RESUMO
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.
Assuntos
Biomarcadores , COVID-19 , Humanos , COVID-19/sangue , COVID-19/diagnóstico , Biomarcadores/sangue , Masculino , Feminino , Pessoa de Meia-Idade , Itália , Aprendizado de Máquina , Carnitina/sangue , Carnitina/análogos & derivados , França/epidemiologia , SARS-CoV-2 , Adulto , China , Prognóstico , Espanha , MultiômicaRESUMO
Whole-cell mass spectrometry analysis is a powerful tool to rapidly identify microorganisms. Several studies reported the successful application of this technique to identify a variety of bacterial species with a discriminatory power at the strain level, mainly for bacteria of clinical importance. In this study we used matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to assess the diversity of wheat-associated bacterial isolates. Wheat plants cultivated in non-sterile vermiculite, under greenhouse conditions were used for bacterial isolation. Total cellular extracts of 138 isolates were analyzed by MALDI-TOF MS and the mass spectra were used to cluster the isolates. Taxonomic identification and phylogenetic reconstruction based on 16S rRNA gene sequences showed the presence of Pseudomonas, Pantoea, Acinetobacter, Enterobacter and Curtobacterium. The 16S rRNA gene sequence analyses were congruent with the clusterization from mass spectra profile. Moreover, MALDI-TOF whole cell mass profiling allowed a finer discrimination of the isolates, suggesting that this technique has the potential of differentiating bacterial isolates at the strain level.