospital System, Detroit, Michigan
| | - Jacqueline Pflaum-Carlson
- Department of Emergency Medicine, Henry Ford Hospital System, Detroit, Michigan
- Department of Medicine, Henry Ford Hospital System, Detroit, Michigan
- Division of Pulmonary and Critical Care Medicine, Henry Ford Hospital System, Detroit, Michigan
| | - Namita Jayaprakash
- Department of Emergency Medicine, Henry Ford Hospital System, Detroit, Michigan
- Division of Pulmonary and Critical Care Medicine, Henry Ford Hospital System, Detroit, Michigan
| | - Emanuel P. Rivers
- Department of Emergency Medicine, Henry Ford Hospital System, Detroit, Michigan
- Department of Surgery, Henry Ford Hospital System, Detroit, Michigan
| | - Henry Wang
- McGovern Medical University of Texas Health, Houston
- Department of Emergency Medicine, The Ohio State University, Columbus
| | - Irma Ugalde
- McGovern Medical University of Texas Health, Houston
| | - Siraj Amanullah
- Department of Emergency Medicine, Alpert Medical School of Brown University, Hasbro Children’s Hospital, Providence, Rhode Island
- Department of Pediatrics, Alpert Medical School of Brown University, Hasbro Children’s Hospital, Providence, Rhode Island
| | - Laura Mercurio
- Department of Emergency Medicine, Alpert Medical School of Brown University, Hasbro Children’s Hospital, Providence, Rhode Island
- Department of Pediatrics, Alpert Medical School of Brown University, Hasbro Children’s Hospital, Providence, Rhode Island
| | - Thomas H. Chun
- Department of Emergency Medicine, Alpert Medical School of Brown University, Hasbro Children’s Hospital, Providence, Rhode Island
- Department of Pediatrics, Alpert Medical School of Brown University, Hasbro Children’s Hospital, Providence, Rhode Island
| | - Larissa May
- Department of Emergency Medicine, University of California, Davis
| | - Robert W. Hickey
- Division of Pediatric Emergency Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jacob E. Lazarus
- Division of Infectious Diseases, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Shauna H. Gunaratne
- Division of Infectious Diseases, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Daniel J. Pallin
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | | | - David S. Huckins
- Department of Emergency Medicine, Newton-Wellesley Hospital, Boston, Massachusetts
| | - Krow Ampofo
- Department of Pediatrics, University of Utah, Salt Lake City
| | - Ravi Jhaveri
- Department of Pediatrics, University of North Carolina at Chapel Hill
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Yunyun Jiang
- The Biostatistics Center, George Washington University, Rockville, Maryland
| | - Lauren Komarow
- The Biostatistics Center, George Washington University, Rockville, Maryland
| | - Scott R. Evans
- The Biostatistics Center, George Washington University, Rockville, Maryland
| | - Geoffrey S. Ginsburg
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
| | - L. Gayani Tillekeratne
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
- Medical Service, Durham Veterans Affairs Health Care System, Durham, North Carolina
| | - Micah T. McClain
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
- Medical Service, Durham Veterans Affairs Health Care System, Durham, North Carolina
| | - Thomas W. Burke
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Christopher W. Woods
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
- Medical Service, Durham Veterans Affairs Health Care System, Durham, North Carolina
| | - Ephraim L. Tsalik
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
- Emergency Medicine Service, Durham Veterans Affairs Health Care System, Durham, North Carolina
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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.
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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
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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.
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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
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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.
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7
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Martin MJ, Beasley R, Harrison TW. Towards a personalised treatment approach for asthma attacks. Thorax 2020; 75:1119-1129. [PMID: 32839286 DOI: 10.1136/thoraxjnl-2020-214692] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 07/02/2020] [Accepted: 07/05/2020] [Indexed: 12/16/2022]
Abstract
Asthma attacks (exacerbations) are common, accounting for over 90 000 UK hospital admissions per annum. They kill nearly 1500 people per year in the UK, have significant associated direct and indirect costs and lead to accelerated and permanent loss of lung function. The recognition of asthma as a heterogeneous condition with multiple phenotypes has revolutionised the approach to the long-term management of the condition, with greater emphasis on personalised treatment and the introduction of the treatable traits concept. In contrast asthma attacks are poorly defined and understood and our treatment approach consists of bronchodilators and systemic corticosteroids. This review aims to explore the current limitations in the description, assessment and management of asthma attacks. We will outline the risk factors for attacks, strategies to modify this risk and describe the recognised characteristics of attacks as a first step towards the development of an approach for phenotyping and personalising the treatment of these critically important events. By doing this, we hope to gradually improve asthma attack treatment and reduce the adverse effects associated with recurrent courses of corticosteroids.
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Affiliation(s)
- Matthew J Martin
- Nottingham Respiratory Research Unit, University of Nottingham, Nottingham, UK
| | - Richard Beasley
- Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Tim W Harrison
- Nottingham Respiratory Research Unit, University of Nottingham, Nottingham, UK
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Mayhew MB, Buturovic L, Luethy R, Midic U, Moore AR, Roque JA, Shaller BD, Asuni T, Rawling D, Remmel M, Choi K, Wacker J, Khatri P, Rogers AJ, Sweeney TE. A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections. Nat Commun 2020; 11:1177. [PMID: 32132525 PMCID: PMC7055276 DOI: 10.1038/s41467-020-14975-w] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 02/13/2020] [Indexed: 02/07/2023] Open
Abstract
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90–0.93) and a viral-vs-other AUROC 0.92 (95% CI 0.90–0.93). We then apply this classifier, inflammatix-bacterial-viral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77–0.93), and viral-vs.-other 0.85 (95% CI 0.76–0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83–0.99), and viral-vs.-other 0.91 (95% CI 0.82–0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission. Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.
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Affiliation(s)
- Michael B Mayhew
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA
| | | | - Roland Luethy
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA
| | - Uros Midic
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA
| | - Andrew R Moore
- Department of Medicine, Stanford University, Palo Alto, CA, 94305, USA
| | - Jonasel A Roque
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Stanford University, Palo Alto, CA, 94305, USA
| | - Brian D Shaller
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Stanford University, Palo Alto, CA, 94305, USA
| | - Tola Asuni
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Stanford University, Palo Alto, CA, 94305, USA
| | - David Rawling
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA
| | - Melissa Remmel
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA
| | - Kirindi Choi
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA
| | - James Wacker
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA
| | - Purvesh Khatri
- Institute for Immunity, Transplantation and Infections, Stanford University, Palo Alto, CA, 94305, USA.,Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Palo Alto, CA, 94305, USA
| | - Angela J Rogers
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Stanford University, Palo Alto, CA, 94305, USA
| | - Timothy E Sweeney
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA, 94010, USA.
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