4.8 Article

A GMR-based assay for quantification of the human response to influenza

Journal

BIOSENSORS & BIOELECTRONICS
Volume 205, Issue -, Pages -

Publisher

ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.bios.2022.114086

Keywords

GMR sensors; Influenza; Host response; Gene expression; Point-of-care diagnostics

Funding

  1. Stanford Graduate Fellowship
  2. Advancing Science in America (ARCS) Fellowship
  3. National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH) [R01 AI125197]
  4. Autoimmunity Center of Excellence of the NIH [U19 AI110491]
  5. National Cancer Institute [R01 CA257843, U54 CA199075]
  6. NIH [R01 AI125197-04, R21 AI59578-01]
  7. Henry Gustav Floren Trust
  8. Bill & Melinda Gates Foundation [OPP1113682]
  9. National Institute of Arthritis and Musculoskeletal and Skin Diseases
  10. National Institute of Allergy and Infectious Diseases, at the U.S. National Institutes of Health
  11. Clinical Translational Core at Baylor College of Medicine
  12. IDDRC grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development [U54 HD083092]

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Detecting and quantifying the host transcriptional response to influenza virus infection can serve as a real-time diagnostic tool for clinical management. We have developed a novel assay based on the influenza metasignature (IMS) using GMR sensors, which can classify influenza infection based on transcript levels. We also validated the accuracy of a single biomarker in stratifying patients with influenza.
Detecting and quantifying the host transcriptional response to influenza virus infection can serve as a real-time diagnostic tool for clinical management. We have employed the multiplexing capabilities of GMR sensors to develop a novel assay based on the influenza metasignature (IMS), which can classify influenza infection based on transcript levels. We show that the assay can reliably detect ten IMS transcripts and distinguish subjects with naturally acquired influenza infection from those with other symptomatic viral infections (AUC 0.93, 95% CI: 0.82-1.00). Separately, we validated that the gene IFI27, not included in the IMS panel, has very high single-biomarker accuracy (AUC 0.95, 95% CI: 0.90-0.99) in stratifying patients with influenza. We demonstrate that a portable GMR biosensor can be used as a tool to diagnose influenza infection by measuring the host response, simultaneously highlighting the power of immune system metrics and advancing the field of gene expression-based diagnostics.

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