tetano
Editor, Senior Moderator
Biosens Bioelectron
. 2022 Feb 17;205:114086.
doi: 10.1016/j.bios.2022.114086. Online ahead of print.
A GMR-based assay for quantification of the human response to influenza
Neeraja Ravi[SUP] 1 [/SUP], Sarah E Chang[SUP] 2 [/SUP], Luis M Franco[SUP] 3 [/SUP], Sandesh C S Nagamani[SUP] 4 [/SUP], Purvesh Khatri[SUP] 5 [/SUP], Paul J Utz[SUP] 6 [/SUP], Shan X Wang[SUP] 7 [/SUP]
Affiliations
Abstract
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.
Keywords: GMR sensors; Gene expression; Host response; Influenza; Point-of-care diagnostics.
. 2022 Feb 17;205:114086.
doi: 10.1016/j.bios.2022.114086. Online ahead of print.
A GMR-based assay for quantification of the human response to influenza
Neeraja Ravi[SUP] 1 [/SUP], Sarah E Chang[SUP] 2 [/SUP], Luis M Franco[SUP] 3 [/SUP], Sandesh C S Nagamani[SUP] 4 [/SUP], Purvesh Khatri[SUP] 5 [/SUP], Paul J Utz[SUP] 6 [/SUP], Shan X Wang[SUP] 7 [/SUP]
Affiliations
- PMID: 35192997
- DOI: 10.1016/j.bios.2022.114086
Abstract
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.
Keywords: GMR sensors; Gene expression; Host response; Influenza; Point-of-care diagnostics.