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Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate
Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC–MS, LC–MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, l-aspartic, l-glutamic, l-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-l-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-l-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Biomarker, COVID-19, Diagnosis, Machine learning, Prognosis
1828-0447
1439-1458
Cobre, Alexandre de Fátima
52e7a572-9a8d-47ae-a8ae-04f942177e5d
Alves, Alexessander Couto
87b9179e-abde-4ca5-abfc-4b7c5ac8b03b
Gotine, Ana Raquel Manuel
f833f4d9-384f-4633-8389-b1f3195179e6
Domingues, Karime Zeraik Abdalla
aa965d5a-ae6d-42f7-b2a0-11ddebace3c7
Lazo, Raul Edison Luna
cd1fa748-34d2-4a99-be08-6884f6b9bc90
Ferreira, Luana Mota
b30b199b-ddf4-47de-b5b4-66de851e2ce1
Tonin, Fernanda Stumpf
2563480c-6cf6-47c6-b02e-0f0ac422a7f9
Pontarolo, Roberto
f1338bb3-d7f8-44aa-97f1-c3f3b7bc3ce4
Cobre, Alexandre de Fátima
52e7a572-9a8d-47ae-a8ae-04f942177e5d
Alves, Alexessander Couto
87b9179e-abde-4ca5-abfc-4b7c5ac8b03b
Gotine, Ana Raquel Manuel
f833f4d9-384f-4633-8389-b1f3195179e6
Domingues, Karime Zeraik Abdalla
aa965d5a-ae6d-42f7-b2a0-11ddebace3c7
Lazo, Raul Edison Luna
cd1fa748-34d2-4a99-be08-6884f6b9bc90
Ferreira, Luana Mota
b30b199b-ddf4-47de-b5b4-66de851e2ce1
Tonin, Fernanda Stumpf
2563480c-6cf6-47c6-b02e-0f0ac422a7f9
Pontarolo, Roberto
f1338bb3-d7f8-44aa-97f1-c3f3b7bc3ce4

Cobre, Alexandre de Fátima, Alves, Alexessander Couto, Gotine, Ana Raquel Manuel, Domingues, Karime Zeraik Abdalla, Lazo, Raul Edison Luna, Ferreira, Luana Mota, Tonin, Fernanda Stumpf and Pontarolo, Roberto (2024) Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. Internal and Emergency Medicine, 19 (5), 1439-1458. (doi:10.1007/s11739-024-03547-1).

Record type: Article

Abstract

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC–MS, LC–MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, l-aspartic, l-glutamic, l-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-l-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-l-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

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Accepted/In Press date: 16 January 2024
e-pub ahead of print date: 28 February 2024
Keywords: Biomarker, COVID-19, Diagnosis, Machine learning, Prognosis

Identifiers

Local EPrints ID: 494085
URI: http://eprints.soton.ac.uk/id/eprint/494085
ISSN: 1828-0447
PURE UUID: 4d6b1241-4598-430f-924f-37cbed2179b9
ORCID for Alexessander Couto Alves: ORCID iD orcid.org/0000-0001-8519-7356

Catalogue record

Date deposited: 23 Sep 2024 16:42
Last modified: 24 Sep 2024 02:09

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Contributors

Author: Alexandre de Fátima Cobre
Author: Alexessander Couto Alves ORCID iD
Author: Ana Raquel Manuel Gotine
Author: Karime Zeraik Abdalla Domingues
Author: Raul Edison Luna Lazo
Author: Luana Mota Ferreira
Author: Fernanda Stumpf Tonin
Author: Roberto Pontarolo

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