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Ko ER, Reller ME, Tillekeratne LG, Bodinayake CK, Miller C, Burke TW, Henao R, McClain MT, Suchindran S, Nicholson B, Blatt A, Petzold E, Tsalik EL, Nagahawatte A, Devasiri V, Rubach MP, Maro VP, Lwezaula BF, Kodikara-Arachichi W, Kurukulasooriya R, De Silva AD, Clark DV, Schully KL, Madut D, Dumler JS, Kato C, Galloway R, Crump JA, Ginsburg GS, Minogue TD, Woods CW. Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance. Sci Rep 2023; 13:22554. [PMID: 38110534 PMCID: PMC10728077 DOI: 10.1038/s41598-023-49734-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 12/11/2023] [Indexed: 12/20/2023] Open
Abstract
Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.
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Affiliation(s)
- Emily R Ko
- Division of General Internal Medicine, Department of Medicine, Duke Regional Hospital, Duke University Health System, Duke University School of Medicine, 3643 N. Roxboro St., Durham, NC, 27704, USA.
| | - Megan E Reller
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - L Gayani Tillekeratne
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Champica K Bodinayake
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Cameron Miller
- Clinical Research Unit, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Thomas W Burke
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Ricardo Henao
- Department of Biostatistics and Informatics, Duke University, Durham, NC, USA
| | - Micah T McClain
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Sunil Suchindran
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | | | - Adam Blatt
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA
| | - Elizabeth Petzold
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Ephraim L Tsalik
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Danaher Diagnostics, Washington, DC, USA
| | - Ajith Nagahawatte
- Department of Microbiology, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Vasantha Devasiri
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Matthew P Rubach
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Programme in Emerging Infectious Diseases, Duke-National University of Singapore, Singapore, Singapore
- Kilimanjaro Christian Medical Center, Moshi, Tanzania
| | - Venance P Maro
- Kilimanjaro Christian Medical Center, Moshi, Tanzania
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
| | - Bingileki F Lwezaula
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
- Maswenzi Regional Referral Hospital, Moshi, Tanzania
| | | | | | - Aruna D De Silva
- General Sir John Kotelawala Defence University, Colombo, Sri Lanka
| | - Danielle V Clark
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
- Austere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Ft. Detrick, MD, USA
| | - Kevin L Schully
- Austere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Ft. Detrick, MD, USA
| | - Deng Madut
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - J Stephen Dumler
- Joint Departments of Pathology, School of Medicine, Uniformed Services University, Bethesda, MD, USA
| | - Cecilia Kato
- Centers for Disease Control and Prevention, National Center for Emerging Zoonotic Infectious Diseases, Atlanta, USA
| | - Renee Galloway
- Centers for Disease Control and Prevention, National Center for Emerging Zoonotic Infectious Diseases, Atlanta, USA
| | - John A Crump
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
- Kilimanjaro Christian Medical Center, Moshi, Tanzania
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
- Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Geoffrey S Ginsburg
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- National Institute of Health, Bethesda, MD, USA
| | - Timothy D Minogue
- Diagnostic Systems Division, USAMRIID, Fort Detrick, Frederick, MD, USA
| | - Christopher W Woods
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
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Hanson KE, Banerjee R, Doernberg SB, Evans SR, Komarow L, Satlin MJ, Schwager N, Simner PJ, Tillekeratne LG, Patel R. Priorities and Progress in Diagnostic Research by the Antibacterial Resistance Leadership Group. Clin Infect Dis 2023; 77:S314-S320. [PMID: 37843119 PMCID: PMC10578045 DOI: 10.1093/cid/ciad541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023] Open
Abstract
The advancement of infectious disease diagnostics, along with studies devoted to infections caused by gram-negative and gram-positive bacteria, is a top scientific priority of the Antibacterial Resistance Leadership Group (ARLG). Diagnostic tests for infectious diseases are rapidly evolving and improving. However, the availability of rapid tests designed to determine antibacterial resistance or susceptibility directly in clinical specimens remains limited, especially for gram-negative organisms. Additionally, the clinical impact of many new tests, including an understanding of how best to use them to inform optimal antibiotic prescribing, remains to be defined. This review summarizes the recent work of the ARLG toward addressing these unmet needs in the diagnostics field and describes future directions for clinical research aimed at curbing the threat of antibiotic-resistant bacterial infections.
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Affiliation(s)
- Kimberly E Hanson
- Division of Infectious Diseases, Department of Medicine, University of Utah, Salt Lake City, Utah, USA
- Division of Clinical Microbiology, Department of Pathology, University of Utah, Salt Lake City, Utah, USA
| | - Ritu Banerjee
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sarah B Doernberg
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Scott R Evans
- Department of Biostatistics, George Washington University, Washington, DC, USA
| | - Lauren Komarow
- George Washington University Biostatistics Center, Rockville, Maryland, USA
| | - Michael J Satlin
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Nyssa Schwager
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Patricia J Simner
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - L Gayani Tillekeratne
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Robin Patel
- Division of Clinical Microbiology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
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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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Almansa R, Herrero-Rodríguez C, Martínez-Huélamo M, Vicente-Andres MDP, Nieto-Barbero JA, Martín-Ballesteros M, Rodilla-Carvajal MDM, de la Fuente A, Ortega A, Alonso-Ramos MJ, Wacker J, Liesenfeld O, Sweeney TE, Bermejo-Martin JF, García-Ortiz L. A host transcriptomic signature for identification of respiratory viral infections in the community. Eur J Clin Invest 2021; 51:e13626. [PMID: 34120332 DOI: 10.1111/eci.13626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/22/2021] [Accepted: 05/25/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Fever-7 is a test evaluating host mRNA expression levels of IFI27, JUP, LAX, HK3, TNIP1, GPAA1 and CTSB in blood able to detect viral infections. This test has been validated mostly in hospital settings. Here we have evaluated Fever-7 to identify the presence of respiratory viral infections in a Community Health Center. METHODS A prospective study was conducted in the "Servicio de Urgencias de Atención Primaria" in Salamanca, Spain. Patients with clinical signs of respiratory infection and at least one point in the National Early Warning Score were recruited. Fever-7 mRNAs were profiled on a Nanostring nCounter® SPRINT instrument from blood collected upon patient enrolment. Viral diagnosis was performed on nasopharyngeal aspirates (NPAs) using the Biofire-RP2 panel. RESULTS A respiratory virus was detected in the NPAs of 66 of the 100 patients enrolled. Median National Early Warning Score was 7 in the group with no virus detected and 6.5 in the group with a respiratory viral infection (P > .05). The Fever-7 score yielded an overall AUC of 0.81 to predict a positive viral syndromic test. The optimal operating point for the Fever-7 score yielded a sensitivity of 82% with a specificity of 71%. Multivariate analysis showed that Fever-7 was a robust marker of viral infection independently of age, sex, major comorbidities and disease severity at presentation (OR [CI95%], 3.73 [2.14-6.51], P < .001). CONCLUSIONS Fever-7 is a promising host immune mRNA signature for the early identification of a respiratory viral infection in the community.
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Affiliation(s)
- Raquel Almansa
- Group for Biomedical Research in Sepsis (BioSepsis), Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud, Salamanca, Spain.,Hospital Universitario Río Hortega, Gerencia Regional de Salud, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carmen Herrero-Rodríguez
- Servicio de Urgencias de Atención Primaria de Salamanca (SUAP). Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain.,Unidad de Investigación en Atención Primaria de Salamanca (APISAL), Instituto de investigación Biomédica de Salamanca (IBSAL), Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain
| | - Misericordia Martínez-Huélamo
- Servicio de Urgencias de Atención Primaria de Salamanca (SUAP). Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain
| | - Maria Del Pilar Vicente-Andres
- Servicio de Urgencias de Atención Primaria de Salamanca (SUAP). Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain
| | - Jose Angel Nieto-Barbero
- Servicio de Urgencias de Atención Primaria de Salamanca (SUAP). Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain
| | - Miryam Martín-Ballesteros
- Servicio de Urgencias de Atención Primaria de Salamanca (SUAP). Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain
| | - Maria Del Mar Rodilla-Carvajal
- Servicio de Urgencias de Atención Primaria de Salamanca (SUAP). Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain
| | - Amanda de la Fuente
- Group for Biomedical Research in Sepsis (BioSepsis), Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud, Salamanca, Spain.,Hospital Universitario Río Hortega, Gerencia Regional de Salud, Valladolid, Spain
| | - Alicia Ortega
- Group for Biomedical Research in Sepsis (BioSepsis), Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud, Salamanca, Spain.,Hospital Universitario Río Hortega, Gerencia Regional de Salud, Valladolid, Spain
| | - Maria Jesus Alonso-Ramos
- Group for Biomedical Research in Sepsis (BioSepsis), Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud, Salamanca, Spain
| | | | | | | | - Jesús F Bermejo-Martin
- Group for Biomedical Research in Sepsis (BioSepsis), Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud, Salamanca, Spain.,Hospital Universitario Río Hortega, Gerencia Regional de Salud, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Luis García-Ortiz
- Unidad de Investigación en Atención Primaria de Salamanca (APISAL), Instituto de investigación Biomédica de Salamanca (IBSAL), Gerencia de Atención Primaria de Salamanca, Gerencia Regional de salud de Castilla y León (SACyL), Salamanca, Spain.,Departamento de Ciencias Biomédicas y del Diagnóstico, Universidad de Salamanca, Salamanca, Spain
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5
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Steinbrink JM, Myers RA, Hua K, Johnson MD, Seidelman JL, Tsalik EL, Henao R, Ginsburg GS, Woods CW, Alexander BD, McClain MT. The host transcriptional response to Candidemia is dominated by neutrophil activation and heme biosynthesis and supports novel diagnostic approaches. Genome Med 2021; 13:108. [PMID: 34225776 PMCID: PMC8259367 DOI: 10.1186/s13073-021-00924-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 06/11/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Candidemia is one of the most common nosocomial bloodstream infections in the United States, causing significant morbidity and mortality in hospitalized patients, but the breadth of the host response to Candida infections in human patients remains poorly defined. METHODS In order to better define the host response to Candida infection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive for Candida species and compared them to patients with other acute viral, bacterial, and non-infectious illnesses. Regularized multinomial regression was utilized to develop pathogen class-specific gene expression classifiers. RESULTS Candidemia triggers a unique, robust, and conserved transcriptomic response in human hosts with 1641 genes differentially upregulated compared to healthy controls. Many of these genes corresponded to components of the immune response to fungal infection, heavily weighted toward neutrophil activation, heme biosynthesis, and T cell signaling. We developed pathogen class-specific classifiers from these unique signals capable of identifying and differentiating candidemia, viral, or bacterial infection across a variety of hosts with a high degree of accuracy (auROC 0.98 for candidemia, 0.99 for viral and bacterial infection). This classifier was validated on two separate human cohorts (auROC 0.88 for viral infection and 0.87 for bacterial infection in one cohort; auROC 0.97 in another cohort) and an in vitro model (auROC 0.94 for fungal infection, 0.96 for bacterial, and 0.90 for viral infection). CONCLUSIONS Transcriptional analysis of circulating leukocytes in patients with acute Candida infections defines novel aspects of the breadth of the human immune response during candidemia and suggests promising diagnostic approaches for simultaneously differentiating multiple types of clinical illnesses in at-risk, acutely ill patients.
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Affiliation(s)
- Julie M Steinbrink
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA.
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
| | - Rachel A Myers
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA
| | - Kaiyuan Hua
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA
| | - Melissa D Johnson
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
| | - Jessica L Seidelman
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
| | - Ephraim L Tsalik
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA
- Emergency Medicine Service, Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Ricardo Henao
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA
| | - Geoffrey S Ginsburg
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA
| | - Christopher W Woods
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA
- Division of Infectious Diseases, Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Barbara D Alexander
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
| | - Micah T McClain
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA
- Division of Infectious Diseases, Durham Veterans Affairs Health Care System, Durham, NC, USA
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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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Steinbrink JM, Zaas AK, Betancourt M, Modliszewski JL, Corcoran DL, McClain MT. A transcriptional signature accurately identifies Aspergillus Infection across healthy and immunosuppressed states. Transl Res 2020; 219:1-12. [PMID: 32165060 PMCID: PMC7170547 DOI: 10.1016/j.trsl.2020.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/11/2020] [Accepted: 02/15/2020] [Indexed: 12/12/2022]
Abstract
Invasive aspergillosis (IA) is a major cause of critical illness in immunocompromised (IC) patients. However, current fungal tests are limited. Disease-specific gene expression patterns in circulating host cells show promise as novel diagnostics, however it is unknown whether such a 'signature' exists for IA and the effect of iatrogenic immunosuppression on any such biomarkers. Male BALB/c mice were separated into 6 experimental groups based on Aspergillus fumigatus inhalational exposure and IC status (no immunosuppression, cyclophosphamide, and corticosteroids). Mice were sacrificed 4 days postinfection. Whole blood was assayed for transcriptomic responses in peripheral white blood cells via microarray. An elastic net regularized logistic regression was employed to develop classifiers of IA based on gene expression. Aspergillus infection triggers a powerful response in non-IC hosts with 2718 genes differentially expressed between IA and controls. We generated a 146-gene classifier able to discriminate between non-IC infected and uninfected mice with an AUC of 1. However, immunosuppressive medications exhibited a confounding effect on this transcriptomic classifier. After controlling for the genomic effects of immunosuppression, we were able to generate a 187-gene classifier with an AUC of 0.92 in the absence of immunosuppression, 1 with cyclophosphamide, and 0.9 with steroids. The host transcriptomic response to IA is robust and conserved. Pharmacologic perturbation of the host immune response has powerful effects on classifier performance and must be considered when developing such novel diagnostics. When appropriately designed, host-derived peripheral blood transcriptomic responses demonstrate the ability to accurately diagnose Aspergillus infection, even in the presence of immunosuppression.
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Affiliation(s)
- Julie M Steinbrink
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, North Carolina; Division of Infectious Diseases, Duke University Medical Center, Durham, North Carolina.
| | - Aimee K Zaas
- Division of Infectious Diseases, Duke University Medical Center, Durham, North Carolina
| | - Marisol Betancourt
- Division of Infectious Diseases, Duke University Medical Center, Durham, North Carolina; Augusta University Medical Center, Augusta, Georgia
| | | | - David L Corcoran
- Duke Center for Genomic and Computational Biology, Duke University, Durham North Carolina
| | - Micah T McClain
- Center for Applied Genomics and Precision Medicine, Duke University, Durham, North Carolina; Division of Infectious Diseases, Duke University Medical Center, Durham, North Carolina; Durham VA Medical Center, Durham, North Carolina
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