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SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS–PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

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USING ISSR FOR FINGERPRINTING ACCESSIONS OF BIRIBAZEIROABSTRACT-The biribazeiro is a fruit plant native from Amazonian and Atlantic forests, in Brazil. Their fruits have great popular acceptance for fresh consumption. The objective of this study was to measure the genetic divergence of biriba genotypes (Rollinia mucosa [Jacq.]Baill) using ISSR molecular markers. Sixteen genotypes of biriba were screened with 20 ISSR primers, which produced a total of 118 bands, with 96 polymorphic and 22 monomorphic fragments. The genetic dissimilarity values ranged from 0.0909 to 0.5147, based on the complement of the Jaccard index. The UPGMA (Unweighted Pair Group Average Method) grouped the accessions into six groups. The genotypes 1 and 5 were most dissimilar and 11 and 12 the most similar. The ISSR markers used in this study demonstrated the efficiency of molecular polymorphisms detection, revealing high genetic variability among the 16 accessions. So, it can be inferred that there is a considerable genetic variation among accessions of the biribazeiro, showing the importance of molecular markers in the analysis of variability of species poorly studied, as Rollinia mucosa [Jacq.]Baill.

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Purpose: SARS-CoV-2 infection poses a global public health problem. There is a critical need for improvements in the noninvasive prognosis of COVID-19. We hypothesized that matrix-assisted laser desorption ionization mass spectrometry (MALDI-TOF MS) analysis combined with bottom-up proteomic analysis of plasma proteins might identify features to predict high and low risk cases of COVID-19. Patients and Methods: We used MALDI-TOF MS to analyze plasma small proteins and peptides isolated using C18 micro-columns from a cohort containing a total of 117 cases of high (hospitalized) and low risk (outpatients) cases split into training (n = 88) and validation sets (n= 29). The plasma protein/peptide fingerprint obtained was used to train the algorithm before validation using a blinded test cohort. Results: Several sample preparation, MS and data analysis parameters were optimized to achieve an overall accuracy of 85%, sensitivity of 90%, and specificity of 81% in the training set. In the blinded test set, this signature reached an overall accuracy of 93.1%, sensitivity of 87.5%, and specificity of 100%. From this signature, we identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of 1D SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins. Conclusions: We found a plasma proteomic profile that discriminates against patients with high and low risk COVID-19. Proteomic analysis of C18-fractionated plasma may have a role in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.