Identification of a specific biomarker of Acinetobacter baumannii Global Clone 1 by machine learning and PCR related to metabolic PCR related to metabolic fitness of ESKAPE pathogens.

VERONICA ALVAREZ, DANIELA CENTRON

mSystem

American Society for Microbiology

Lugar: Washington; Año: 2023

2379-5077

Since the emergence of high-risk clones worldwide, constant investigations have been undertaken to comprehend the molecular basis that led to their prevalent dissemination in nosocomial settings over time. So far, the complex and multifactorial genetic traits of this type of epidemic clones have only allowed the identification of biomarkers with low specificity. A machine learning algorithm was able to recognize unequivocally a biomarker for early and accurate detection of Acinetobacter baumannii Global Clone 1 (GC1), one of the most disseminated high-risk clones. Support Vector Machine identified the U1 sequence with 367 nucleotides length that matched a fragment of the moaCB gene, which encodes the molybdenum cofactor biosynthesis C and B proteins. U1 differentiates specifically between A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method for early diagnosis based on PCR as shown here. Since the metabolic pathways of Mo enzymes have been recognized as putative therapeutic targets for ESKAPE pathogens, our findings highlighted that machine learning can be also useful in knowledge gaps of high-risk clones and implies noteworthy support to the literature to identify relevant nosocomial biomarkers for other multidrug-resistant high-risk clones.