IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study.
Shojaei, M
Shamshirian, A
Monkman, J
Grice, L
Tran, M
Tan, CW
Teo, SM
Rodrigues Rossi, G
McCulloch, TR
Nalos, M
Raei, M
Razavi, A
Ghasemian, R
Gheibi, M
Roozbeh, F
Sly, PD
Spann, KM
Chew, KY
Zhu, Y
Xia, Y
Wells, TJ
Senegaglia, AC
Kuniyoshi, CL
Franck, CL
Dos Santos, AFR
de Noronha, L
Motamen, S
Valadan, R
Amjadi, O
Gogna, R
Madan, E
Alizadeh-Navaei, R
Lamperti, L
Zuñiga, F
Nova-Lamperti, E
Labarca, G
Knippenberg, B
Herwanto, V
Wang, Y
Phu, A
Chew, T
Kwan, T
Kim, K
Teoh, S
Pelaia, TM
Kuan, WS
Jee, Y
Iredell, J
O'Byrne, K
Fraser, JF
Davis, MJ
Belz, GT
Warkiani, ME
Gallo, CS
Souza-Fonseca-Guimaraes, F
Nguyen, Q
Mclean, A
Kulasinghe, A
Short, KR
Tang, B
- Publisher:
- Frontiers Media SA
- Publication Type:
- Journal Article
- Citation:
- Front Immunol, 2022, 13, pp. 1060438
- Issue Date:
- 2022
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Full metadata record
| Field | Value | Language |
|---|---|---|
| dc.contributor.author | Shojaei, M | |
| dc.contributor.author | Shamshirian, A | |
| dc.contributor.author | Monkman, J | |
| dc.contributor.author | Grice, L | |
| dc.contributor.author | Tran, M | |
| dc.contributor.author | Tan, CW | |
| dc.contributor.author | Teo, SM | |
| dc.contributor.author | Rodrigues Rossi, G | |
| dc.contributor.author | McCulloch, TR | |
| dc.contributor.author | Nalos, M | |
| dc.contributor.author | Raei, M | |
| dc.contributor.author | Razavi, A | |
| dc.contributor.author | Ghasemian, R | |
| dc.contributor.author | Gheibi, M | |
| dc.contributor.author | Roozbeh, F | |
| dc.contributor.author | Sly, PD | |
| dc.contributor.author | Spann, KM | |
| dc.contributor.author | Chew, KY | |
| dc.contributor.author | Zhu, Y | |
| dc.contributor.author | Xia, Y | |
| dc.contributor.author | Wells, TJ | |
| dc.contributor.author | Senegaglia, AC | |
| dc.contributor.author | Kuniyoshi, CL | |
| dc.contributor.author | Franck, CL | |
| dc.contributor.author | Dos Santos, AFR | |
| dc.contributor.author | de Noronha, L | |
| dc.contributor.author | Motamen, S | |
| dc.contributor.author | Valadan, R | |
| dc.contributor.author | Amjadi, O | |
| dc.contributor.author | Gogna, R | |
| dc.contributor.author | Madan, E | |
| dc.contributor.author | Alizadeh-Navaei, R | |
| dc.contributor.author | Lamperti, L | |
| dc.contributor.author | Zuñiga, F | |
| dc.contributor.author | Nova-Lamperti, E | |
| dc.contributor.author | Labarca, G | |
| dc.contributor.author | Knippenberg, B | |
| dc.contributor.author | Herwanto, V | |
| dc.contributor.author | Wang, Y | |
| dc.contributor.author | Phu, A | |
| dc.contributor.author | Chew, T | |
| dc.contributor.author | Kwan, T | |
| dc.contributor.author | Kim, K | |
| dc.contributor.author | Teoh, S | |
| dc.contributor.author | Pelaia, TM | |
| dc.contributor.author | Kuan, WS | |
| dc.contributor.author | Jee, Y | |
| dc.contributor.author | Iredell, J | |
| dc.contributor.author | O'Byrne, K | |
| dc.contributor.author | Fraser, JF | |
| dc.contributor.author | Davis, MJ | |
| dc.contributor.author | Belz, GT | |
| dc.contributor.author | Warkiani, ME | |
| dc.contributor.author | Gallo, CS | |
| dc.contributor.author | Souza-Fonseca-Guimaraes, F | |
| dc.contributor.author | Nguyen, Q | |
| dc.contributor.author | Mclean, A | |
| dc.contributor.author | Kulasinghe, A | |
| dc.contributor.author | Short, KR | |
| dc.contributor.author | Tang, B | |
| dc.date.accessioned | 2023-03-12T22:32:40Z | |
| dc.date.available | 2022-12-09 | |
| dc.date.available | 2023-03-12T22:32:40Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Front Immunol, 2022, 13, pp. 1060438 | |
| dc.identifier.issn | 1664-3224 | |
| dc.identifier.issn | 1664-3224 | |
| dc.identifier.uri | http://hdl.handle.net/10453/167074 | |
| dc.description.abstract | PURPOSE: Robust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness. METHODS: We conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients. RESULTS: We show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients. CONCLUSION: These data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus. | |
| dc.format | Electronic-eCollection | |
| dc.language | eng | |
| dc.publisher | Frontiers Media SA | |
| dc.relation.ispartof | Front Immunol | |
| dc.relation.isbasedon | 10.3389/fimmu.2022.1060438 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | 1107 Immunology, 1108 Medical Microbiology | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | COVID-19 | |
| dc.subject.mesh | SARS-CoV-2 | |
| dc.subject.mesh | Influenza A Virus, H1N1 Subtype | |
| dc.subject.mesh | Influenza, Human | |
| dc.subject.mesh | Biomarkers | |
| dc.subject.mesh | Membrane Proteins | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Membrane Proteins | |
| dc.subject.mesh | Influenza, Human | |
| dc.subject.mesh | Influenza A Virus, H1N1 Subtype | |
| dc.subject.mesh | Biomarkers | |
| dc.subject.mesh | COVID-19 | |
| dc.subject.mesh | SARS-CoV-2 | |
| dc.title | IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study. | |
| dc.type | Journal Article | |
| utslib.citation.volume | 13 | |
| utslib.location.activity | Switzerland | |
| utslib.for | 1107 Immunology | |
| utslib.for | 1108 Medical Microbiology | |
| pubs.organisational-group | /University of Technology Sydney | |
| pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
| pubs.organisational-group | /University of Technology Sydney/Strength - CHT - Health Technologies | |
| pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering | |
| pubs.organisational-group | /University of Technology Sydney/Strength - IBMD - Initiative for Biomedical Devices | |
| pubs.organisational-group | /University of Technology Sydney/Centre for Health Technologies (CHT) | |
| utslib.copyright.status | open_access | * |
| dc.date.updated | 2023-03-12T22:31:45Z | |
| pubs.publication-status | Published online | |
| pubs.volume | 13 |
Abstract:
PURPOSE: Robust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness. METHODS: We conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients. RESULTS: We show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients. CONCLUSION: These data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.
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